Project_Data_Script_FV.m

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\f0\fs24 \cf0 \
	Student License -- for use by students to meet course requirements\
	and perform academic research at degree granting institutions only.\
>> plot_arr = [ Bedrooms Bathrooms Stories GarageSpace PostalCode HouseSize LotSize ComplexAge TotalUnits SalePrice]\
\
plot_arr =\
\
   1.0e+06 *\
\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0036    0.0054    0.0000    1.3600    1.3600\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0022    0.0044    0.0000    1.6005    1.6005\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0016    0.0060    0.0001    0.7550    0.7550\
    0.0000    0.0000    0.0000         0    0.0951    0.0022    0.0027    0.0000    1.2500    1.2500\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0014    0.0062    0.0001    0.8700    0.8700\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0020    0.0073    0.0001    1.5700    1.5700\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0018    0.0071    0.0000    1.4070    1.4070\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0010    0.0060    0.0001    0.9500    0.9500\
    0.0000    0.0000    0.0000         0    0.0951    0.0014    0.0014    0.0000    0.8100    0.8100\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0013    0.0060    0.0001    0.9890    0.9890\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0021    0.0061    0.0000    0.9850    0.9850\
    0.0000    0.0000    0.0000         0    0.0951    0.0008    0.0011    0.0000    0.5600    0.5600\
    0.0000    0.0000    0.0000         0    0.0951    0.0011    0.0011    0.0000    0.5550    0.5550\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0017    0.0068    0.0001    1.1500    1.1500\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0018    0.0064    0.0000    1.0150    1.0150\
    0.0000    0.0000    0.0000         0    0.0951    0.0013    0.0009    0.0000    0.5800    0.5800\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0016    0.0016    0.0001    1.0650    1.0650\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0010    0.0048    0.0001    0.6185    0.6185\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0014    0.0014    0.0000    0.5490    0.5490\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0012    0.0062    0.0001    0.8210    0.8210\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0029    0.0103    0.0001    1.6100    1.6100\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0019    0.0050    0.0000    1.2900    1.2900\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0012    0.0078    0.0001    0.9000    0.9000\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0016    0.0089    0.0000    0.8750    0.8750\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0018    0.0135    0.0000    0.8910    0.8910\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0032    0.0107    0.0001    1.6500    1.6500\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0020    0.0183    0.0001    1.4650    1.4650\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0016    0.0062    0.0000    0.9460    0.9460\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0023    0.0081    0.0000    1.2000    1.2000\
    0.0000    0.0000    0.0000         0    0.0951    0.0013    0.0014    0.0000    0.5800    0.5800\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0021    0.0075    0.0000    1.0100    1.0100\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0012    0.0060    0.0000    1.0510    1.0510\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0014    0.0090    0.0000    1.3500    1.3500\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0016    0.0072    0.0001    1.1000    1.1000\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0009    0.0045    0.0001    0.8630    0.8630\
    0.0000    0.0000    0.0000         0    0.0951    0.0022    0.0037    0.0000    0.9250    0.9250\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0013    0.0060    0.0000    0.8400    0.8400\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0021    0.0057    0.0001    0.7990    0.7990\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0015    0.0027    0.0000    0.6350    0.6350\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0011    0.0006    0.0000    0.6105    0.6105\
    0.0000    0.0000    0.0000         0    0.0951    0.0008    0.0081    0.0001    0.7700    0.7700\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0013    0.0033    0.0000    0.6620    0.6620\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0020    0.0121    0.0000    1.0450    1.0450\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0023    0.0060    0.0000    1.0200    1.0200\
    0.0000    0.0000    0.0000         0    0.0951    0.0016    0.0070    0.0001    1.2000    1.2000\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0019    0.0094    0.0001    0.7900    0.7900\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0013    0.0063    0.0000    0.8200    0.8200\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0012    0.0063    0.0000    0.6850    0.6850\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0011    0.0014    0.0000    0.6850    0.6850\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0017    0.0012    0.0000    0.7250    0.7250\
    0.0000    0.0000    0.0000         0    0.0951    0.0010    0.0004    0.0000    0.5030    0.5030\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0010    0.0060    0.0001    1.1800    1.1800\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0014    0.0058    0.0000    0.7300    0.7300\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0012    0.0080    0.0001    0.9050    0.9050\
    0.0000    0.0000    0.0000         0    0.0951    0.0021    0.0057    0.0001    0.9880    0.9880\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0021    0.0035    0.0000    0.9480    0.9480\
    0.0000    0.0000    0.0000         0    0.0951    0.0012    0.0050    0.0001    0.7889    0.7889\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0024    0.0056    0.0001    1.6950    1.6950\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0011    0.0070    0.0001    0.8590    0.8590\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0022    0.0073    0.0001    1.7999    1.7999\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0017    0.0059    0.0001    0.7590    0.7590\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0016    0.0060    0.0001    0.9995    0.9995\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0039    0.0196    0.0000    1.8778    1.8778\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0033    0.0070    0.0000    1.6500    1.6500\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0015    0.0050    0.0000    0.8490    0.8490\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0020    0.0063    0.0001    1.2750    1.2750\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0023    0.0067    0.0001    1.7990    1.7990\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0021    0.0055    0.0000    0.9890    0.9890\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0019    0.0064    0.0001    1.3500    1.3500\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0014    0.0066    0.0000    0.7490    0.7490\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0019    0.0780    0.0001    0.8390    0.8390\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0014    0.0061    0.0001    0.8950    0.8950\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0013    0.0062    0.0001    0.9899    0.9899\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0016    0.0083    0.0000    0.8000    0.8000\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0021    0.0068    0.0001    1.2950    1.2950\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0028    0.0235    0.0000    1.4990    1.4990\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0013    0.0074    0.0000    0.7800    0.7800\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0020    0.0070    0.0001    0.9990    0.9990\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0011    0.0050    0.0000    0.7600    0.7600\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0008    0.0052    0.0001    0.7490    0.7490\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0031    0.0077         0    1.4000    1.4000\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0012    0.0079    0.0001    0.9000    0.9000\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0010    0.0060    0.0000    0.5999    0.5999\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0027    0.0192    0.0000    1.5390    1.5390\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0025    0.0066    0.0001    1.4500    1.4500\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0014    0.0060    0.0000    0.8490    0.8490\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0015    0.0065    0.0000    0.7999    0.7999\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0020    0.0038    0.0000    1.1990    1.1990\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0017    0.0060    0.0000    0.8450    0.8450\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0035    0.0092    0.0000    1.7980    1.7980\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0018    0.0049    0.0000    0.7490    0.7490\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0014    0.0034    0.0000    1.1990    1.1990\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0019    0.0113    0.0001    1.3500    1.3500\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0030    0.0492    0.0000    1.7890    1.7890\
    0.0000    0.0000    0.0000         0    0.0951    0.0013    0.0047    0.0001    0.8980    0.8980\
    0.0000    0.0000    0.0000         0    0.0951    0.0011    0.0076    0.0001    0.6380    0.6380\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0026    0.0084    0.0000    1.5490    1.5490\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0018    0.0067    0.0001    0.9155    0.9155\
    0.0000    0.0000    0.0000         0    0.0951    0.0019    0.0052    0.0001    0.7389    0.7389\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0019    0.0079    0.0001    1.3999    1.3999\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0012    0.0059    0.0001    0.8790    0.8790\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0010    0.0077    0.0001    0.5990    0.5990\
         0         0         0         0         0         0         0         0         0         0\
\
>> plotmatrix(plot_arr)\
\
>> tbl1a = table(Bedrooms_train, SalePrice_train)\
\
tbl1a = \
\
    Bedrooms_train    SalePrice_train\
    ______________    _______________\
\
    5                   1.36e+06     \
    3                 1.6005e+06     \
    4                   7.55e+05     \
    2                   1.25e+06     \
    4                    8.7e+05     \
    3                   1.57e+06     \
    4                  1.407e+06     \
    3                    9.5e+05     \
    2                    8.1e+05     \
    3                   9.89e+05     \
    4                   9.85e+05     \
    1                    5.6e+05     \
    2                   5.55e+05     \
    4                   1.15e+06     \
    4                  1.015e+06     \
    2                    5.8e+05     \
    4                  1.065e+06     \
    3                  6.185e+05     \
    3                   5.49e+05     \
    3                   8.21e+05     \
    5                   1.61e+06     \
    3                   1.29e+06     \
    3                      9e+05     \
    4                   8.75e+05     \
    4                   8.91e+05     \
    3                   1.65e+06     \
    3                  1.465e+06     \
    4                   9.46e+05     \
    5                    1.2e+06     \
    3                    5.8e+05     \
    4                   1.01e+06     \
    3                  1.051e+06     \
    3                   1.35e+06     \
    4                    1.1e+06     \
    2                   8.63e+05     \
    4                   9.25e+05     \
    4                    8.4e+05     \
    3                   7.99e+05     \
    2                   6.35e+05     \
    2                  6.105e+05     \
    2                    7.7e+05     \
    3                   6.62e+05     \
    4                  1.045e+06     \
    5                   1.02e+06     \
    3                    1.2e+06     \
    3                    7.9e+05     \
    4                    8.2e+05     \
    3                   6.85e+05     \
    2                   6.85e+05     \
    3                   7.25e+05     \
    2                   5.03e+05     \
    3                   1.18e+06     \
    4                    7.3e+05     \
    2                   9.05e+05     \
    4                   9.88e+05     \
    4                   9.48e+05     \
    4                 7.8889e+05     \
    5                  1.695e+06     \
    3                   8.59e+05     \
    4                 1.7999e+06     \
    4                   7.59e+05     \
    4                  9.995e+05     \
    5                 1.8778e+06     \
    5                   1.65e+06     \
    4                   8.49e+05     \
    4                  1.275e+06     \
    3                  1.799e+06     \
    4                   9.89e+05     \
    2                   1.35e+06     \
    4                   7.49e+05     \
    5                   8.39e+05     \
    4                   8.95e+05     \
    3                  9.899e+05     \
    3                      8e+05     \
    5                  1.295e+06     \
    4                  1.499e+06     \
    3                    7.8e+05     \
    4                 9.9895e+05     \
    3                    7.6e+05     \
    2                   7.49e+05     \
    5                    1.4e+06     \
    3                      9e+05     \
    3                 5.9995e+05     \
    4                  1.539e+06     \
    5                   1.45e+06     \
    3                   8.49e+05     \
    3                 7.9989e+05     \
    4                  1.199e+06     \
    3                   8.45e+05     \
    4                  1.798e+06     \
    4                   7.49e+05     \
    3                  1.199e+06     \
\
>> mdlx = fitlm(tbl1a)\
\
mdlx = \
\
\
Linear regression model:\
    SalePrice_train ~ 1 + Bedrooms_train\
\
Estimated Coefficients:\
                       Estimate         SE        tStat       pValue  \
                      __________    __________    ______    __________\
\
    (Intercept)        4.508e+05    1.2427e+05    3.6274    0.00047441\
    Bedrooms_train    1.6595e+05         34863    4.7601    7.3513e-06\
\
\
Number of observations: 92, Error degrees of freedom: 90\
Root Mean Squared Error: 3.05e+05\
R-squared: 0.201,  Adjusted R-Squared 0.192\
F-statistic vs. constant model: 22.7, p-value = 7.35e-06\
>> tbl1b = table(Bathrooms_train, SalePrice_train)\
\
tbl1b = \
\
    Bathrooms_train    SalePrice_train\
    _______________    _______________\
\
      4                  1.36e+06     \
      3                1.6005e+06     \
      2                  7.55e+05     \
      3                  1.25e+06     \
      2                   8.7e+05     \
      2                  1.57e+06     \
      2                 1.407e+06     \
      1                   9.5e+05     \
      3                   8.1e+05     \
      2                  9.89e+05     \
      3                  9.85e+05     \
      1                   5.6e+05     \
      2                  5.55e+05     \
      2                  1.15e+06     \
      2                 1.015e+06     \
      3                   5.8e+05     \
      2                 1.065e+06     \
      1                 6.185e+05     \
      3                  5.49e+05     \
      2                  8.21e+05     \
      4                  1.61e+06     \
      2                  1.29e+06     \
      2                     9e+05     \
      2                  8.75e+05     \
      3                  8.91e+05     \
      3                  1.65e+06     \
      3                 1.465e+06     \
      2                  9.46e+05     \
      3                   1.2e+06     \
      2                   5.8e+05     \
      3                  1.01e+06     \
      2                 1.051e+06     \
      2                  1.35e+06     \
      3                   1.1e+06     \
      1                  8.63e+05     \
      3                  9.25e+05     \
      2                   8.4e+05     \
      3                  7.99e+05     \
      2                  6.35e+05     \
      2                 6.105e+05     \
      1                   7.7e+05     \
      3                  6.62e+05     \
      2                 1.045e+06     \
      3                  1.02e+06     \
      2                   1.2e+06     \
      2                   7.9e+05     \
      2                   8.2e+05     \
      2                  6.85e+05     \
      3                  6.85e+05     \
      2                  7.25e+05     \
      2                  5.03e+05     \
      1                  1.18e+06     \
      2                   7.3e+05     \
      1                  9.05e+05     \
      3                  9.88e+05     \
      3                  9.48e+05     \
      2                7.8889e+05     \
      3                 1.695e+06     \
      2                  8.59e+05     \
      3                1.7999e+06     \
      3                  7.59e+05     \
      3                 9.995e+05     \
    3.5                1.8778e+06     \
    3.5                  1.65e+06     \
    2.5                  8.49e+05     \
    2.5                 1.275e+06     \
      2                 1.799e+06     \
      3                  9.89e+05     \
      2                  1.35e+06     \
      2                  7.49e+05     \
      2                  8.39e+05     \
      2                  8.95e+05     \
      2                 9.899e+05     \
      2                     8e+05     \
      3                 1.295e+06     \
      3                 1.499e+06     \
      2                   7.8e+05     \
      2                9.9895e+05     \
      2                   7.6e+05     \
      1                  7.49e+05     \
    3.5                   1.4e+06     \
      2                     9e+05     \
      2                5.9995e+05     \
      3                 1.539e+06     \
    2.5                  1.45e+06     \
      2                  8.49e+05     \
      2                7.9989e+05     \
      3                 1.199e+06     \
      2                  8.45e+05     \
      3                 1.798e+06     \
      3                  7.49e+05     \
      2                 1.199e+06     \
\
>> mdly = fitlm(tbl1b)\
\
mdly = \
\
\
Linear regression model:\
    SalePrice_train ~ 1 + Bathrooms_train\
\
Estimated Coefficients:\
                        Estimate         SE        tStat       pValue  \
                       __________    __________    ______    __________\
\
    (Intercept)        5.1068e+05    1.1533e+05    4.4279    2.6676e-05\
    Bathrooms_train    2.1805e+05         47191    4.6205    1.2713e-05\
\
\
Number of observations: 92, Error degrees of freedom: 90\
Root Mean Squared Error: 3.07e+05\
R-squared: 0.192,  Adjusted R-Squared 0.183\
F-statistic vs. constant model: 21.3, p-value = 1.27e-05\
>> tbl1c = table(HouseSize_train, SalePrice_train)\
\
tbl1c = \
\
    HouseSize_train    SalePrice_train\
    _______________    _______________\
\
    3575                 1.36e+06     \
    2226               1.6005e+06     \
    1603                 7.55e+05     \
    2224                 1.25e+06     \
    1376                  8.7e+05     \
    2041                 1.57e+06     \
    1797                1.407e+06     \
    1014                  9.5e+05     \
    1445                  8.1e+05     \
    1346                 9.89e+05     \
    2124                 9.85e+05     \
     841                  5.6e+05     \
    1067                 5.55e+05     \
    1688                 1.15e+06     \
    1843                1.015e+06     \
    1294                  5.8e+05     \
    1598                1.065e+06     \
    1024                6.185e+05     \
    1360                 5.49e+05     \
    1160                 8.21e+05     \
    2941                 1.61e+06     \
    1875                 1.29e+06     \
    1198                    9e+05     \
    1647                 8.75e+05     \
    1751                 8.91e+05     \
    3163                 1.65e+06     \
    1974                1.465e+06     \
    1624                 9.46e+05     \
    2278                  1.2e+06     \
    1334                  5.8e+05     \
    2124                 1.01e+06     \
    1212                1.051e+06     \
    1368                 1.35e+06     \
    1628                  1.1e+06     \
     944                 8.63e+05     \
    2232                 9.25e+05     \
    1308                  8.4e+05     \
    2112                 7.99e+05     \
    1497                 6.35e+05     \
    1099                6.105e+05     \
     824                  7.7e+05     \
    1301                 6.62e+05     \
    2036                1.045e+06     \
    2287                 1.02e+06     \
    1600                  1.2e+06     \
    1874                  7.9e+05     \
    1342                  8.2e+05     \
    1152                 6.85e+05     \
    1127                 6.85e+05     \
    1675                 7.25e+05     \
     986                 5.03e+05     \
    1036                 1.18e+06     \
    1412                  7.3e+05     \
    1196                 9.05e+05     \
    2088                 9.88e+05     \
    2125                 9.48e+05     \
    1166               7.8889e+05     \
    2450                1.695e+06     \
    1088                 8.59e+05     \
    2160               1.7999e+06     \
    1732                 7.59e+05     \
    1628                9.995e+05     \
    3940               1.8778e+06     \
    3290                 1.65e+06     \
    1521                 8.49e+05     \
    1984                1.275e+06     \
    2280                1.799e+06     \
    2111                 9.89e+05     \
    1850                 1.35e+06     \
    1392                 7.49e+05     \
    1940                 8.39e+05     \
    1399                 8.95e+05     \
    1275                9.899e+05     \
    1599                    8e+05     \
    2063                1.295e+06     \
    2833                1.499e+06     \
    1286                  7.8e+05     \
    1982               9.9895e+05     \
    1078                  7.6e+05     \
     824                 7.49e+05     \
    3094                  1.4e+06     \
    1204                    9e+05     \
    1020               5.9995e+05     \
    2652                1.539e+06     \
    2479                 1.45e+06     \
    1433                 8.49e+05     \
    1462               7.9989e+05     \
    2020                1.199e+06     \
    1723                 8.45e+05     \
    3522                1.798e+06     \
    1760                 7.49e+05     \
    1422                1.199e+06     \
\
>> mdlz = fitlm(tbl1c)\
\
mdlz = \
\
\
Linear regression model:\
    SalePrice_train ~ 1 + HouseSize_train\
\
Estimated Coefficients:\
                        Estimate       SE      tStat       pValue  \
                       __________    ______    ______    __________\
\
    (Intercept)        3.1868e+05     65730    4.8483    5.1776e-06\
    HouseSize_train        403.06    35.305    11.416    3.4435e-19\
\
\
Number of observations: 92, Error degrees of freedom: 90\
Root Mean Squared Error: 2.18e+05\
R-squared: 0.592,  Adjusted R-Squared 0.587\
F-statistic vs. constant model: 130, p-value = 3.44e-19\
 \
>> tb1 = table(Bathrooms_train, Bedrooms_train, SalePrice_train)\
\
tb1 = \
\
    Bathrooms_train    Bedrooms_train    SalePrice_train\
    _______________    ______________    _______________\
\
      4                5                   1.36e+06     \
      3                3                 1.6005e+06     \
      2                4                   7.55e+05     \
      3                2                   1.25e+06     \
      2                4                    8.7e+05     \
      2                3                   1.57e+06     \
      2                4                  1.407e+06     \
      1                3                    9.5e+05     \
      3                2                    8.1e+05     \
      2                3                   9.89e+05     \
      3                4                   9.85e+05     \
      1                1                    5.6e+05     \
      2                2                   5.55e+05     \
      2                4                   1.15e+06     \
      2                4                  1.015e+06     \
      3                2                    5.8e+05     \
      2                4                  1.065e+06     \
      1                3                  6.185e+05     \
      3                3                   5.49e+05     \
      2                3                   8.21e+05     \
      4                5                   1.61e+06     \
      2                3                   1.29e+06     \
      2                3                      9e+05     \
      2                4                   8.75e+05     \
      3                4                   8.91e+05     \
      3                3                   1.65e+06     \
      3                3                  1.465e+06     \
      2                4                   9.46e+05     \
      3                5                    1.2e+06     \
      2                3                    5.8e+05     \
      3                4                   1.01e+06     \
      2                3                  1.051e+06     \
      2                3                   1.35e+06     \
      3                4                    1.1e+06     \
      1                2                   8.63e+05     \
      3                4                   9.25e+05     \
      2                4                    8.4e+05     \
      3                3                   7.99e+05     \
      2                2                   6.35e+05     \
      2                2                  6.105e+05     \
      1                2                    7.7e+05     \
      3                3                   6.62e+05     \
      2                4                  1.045e+06     \
      3                5                   1.02e+06     \
      2                3                    1.2e+06     \
      2                3                    7.9e+05     \
      2                4                    8.2e+05     \
      2                3                   6.85e+05     \
      3                2                   6.85e+05     \
      2                3                   7.25e+05     \
      2                2                   5.03e+05     \
      1                3                   1.18e+06     \
      2                4                    7.3e+05     \
      1                2                   9.05e+05     \
      3                4                   9.88e+05     \
      3                4                   9.48e+05     \
      2                4                 7.8889e+05     \
      3                5                  1.695e+06     \
      2                3                   8.59e+05     \
      3                4                 1.7999e+06     \
      3                4                   7.59e+05     \
      3                4                  9.995e+05     \
    3.5                5                 1.8778e+06     \
    3.5                5                   1.65e+06     \
    2.5                4                   8.49e+05     \
    2.5                4                  1.275e+06     \
      2                3                  1.799e+06     \
      3                4                   9.89e+05     \
      2                2                   1.35e+06     \
      2                4                   7.49e+05     \
      2                5                   8.39e+05     \
      2                4                   8.95e+05     \
      2                3                  9.899e+05     \
      2                3                      8e+05     \
      3                5                  1.295e+06     \
      3                4                  1.499e+06     \
      2                3                    7.8e+05     \
      2                4                 9.9895e+05     \
      2                3                    7.6e+05     \
      1                2                   7.49e+05     \
    3.5                5                    1.4e+06     \
      2                3                      9e+05     \
      2                3                 5.9995e+05     \
      3                4                  1.539e+06     \
    2.5                5                   1.45e+06     \
      2                3                   8.49e+05     \
      2                3                 7.9989e+05     \
      3                4                  1.199e+06     \
      2                3                   8.45e+05     \
      3                4                  1.798e+06     \
      3                4                   7.49e+05     \
      2                3                  1.199e+06     \
\
>> mdl4 = fitlm(tb1)\
\
mdl4 = \
\
\
Linear regression model:\
    SalePrice_train ~ 1 + Bathrooms_train + Bedrooms_train\
\
Estimated Coefficients:\
                        Estimate         SE        tStat      pValue  \
                       __________    __________    ______    _________\
\
    (Intercept)        3.1763e+05    1.3151e+05    2.4152     0.017773\
    Bathrooms_train    1.3766e+05         54083    2.5453      0.01264\
    Bedrooms_train      1.108e+05         40189     2.757    0.0070763\
\
\
Number of observations: 92, Error degrees of freedom: 89\
Root Mean Squared Error: 2.96e+05\
R-squared: 0.255,  Adjusted R-Squared 0.239\
F-statistic vs. constant model: 15.3, p-value = 2.01e-06\
>> tbl2 = table (Bedrooms_train, HouseSize_train, SalePrice_train)\
\
tbl2 = \
\
    Bedrooms_train    HouseSize_train    SalePrice_train\
    ______________    _______________    _______________\
\
    5                 3575                 1.36e+06     \
    3                 2226               1.6005e+06     \
    4                 1603                 7.55e+05     \
    2                 2224                 1.25e+06     \
    4                 1376                  8.7e+05     \
    3                 2041                 1.57e+06     \
    4                 1797                1.407e+06     \
    3                 1014                  9.5e+05     \
    2                 1445                  8.1e+05     \
    3                 1346                 9.89e+05     \
    4                 2124                 9.85e+05     \
    1                  841                  5.6e+05     \
    2                 1067                 5.55e+05     \
    4                 1688                 1.15e+06     \
    4                 1843                1.015e+06     \
    2                 1294                  5.8e+05     \
    4                 1598                1.065e+06     \
    3                 1024                6.185e+05     \
    3                 1360                 5.49e+05     \
    3                 1160                 8.21e+05     \
    5                 2941                 1.61e+06     \
    3                 1875                 1.29e+06     \
    3                 1198                    9e+05     \
    4                 1647                 8.75e+05     \
    4                 1751                 8.91e+05     \
    3                 3163                 1.65e+06     \
    3                 1974                1.465e+06     \
    4                 1624                 9.46e+05     \
    5                 2278                  1.2e+06     \
    3                 1334                  5.8e+05     \
    4                 2124                 1.01e+06     \
    3                 1212                1.051e+06     \
    3                 1368                 1.35e+06     \
    4                 1628                  1.1e+06     \
    2                  944                 8.63e+05     \
    4                 2232                 9.25e+05     \
    4                 1308                  8.4e+05     \
    3                 2112                 7.99e+05     \
    2                 1497                 6.35e+05     \
    2                 1099                6.105e+05     \
    2                  824                  7.7e+05     \
    3                 1301                 6.62e+05     \
    4                 2036                1.045e+06     \
    5                 2287                 1.02e+06     \
    3                 1600                  1.2e+06     \
    3                 1874                  7.9e+05     \
    4                 1342                  8.2e+05     \
    3                 1152                 6.85e+05     \
    2                 1127                 6.85e+05     \
    3                 1675                 7.25e+05     \
    2                  986                 5.03e+05     \
    3                 1036                 1.18e+06     \
    4                 1412                  7.3e+05     \
    2                 1196                 9.05e+05     \
    4                 2088                 9.88e+05     \
    4                 2125                 9.48e+05     \
    4                 1166               7.8889e+05     \
    5                 2450                1.695e+06     \
    3                 1088                 8.59e+05     \
    4                 2160               1.7999e+06     \
    4                 1732                 7.59e+05     \
    4                 1628                9.995e+05     \
    5                 3940               1.8778e+06     \
    5                 3290                 1.65e+06     \
    4                 1521                 8.49e+05     \
    4                 1984                1.275e+06     \
    3                 2280                1.799e+06     \
    4                 2111                 9.89e+05     \
    2                 1850                 1.35e+06     \
    4                 1392                 7.49e+05     \
    5                 1940                 8.39e+05     \
    4                 1399                 8.95e+05     \
    3                 1275                9.899e+05     \
    3                 1599                    8e+05     \
    5                 2063                1.295e+06     \
    4                 2833                1.499e+06     \
    3                 1286                  7.8e+05     \
    4                 1982               9.9895e+05     \
    3                 1078                  7.6e+05     \
    2                  824                 7.49e+05     \
    5                 3094                  1.4e+06     \
    3                 1204                    9e+05     \
    3                 1020               5.9995e+05     \
    4                 2652                1.539e+06     \
    5                 2479                 1.45e+06     \
    3                 1433                 8.49e+05     \
    3                 1462               7.9989e+05     \
    4                 2020                1.199e+06     \
    3                 1723                 8.45e+05     \
    4                 3522                1.798e+06     \
    4                 1760                 7.49e+05     \
    3                 1422                1.199e+06     \
\
>> mdl5 = fitlm(tbl2)\
\
mdl5 = \
\
\
Linear regression model:\
    SalePrice_train ~ 1 + Bedrooms_train + HouseSize_train\
\
Estimated Coefficients:\
                        Estimate       SE       tStat        pValue  \
                       __________    ______    ________    __________\
\
    (Intercept)        3.6188e+05     89623      4.0378    0.00011409\
    Bedrooms_train         -22936     32242    -0.71138       0.47871\
    HouseSize_train        423.57    45.661      9.2763    1.0058e-14\
\
\
Number of observations: 92, Error degrees of freedom: 89\
Root Mean Squared Error: 2.19e+05\
R-squared: 0.594,  Adjusted R-Squared 0.585\
F-statistic vs. constant model: 65.1, p-value = 3.87e-18\
>> tbl3 = table (Bathrooms_train, HouseSize_train, SalePrice_train)\
\
tbl3 = \
\
    Bathrooms_train    HouseSize_train    SalePrice_train\
    _______________    _______________    _______________\
\
      4                3575                 1.36e+06     \
      3                2226               1.6005e+06     \
      2                1603                 7.55e+05     \
      3                2224                 1.25e+06     \
      2                1376                  8.7e+05     \
      2                2041                 1.57e+06     \
      2                1797                1.407e+06     \
      1                1014                  9.5e+05     \
      3                1445                  8.1e+05     \
      2                1346                 9.89e+05     \
      3                2124                 9.85e+05     \
      1                 841                  5.6e+05     \
      2                1067                 5.55e+05     \
      2                1688                 1.15e+06     \
      2                1843                1.015e+06     \
      3                1294                  5.8e+05     \
      2                1598                1.065e+06     \
      1                1024                6.185e+05     \
      3                1360                 5.49e+05     \
      2                1160                 8.21e+05     \
      4                2941                 1.61e+06     \
      2                1875                 1.29e+06     \
      2                1198                    9e+05     \
      2                1647                 8.75e+05     \
      3                1751                 8.91e+05     \
      3                3163                 1.65e+06     \
      3                1974                1.465e+06     \
      2                1624                 9.46e+05     \
      3                2278                  1.2e+06     \
      2                1334                  5.8e+05     \
      3                2124                 1.01e+06     \
      2                1212                1.051e+06     \
      2                1368                 1.35e+06     \
      3                1628                  1.1e+06     \
      1                 944                 8.63e+05     \
      3                2232                 9.25e+05     \
      2                1308                  8.4e+05     \
      3                2112                 7.99e+05     \
      2                1497                 6.35e+05     \
      2                1099                6.105e+05     \
      1                 824                  7.7e+05     \
      3                1301                 6.62e+05     \
      2                2036                1.045e+06     \
      3                2287                 1.02e+06     \
      2                1600                  1.2e+06     \
      2                1874                  7.9e+05     \
      2                1342                  8.2e+05     \
      2                1152                 6.85e+05     \
      3                1127                 6.85e+05     \
      2                1675                 7.25e+05     \
      2                 986                 5.03e+05     \
      1                1036                 1.18e+06     \
      2                1412                  7.3e+05     \
      1                1196                 9.05e+05     \
      3                2088                 9.88e+05     \
      3                2125                 9.48e+05     \
      2                1166               7.8889e+05     \
      3                2450                1.695e+06     \
      2                1088                 8.59e+05     \
      3                2160               1.7999e+06     \
      3                1732                 7.59e+05     \
      3                1628                9.995e+05     \
    3.5                3940               1.8778e+06     \
    3.5                3290                 1.65e+06     \
    2.5                1521                 8.49e+05     \
    2.5                1984                1.275e+06     \
      2                2280                1.799e+06     \
      3                2111                 9.89e+05     \
      2                1850                 1.35e+06     \
      2                1392                 7.49e+05     \
      2                1940                 8.39e+05     \
      2                1399                 8.95e+05     \
      2                1275                9.899e+05     \
      2                1599                    8e+05     \
      3                2063                1.295e+06     \
      3                2833                1.499e+06     \
      2                1286                  7.8e+05     \
      2                1982               9.9895e+05     \
      2                1078                  7.6e+05     \
      1                 824                 7.49e+05     \
    3.5                3094                  1.4e+06     \
      2                1204                    9e+05     \
      2                1020               5.9995e+05     \
      3                2652                1.539e+06     \
    2.5                2479                 1.45e+06     \
      2                1433                 8.49e+05     \
      2                1462               7.9989e+05     \
      3                2020                1.199e+06     \
      2                1723                 8.45e+05     \
      3                3522                1.798e+06     \
      3                1760                 7.49e+05     \
      2                1422                1.199e+06     \
\
>> mdl6 = fitlm(tbl3)\
\
mdl6 = \
\
\
Linear regression model:\
    SalePrice_train ~ 1 + Bathrooms_train + HouseSize_train\
\
Estimated Coefficients:\
                        Estimate       SE       tStat       pValue  \
                       __________    ______    _______    __________\
\
    (Intercept)        4.5935e+05     78788     5.8301    8.7532e-08\
    Bathrooms_train    -1.419e+05     47679    -2.9761     0.0037587\
    HouseSize_train        513.27    50.177     10.229    1.0787e-16\
\
\
Number of observations: 92, Error degrees of freedom: 89\
Root Mean Squared Error: 2.09e+05\
R-squared: 0.628,  Adjusted R-Squared 0.62\
F-statistic vs. constant model: 75.3, p-value = 7.3e-20\
>> tbl3 = table (Bathrooms_train, Bedrooms_train, HouseSize_train, SalePrice_train)\
\
tbl3 = \
\
    Bathrooms_train    Bedrooms_train    HouseSize_train    SalePrice_train\
    _______________    ______________    _______________    _______________\
\
      4                5                 3575                 1.36e+06     \
      3                3                 2226               1.6005e+06     \
      2                4                 1603                 7.55e+05     \
      3                2                 2224                 1.25e+06     \
      2                4                 1376                  8.7e+05     \
      2                3                 2041                 1.57e+06     \
      2                4                 1797                1.407e+06     \
      1                3                 1014                  9.5e+05     \
      3                2                 1445                  8.1e+05     \
      2                3                 1346                 9.89e+05     \
      3                4                 2124                 9.85e+05     \
      1                1                  841                  5.6e+05     \
      2                2                 1067                 5.55e+05     \
      2                4                 1688                 1.15e+06     \
      2                4                 1843                1.015e+06     \
      3                2                 1294                  5.8e+05     \
      2                4                 1598                1.065e+06     \
      1                3                 1024                6.185e+05     \
      3                3                 1360                 5.49e+05     \
      2                3                 1160                 8.21e+05     \
      4                5                 2941                 1.61e+06     \
      2                3                 1875                 1.29e+06     \
      2                3                 1198                    9e+05     \
      2                4                 1647                 8.75e+05     \
      3                4                 1751                 8.91e+05     \
      3                3                 3163                 1.65e+06     \
      3                3                 1974                1.465e+06     \
      2                4                 1624                 9.46e+05     \
      3                5                 2278                  1.2e+06     \
      2                3                 1334                  5.8e+05     \
      3                4                 2124                 1.01e+06     \
      2                3                 1212                1.051e+06     \
      2                3                 1368                 1.35e+06     \
      3                4                 1628                  1.1e+06     \
      1                2                  944                 8.63e+05     \
      3                4                 2232                 9.25e+05     \
      2                4                 1308                  8.4e+05     \
      3                3                 2112                 7.99e+05     \
      2                2                 1497                 6.35e+05     \
      2                2                 1099                6.105e+05     \
      1                2                  824                  7.7e+05     \
      3                3                 1301                 6.62e+05     \
      2                4                 2036                1.045e+06     \
      3                5                 2287                 1.02e+06     \
      2                3                 1600                  1.2e+06     \
      2                3                 1874                  7.9e+05     \
      2                4                 1342                  8.2e+05     \
      2                3                 1152                 6.85e+05     \
      3                2                 1127                 6.85e+05     \
      2                3                 1675                 7.25e+05     \
      2                2                  986                 5.03e+05     \
      1                3                 1036                 1.18e+06     \
      2                4                 1412                  7.3e+05     \
      1                2                 1196                 9.05e+05     \
      3                4                 2088                 9.88e+05     \
      3                4                 2125                 9.48e+05     \
      2                4                 1166               7.8889e+05     \
      3                5                 2450                1.695e+06     \
      2                3                 1088                 8.59e+05     \
      3                4                 2160               1.7999e+06     \
      3                4                 1732                 7.59e+05     \
      3                4                 1628                9.995e+05     \
    3.5                5                 3940               1.8778e+06     \
    3.5                5                 3290                 1.65e+06     \
    2.5                4                 1521                 8.49e+05     \
    2.5                4                 1984                1.275e+06     \
      2                3                 2280                1.799e+06     \
      3                4                 2111                 9.89e+05     \
      2                2                 1850                 1.35e+06     \
      2                4                 1392                 7.49e+05     \
      2                5                 1940                 8.39e+05     \
      2                4                 1399                 8.95e+05     \
      2                3                 1275                9.899e+05     \
      2                3                 1599                    8e+05     \
      3                5                 2063                1.295e+06     \
      3                4                 2833                1.499e+06     \
      2                3                 1286                  7.8e+05     \
      2                4                 1982               9.9895e+05     \
      2                3                 1078                  7.6e+05     \
      1                2                  824                 7.49e+05     \
    3.5                5                 3094                  1.4e+06     \
      2                3                 1204                    9e+05     \
      2                3                 1020               5.9995e+05     \
      3                4                 2652                1.539e+06     \
    2.5                5                 2479                 1.45e+06     \
      2                3                 1433                 8.49e+05     \
      2                3                 1462               7.9989e+05     \
      3                4                 2020                1.199e+06     \
      2                3                 1723                 8.45e+05     \
      3                4                 3522                1.798e+06     \
      3                4                 1760                 7.49e+05     \
      2                3                 1422                1.199e+06     \
\
>> mdl7 = fitlm(tbl3)\
\
mdl7 = \
\
\
Linear regression model:\
    SalePrice_train ~ 1 + Bathrooms_train + Bedrooms_train + HouseSize_train\
\
Estimated Coefficients:\
                        Estimate        SE       tStat        pValue  \
                       ___________    ______    ________    __________\
\
    (Intercept)         4.7659e+05     94873      5.0234    2.6367e-06\
    Bathrooms_train    -1.3967e+05     48394     -2.8861     0.0049053\
    Bedrooms_train          -10329     31297    -0.33002       0.74217\
    HouseSize_train         520.77    55.323      9.4133    5.7811e-15\
\
\
Number of observations: 92, Error degrees of freedom: 88\
Root Mean Squared Error: 2.1e+05\
R-squared: 0.629,  Adjusted R-Squared 0.616\
F-statistic vs. constant model: 49.7, p-value = 6.84e-19\
 \
>> Bathrooms_test = Bathrooms (93:102)\
\
Bathrooms_test =\
\
    2.0000    3.0000    2.0000    2.0000    2.0000    2.0000    2.0000    2.5000    2.0000    1.0000\
\
>> Bedrooms_test = Bedrooms (93:102)\
\
Bedrooms_test =\
\
     3     5     2     3     4     4     6     4     3     2\
\
>> HouseSize_test = HouseSize (93:102)\
\
HouseSize_test =\
\
  Columns 1 through 8\
\
        1874        3015        1273        1120        2585        1847        1884        1897\
\
  Columns 9 through 10\
\
        1178         959\
\
>> SalePrice_test = SalePrice (93:102)\
\
SalePrice_test =\
\
  Columns 1 through 8\
\
     1350000     1788999      898000      638000     1549000      915500      738888     1399888\
\
  Columns 9 through 10\
\
      879000      599000\
\
>> i  = [1 : 10]\
\
i =\
\
     1     2     3     4     5     6     7     8     9    10\
\
>> price_mdl1(i) = (4.508e+05) + (1.6595e+05) * Bedrooms(i+92)\
\
price_mdl1 =\
\
  Columns 1 through 8\
\
      948650     1280550      782700      948650     1114600     1114600     1446500     1114600\
\
  Columns 9 through 10\
\
      948650      782700\
\
>> price_mdl2(i) = (5.1068e+05) + (2.1805e+05) * Bathrooms(i+92)\
\
price_mdl2 =\
\
  Columns 1 through 8\
\
      946780     1164830      946780      946780      946780      946780      946780     1055805\
\
  Columns 9 through 10\
\
      946780      728730\
\
>> price_mdl3(i) = (3.1868e+05) + (403.06) * HouseSize(i+92)\
\
price_mdl3 =\
\
   1.0e+06 *\
\
    1.0740    1.5339    0.8318    0.7701    1.3606    1.0631    1.0780    1.0833    0.7935    0.7052\
\
>> price_mdl4(i) = (3.1763e+05) + (1.3766e+05) * Bathrooms(i+92) + (1.108e+05) * Bedrooms(i+92)\
\
price_mdl4 =\
\
  Columns 1 through 8\
\
      925350     1284610      814550      925350     1036150     1036150     1257750     1104980\
\
  Columns 9 through 10\
\
      925350      676890\
\
>> price_mdl5(i) = (3.6188e+05) + (423.57 ) * HouseSize(i+92) + (-22936) * Bedrooms(i+92)\
\
price_mdl5 =\
\
   1.0e+06 *\
\
    1.0868    1.5243    0.8552    0.7675    1.3651    1.0525    1.0223    1.0736    0.7920    0.7222\
\
>> price_mdl6(i) = (4.5935e+05) + (513.27 ) * HouseSize(i+92) + (-1.419e+05) * Bathrooms(i+92)\
\
price_mdl6 =\
\
   1.0e+06 *\
\
    1.1374    1.5812    0.8289    0.7504    1.5024    1.1236    1.1426    1.0783    0.7802    0.8097\
\
>> price_mdl7(i) = (4.7659e+05) + (520.77) * HouseSize(i+92) + (-1.3967e+05) * Bathrooms(i+92) + (-10329) * Bedrooms(i+92)\
\
price_mdl7 =\
\
   1.0e+06 *\
\
    1.1422    1.5761    0.8395    0.7495    1.5021    1.1178    1.1164    1.0740    0.7797    0.8157\
\
>> MSE_mdl1 = (immse (SalePrice(93:102) , price_mdl1) )*10\
\
MSE_mdl1 =\
\
   1.3784e+12\
\
>> MSE_mdl2 = (immse (SalePrice(93:102) , price_mdl2) )*10\
\
MSE_mdl2 =\
\
   1.1966e+12\
\
>> MSE_mdl3 = (immse (SalePrice(93:102) , price_mdl3) )*10\
\
MSE_mdl3 =\
\
   4.5423e+11\
\
>> MSE_mdl4 = (immse (SalePrice(93:102) , price_mdl4) )*10\
\
MSE_mdl4 =\
\
   1.1662e+12\
\
>> MSE_mdl5 = (immse (SalePrice(93:102) , price_mdl5) )*10\
\
MSE_mdl5 =\
\
   4.2000e+11\
\
>> MSE_mdl6 = (immse (SalePrice(93:102) , price_mdl6) )*10\
\
MSE_mdl6 =\
\
   4.7179e+11\
\
>> MSE_mdl7 = (immse (SalePrice(93:102) , price_mdl7) )*10\
\
MSE_mdl7 =\
\
   4.5304e+11\
>>Age_HSize\
\
Age_Hsize =\
\
        3575\
        2226\
        1603\
        2224\
        1376\
        2041\
        1797\
        1014\
        1445\
        1346\
        2124\
         841\
        1067\
        1688\
        1843\
        1294\
        1598\
        1024\
        1360\
        1160\
        2941\
        1875\
        1198\
        1647\
        1751\
        3163\
        1974\
        1624\
        2278\
        1334\
        2124\
        1212\
        1368\
        1628\
         944\
        2232\
        1308\
        2112\
        1497\
        1099\
         824\
        1301\
        2036\
        2287\
        1600\
        1874\
        1342\
        1152\
        1127\
        1675\
         986\
        1036\
        1412\
        1196\
        2088\
        2125\
        1166\
        2450\
        1088\
        2160\
        1732\
        1628\
        3940\
        3290\
        1521\
        1984\
        2280\
        2111\
        1850\
        1392\
        1940\
        1399\
        1275\
        1599\
        2063\
        2833\
        1286\
        1982\
        1078\
         824\
        3094\
        1204\
        1020\
        2652\
        2479\
        1433\
        1462\
        2020\
        1723\
        3522\
        1760\
        1422\
        1874\
        3015\
        1273\
        1120\
        2585\
        1847\
        1884\
        1897\
        1178\
         959\
           0\
\
>> Age_Hsize_test = Age_Hsize(93:103)\
\
Age_Hsize_test =\
\
      101196\
       63315\
      140030\
      114240\
       85305\
       96044\
      105504\
      146069\
       71858\
       68089\
           0\
\
>> Age_Hsize_train = Age_Hsize(1:92)\
\
Age_Hsize_train =\
\
       60775\
      104622\
       91371\
       40032\
       82560\
      124501\
       71880\
       69966\
       52020\
       68646\
       95580\
       31117\
       36278\
       97904\
       82935\
       41408\
       95880\
       66560\
       51680\
       60320\
      208811\
       39375\
       68286\
       67527\
       68289\
      183454\
      112518\
       77952\
      111622\
       60030\
       84960\
       58176\
       65664\
       89540\
       74576\
       24552\
       61476\
      122496\
       61377\
       50554\
       61800\
       48137\
       85512\
      102915\
      144000\
      121810\
       63074\
       47232\
       10143\
       18425\
       27608\
       59052\
       64952\
       83720\
      210888\
       44625\
      113102\
      132300\
      100096\
      108000\
       95260\
       87912\
      126080\
       65800\
       54756\
      107136\
      175560\
       69663\
      142450\
       64032\
      102820\
       74147\
       71400\
       76752\
      117591\
       59493\
       57870\
      184326\
       35574\
       56856\
        3094\
       67424\
       38760\
       76908\
      123950\
       44423\
       65790\
       46460\
       77535\
      105660\
       47520\
       55458\
\
>> plot_arr = [ Bedrooms Bathrooms Stories GarageSpace PostalCode HouseSize LotSize ComplexAge TotalUnits Age_Hsize SalePrice ]\
\
plot_arr =\
\
   1.0e+06 *\
\
  Columns 1 through 10\
\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0036    0.0054    0.0000    1.3600    0.0608\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0022    0.0044    0.0000    1.6005    0.1046\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0016    0.0060    0.0001    0.7550    0.0914\
    0.0000    0.0000    0.0000         0    0.0951    0.0022    0.0027    0.0000    1.2500    0.0400\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0014    0.0062    0.0001    0.8700    0.0826\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0020    0.0073    0.0001    1.5700    0.1245\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0018    0.0071    0.0000    1.4070    0.0719\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0010    0.0060    0.0001    0.9500    0.0700\
    0.0000    0.0000    0.0000         0    0.0951    0.0014    0.0014    0.0000    0.8100    0.0520\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0013    0.0060    0.0001    0.9890    0.0686\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0021    0.0061    0.0000    0.9850    0.0956\
    0.0000    0.0000    0.0000         0    0.0951    0.0008    0.0011    0.0000    0.5600    0.0311\
    0.0000    0.0000    0.0000         0    0.0951    0.0011    0.0011    0.0000    0.5550    0.0363\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0017    0.0068    0.0001    1.1500    0.0979\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0018    0.0064    0.0000    1.0150    0.0829\
    0.0000    0.0000    0.0000         0    0.0951    0.0013    0.0009    0.0000    0.5800    0.0414\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0016    0.0016    0.0001    1.0650    0.0959\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0010    0.0048    0.0001    0.6185    0.0666\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0014    0.0014    0.0000    0.5490    0.0517\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0012    0.0062    0.0001    0.8210    0.0603\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0029    0.0103    0.0001    1.6100    0.2088\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0019    0.0050    0.0000    1.2900    0.0394\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0012    0.0078    0.0001    0.9000    0.0683\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0016    0.0089    0.0000    0.8750    0.0675\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0018    0.0135    0.0000    0.8910    0.0683\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0032    0.0107    0.0001    1.6500    0.1835\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0020    0.0183    0.0001    1.4650    0.1125\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0016    0.0062    0.0000    0.9460    0.0780\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0023    0.0081    0.0000    1.2000    0.1116\
    0.0000    0.0000    0.0000         0    0.0951    0.0013    0.0014    0.0000    0.5800    0.0600\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0021    0.0075    0.0000    1.0100    0.0850\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0012    0.0060    0.0000    1.0510    0.0582\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0014    0.0090    0.0000    1.3500    0.0657\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0016    0.0072    0.0001    1.1000    0.0895\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0009    0.0045    0.0001    0.8630    0.0746\
    0.0000    0.0000    0.0000         0    0.0951    0.0022    0.0037    0.0000    0.9250    0.0246\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0013    0.0060    0.0000    0.8400    0.0615\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0021    0.0057    0.0001    0.7990    0.1225\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0015    0.0027    0.0000    0.6350    0.0614\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0011    0.0006    0.0000    0.6105    0.0506\
    0.0000    0.0000    0.0000         0    0.0951    0.0008    0.0081    0.0001    0.7700    0.0618\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0013    0.0033    0.0000    0.6620    0.0481\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0020    0.0121    0.0000    1.0450    0.0855\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0023    0.0060    0.0000    1.0200    0.1029\
    0.0000    0.0000    0.0000         0    0.0951    0.0016    0.0070    0.0001    1.2000    0.1440\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0019    0.0094    0.0001    0.7900    0.1218\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0013    0.0063    0.0000    0.8200    0.0631\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0012    0.0063    0.0000    0.6850    0.0472\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0011    0.0014    0.0000    0.6850    0.0101\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0017    0.0012    0.0000    0.7250    0.0184\
    0.0000    0.0000    0.0000         0    0.0951    0.0010    0.0004    0.0000    0.5030    0.0276\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0010    0.0060    0.0001    1.1800    0.0591\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0014    0.0058    0.0000    0.7300    0.0650\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0012    0.0080    0.0001    0.9050    0.0837\
    0.0000    0.0000    0.0000         0    0.0951    0.0021    0.0057    0.0001    0.9880    0.2109\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0021    0.0035    0.0000    0.9480    0.0446\
    0.0000    0.0000    0.0000         0    0.0951    0.0012    0.0050    0.0001    0.7889    0.1131\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0024    0.0056    0.0001    1.6950    0.1323\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0011    0.0070    0.0001    0.8590    0.1001\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0022    0.0073    0.0001    1.7999    0.1080\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0017    0.0059    0.0001    0.7590    0.0953\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0016    0.0060    0.0001    0.9995    0.0879\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0039    0.0196    0.0000    1.8778    0.1261\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0033    0.0070    0.0000    1.6500    0.0658\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0015    0.0050    0.0000    0.8490    0.0548\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0020    0.0063    0.0001    1.2750    0.1071\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0023    0.0067    0.0001    1.7990    0.1756\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0021    0.0055    0.0000    0.9890    0.0697\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0019    0.0064    0.0001    1.3500    0.1424\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0014    0.0066    0.0000    0.7490    0.0640\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0019    0.0780    0.0001    0.8390    0.1028\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0014    0.0061    0.0001    0.8950    0.0741\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0013    0.0062    0.0001    0.9899    0.0714\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0016    0.0083    0.0000    0.8000    0.0768\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0021    0.0068    0.0001    1.2950    0.1176\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0028    0.0235    0.0000    1.4990    0.0595\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0013    0.0074    0.0000    0.7800    0.0579\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0020    0.0070    0.0001    0.9990    0.1843\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0011    0.0050    0.0000    0.7600    0.0356\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0008    0.0052    0.0001    0.7490    0.0569\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0031    0.0077         0    1.4000    0.0031\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0012    0.0079    0.0001    0.9000    0.0674\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0010    0.0060    0.0000    0.5999    0.0388\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0027    0.0192    0.0000    1.5390    0.0769\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0025    0.0066    0.0001    1.4500    0.1240\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0014    0.0060    0.0000    0.8490    0.0444\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0015    0.0065    0.0000    0.7999    0.0658\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0020    0.0038    0.0000    1.1990    0.0465\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0017    0.0060    0.0000    0.8450    0.0775\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0035    0.0092    0.0000    1.7980    0.1057\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0018    0.0049    0.0000    0.7490    0.0475\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0014    0.0034    0.0000    1.1990    0.0555\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0019    0.0113    0.0001    1.3500    0.1012\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0030    0.0492    0.0000    1.7890    0.0633\
    0.0000    0.0000    0.0000         0    0.0951    0.0013    0.0047    0.0001    0.8980    0.1400\
    0.0000    0.0000    0.0000         0    0.0951    0.0011    0.0076    0.0001    0.6380    0.1142\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0026    0.0084    0.0000    1.5490    0.0853\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0018    0.0067    0.0001    0.9155    0.0960\
    0.0000    0.0000    0.0000         0    0.0951    0.0019    0.0052    0.0001    0.7389    0.1055\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0019    0.0079    0.0001    1.3999    0.1461\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0012    0.0059    0.0001    0.8790    0.0719\
    0.0000    0.0000    0.0000    0.0000    0.0951    0.0010    0.0077    0.0001    0.5990    0.0681\
         0         0         0         0         0         0         0         0         0         0\
\
  Column 11\
\
    1.3600\
    1.6005\
    0.7550\
    1.2500\
    0.8700\
    1.5700\
    1.4070\
    0.9500\
    0.8100\
    0.9890\
    0.9850\
    0.5600\
    0.5550\
    1.1500\
    1.0150\
    0.5800\
    1.0650\
    0.6185\
    0.5490\
    0.8210\
    1.6100\
    1.2900\
    0.9000\
    0.8750\
    0.8910\
    1.6500\
    1.4650\
    0.9460\
    1.2000\
    0.5800\
    1.0100\
    1.0510\
    1.3500\
    1.1000\
    0.8630\
    0.9250\
    0.8400\
    0.7990\
    0.6350\
    0.6105\
    0.7700\
    0.6620\
    1.0450\
    1.0200\
    1.2000\
    0.7900\
    0.8200\
    0.6850\
    0.6850\
    0.7250\
    0.5030\
    1.1800\
    0.7300\
    0.9050\
    0.9880\
    0.9480\
    0.7889\
    1.6950\
    0.8590\
    1.7999\
    0.7590\
    0.9995\
    1.8778\
    1.6500\
    0.8490\
    1.2750\
    1.7990\
    0.9890\
    1.3500\
    0.7490\
    0.8390\
    0.8950\
    0.9899\
    0.8000\
    1.2950\
    1.4990\
    0.7800\
    0.9990\
    0.7600\
    0.7490\
    1.4000\
    0.9000\
    0.5999\
    1.5390\
    1.4500\
    0.8490\
    0.7999\
    1.1990\
    0.8450\
    1.7980\
    0.7490\
    1.1990\
    1.3500\
    1.7890\
    0.8980\
    0.6380\
    1.5490\
    0.9155\
    0.7389\
    1.3999\
    0.8790\
    0.5990\
         0\
\
>> plotmatrix(plot_arr)\
>> tbl8 = table(Age_Hsize_train, SalePrice_train)\
\
tbl8 = \
\
    Age_Hsize_train    SalePrice_train\
    _______________    _______________\
\
         60775           1.36e+06     \
    1.0462e+05         1.6005e+06     \
         91371           7.55e+05     \
         40032           1.25e+06     \
         82560            8.7e+05     \
     1.245e+05           1.57e+06     \
         71880          1.407e+06     \
         69966            9.5e+05     \
         52020            8.1e+05     \
         68646           9.89e+05     \
         95580           9.85e+05     \
         31117            5.6e+05     \
         36278           5.55e+05     \
         97904           1.15e+06     \
         82935          1.015e+06     \
         41408            5.8e+05     \
         95880          1.065e+06     \
         66560          6.185e+05     \
         51680           5.49e+05     \
         60320           8.21e+05     \
    2.0881e+05           1.61e+06     \
         39375           1.29e+06     \
         68286              9e+05     \
         67527           8.75e+05     \
         68289           8.91e+05     \
    1.8345e+05           1.65e+06     \
    1.1252e+05          1.465e+06     \
         77952           9.46e+05     \
    1.1162e+05            1.2e+06     \
         60030            5.8e+05     \
         84960           1.01e+06     \
         58176          1.051e+06     \
         65664           1.35e+06     \
         89540            1.1e+06     \
         74576           8.63e+05     \
         24552           9.25e+05     \
         61476            8.4e+05     \
     1.225e+05           7.99e+05     \
         61377           6.35e+05     \
         50554          6.105e+05     \
         61800            7.7e+05     \
         48137           6.62e+05     \
         85512          1.045e+06     \
    1.0292e+05           1.02e+06     \
      1.44e+05            1.2e+06     \
    1.2181e+05            7.9e+05     \
         63074            8.2e+05     \
         47232           6.85e+05     \
         10143           6.85e+05     \
         18425           7.25e+05     \
         27608           5.03e+05     \
         59052           1.18e+06     \
         64952            7.3e+05     \
         83720           9.05e+05     \
    2.1089e+05           9.88e+05     \
         44625           9.48e+05     \
     1.131e+05         7.8889e+05     \
     1.323e+05          1.695e+06     \
     1.001e+05           8.59e+05     \
      1.08e+05         1.7999e+06     \
         95260           7.59e+05     \
         87912          9.995e+05     \
    1.2608e+05         1.8778e+06     \
         65800           1.65e+06     \
         54756           8.49e+05     \
    1.0714e+05          1.275e+06     \
    1.7556e+05          1.799e+06     \
         69663           9.89e+05     \
    1.4245e+05           1.35e+06     \
         64032           7.49e+05     \
    1.0282e+05           8.39e+05     \
         74147           8.95e+05     \
         71400          9.899e+05     \
         76752              8e+05     \
    1.1759e+05          1.295e+06     \
         59493          1.499e+06     \
         57870            7.8e+05     \
    1.8433e+05         9.9895e+05     \
         35574            7.6e+05     \
         56856           7.49e+05     \
          3094            1.4e+06     \
         67424              9e+05     \
         38760         5.9995e+05     \
         76908          1.539e+06     \
    1.2395e+05           1.45e+06     \
         44423           8.49e+05     \
         65790         7.9989e+05     \
         46460          1.199e+06     \
         77535           8.45e+05     \
    1.0566e+05          1.798e+06     \
         47520           7.49e+05     \
         55458          1.199e+06     \
\
>> mdl8 = fitlm(tbl8)\
\
mdl8 = \
\
\
Linear regression model:\
    SalePrice_train ~ 1 + Age_Hsize_train\
\
Estimated Coefficients:\
                        Estimate       SE       tStat       pValue  \
                       __________    _______    ______    __________\
\
    (Intercept)        6.8077e+05      68901    9.8803     5.064e-16\
    Age_Hsize_train        4.2829    0.77248    5.5443    2.9208e-07\
\
\
Number of observations: 92, Error degrees of freedom: 90\
Root Mean Squared Error: 2.95e+05\
R-squared: 0.255,  Adjusted R-Squared 0.246\
F-statistic vs. constant model: 30.7, p-value = 2.92e-07\
>> tbl9 = table(Age_Hsize_train, HouseSize_train, Bedrooms_train,Bathrooms_train, SalePrice_train)\
\
tbl9 = \
\
    Age_Hsize_train    HouseSize_train    Bedrooms_train    Bathrooms_train    SalePrice_train\
    _______________    _______________    ______________    _______________    _______________\
\
         60775         3575               5                   4                  1.36e+06     \
    1.0462e+05         2226               3                   3                1.6005e+06     \
         91371         1603               4                   2                  7.55e+05     \
         40032         2224               2                   3                  1.25e+06     \
         82560         1376               4                   2                   8.7e+05     \
     1.245e+05         2041               3                   2                  1.57e+06     \
         71880         1797               4                   2                 1.407e+06     \
         69966         1014               3                   1                   9.5e+05     \
         52020         1445               2                   3                   8.1e+05     \
         68646         1346               3                   2                  9.89e+05     \
         95580         2124               4                   3                  9.85e+05     \
         31117          841               1                   1                   5.6e+05     \
         36278         1067               2                   2                  5.55e+05     \
         97904         1688               4                   2                  1.15e+06     \
         82935         1843               4                   2                 1.015e+06     \
         41408         1294               2                   3                   5.8e+05     \
         95880         1598               4                   2                 1.065e+06     \
         66560         1024               3                   1                 6.185e+05     \
         51680         1360               3                   3                  5.49e+05     \
         60320         1160               3                   2                  8.21e+05     \
    2.0881e+05         2941               5                   4                  1.61e+06     \
         39375         1875               3                   2                  1.29e+06     \
         68286         1198               3                   2                     9e+05     \
         67527         1647               4                   2                  8.75e+05     \
         68289         1751               4                   3                  8.91e+05     \
    1.8345e+05         3163               3                   3                  1.65e+06     \
    1.1252e+05         1974               3                   3                 1.465e+06     \
         77952         1624               4                   2                  9.46e+05     \
    1.1162e+05         2278               5                   3                   1.2e+06     \
         60030         1334               3                   2                   5.8e+05     \
         84960         2124               4                   3                  1.01e+06     \
         58176         1212               3                   2                 1.051e+06     \
         65664         1368               3                   2                  1.35e+06     \
         89540         1628               4                   3                   1.1e+06     \
         74576          944               2                   1                  8.63e+05     \
         24552         2232               4                   3                  9.25e+05     \
         61476         1308               4                   2                   8.4e+05     \
     1.225e+05         2112               3                   3                  7.99e+05     \
         61377         1497               2                   2                  6.35e+05     \
         50554         1099               2                   2                 6.105e+05     \
         61800          824               2                   1                   7.7e+05     \
         48137         1301               3                   3                  6.62e+05     \
         85512         2036               4                   2                 1.045e+06     \
    1.0292e+05         2287               5                   3                  1.02e+06     \
      1.44e+05         1600               3                   2                   1.2e+06     \
    1.2181e+05         1874               3                   2                   7.9e+05     \
         63074         1342               4                   2                   8.2e+05     \
         47232         1152               3                   2                  6.85e+05     \
         10143         1127               2                   3                  6.85e+05     \
         18425         1675               3                   2                  7.25e+05     \
         27608          986               2                   2                  5.03e+05     \
         59052         1036               3                   1                  1.18e+06     \
         64952         1412               4                   2                   7.3e+05     \
         83720         1196               2                   1                  9.05e+05     \
    2.1089e+05         2088               4                   3                  9.88e+05     \
         44625         2125               4                   3                  9.48e+05     \
     1.131e+05         1166               4                   2                7.8889e+05     \
     1.323e+05         2450               5                   3                 1.695e+06     \
     1.001e+05         1088               3                   2                  8.59e+05     \
      1.08e+05         2160               4                   3                1.7999e+06     \
         95260         1732               4                   3                  7.59e+05     \
         87912         1628               4                   3                 9.995e+05     \
    1.2608e+05         3940               5                 3.5                1.8778e+06     \
         65800         3290               5                 3.5                  1.65e+06     \
         54756         1521               4                 2.5                  8.49e+05     \
    1.0714e+05         1984               4                 2.5                 1.275e+06     \
    1.7556e+05         2280               3                   2                 1.799e+06     \
         69663         2111               4                   3                  9.89e+05     \
    1.4245e+05         1850               2                   2                  1.35e+06     \
         64032         1392               4                   2                  7.49e+05     \
    1.0282e+05         1940               5                   2                  8.39e+05     \
         74147         1399               4                   2                  8.95e+05     \
         71400         1275               3                   2                 9.899e+05     \
         76752         1599               3                   2                     8e+05     \
    1.1759e+05         2063               5                   3                 1.295e+06     \
         59493         2833               4                   3                 1.499e+06     \
         57870         1286               3                   2                   7.8e+05     \
    1.8433e+05         1982               4                   2                9.9895e+05     \
         35574         1078               3                   2                   7.6e+05     \
         56856          824               2                   1                  7.49e+05     \
          3094         3094               5                 3.5                   1.4e+06     \
         67424         1204               3                   2                     9e+05     \
         38760         1020               3                   2                5.9995e+05     \
         76908         2652               4                   3                 1.539e+06     \
    1.2395e+05         2479               5                 2.5                  1.45e+06     \
         44423         1433               3                   2                  8.49e+05     \
         65790         1462               3                   2                7.9989e+05     \
         46460         2020               4                   3                 1.199e+06     \
         77535         1723               3                   2                  8.45e+05     \
    1.0566e+05         3522               4                   3                 1.798e+06     \
         47520         1760               4                   3                  7.49e+05     \
         55458         1422               3                   2                 1.199e+06     \
\
>> mdl9 = fitlm(tbl9)\
\
mdl9 = \
\
\
Linear regression model:\
    SalePrice_train ~ 1 + Age_Hsize_train + HouseSize_train + Bedrooms_train + Bathrooms_train\
\
Estimated Coefficients:\
                        Estimate        SE        tStat        pValue  \
                       ___________    _______    ________    __________\
\
    (Intercept)           4.09e+05      94306       4.337    3.8729e-05\
    Age_Hsize_train         1.6986    0.59702      2.8452      0.005533\
    HouseSize_train         465.73     56.626      8.2247     1.727e-12\
    Bedrooms_train          -21179      30348    -0.69789       0.48711\
    Bathrooms_train    -1.1176e+05      47575     -2.3492      0.021081\
\
\
Number of observations: 92, Error degrees of freedom: 87\
Root Mean Squared Error: 2.02e+05\
R-squared: 0.661,  Adjusted R-Squared 0.645\
F-statistic vs. constant model: 42.3, p-value = 1.15e-19\
>> price_mdl8(i) = (6.8077e+05) + (4.2829) * Age_Hsize_test(i)\
\
price_mdl8 =\
\
   1.0e+06 *\
\
    1.1142    0.9519    1.2805    1.1700    1.0461    1.0921    1.1326    1.3064    0.9885    0.9724\
\
>> price_mdl9(i) = (4.09e+05 ) + (465.73) * HouseSize(i + 92) + (-1.1176e+05) * Bathrooms(i + 92) + (-21179) * Bedrooms(i + 92) + (1.6986) * Age_Hsize_test(i)\
\
price_mdl9 =\
\
   1.0e+06 *\
\
    1.1666    1.4795    0.9739    0.8376    1.4496    1.1241    1.1151    1.1765    0.7926    0.8172\
\
>> MSE_mdl8 = (immse (SalePrice(93:102) , price_mdl8) )*10\
\
MSE_mdl8 =\
\
   1.7849e+12\
\
>> MSE_mdl9 = (immse (SalePrice(93:102) , price_mdl9) )*10\
\
MSE_mdl9 =\
\
   4.7486e+11\
\
}