UofT-DSI · RohanAlexander · Dec 17, 2025 · Dec 17, 2025 · Dec 17, 2025 · Dec 17, 2025
diff --git a/01_materials/slides/00_introduction_slides.pdf b/01_materials/slides/00_introduction_slides.pdf
diff --git a/01_materials/slides/01_probability_intro_slides.pdf b/01_materials/slides/01_probability_intro_slides.pdf
diff --git a/01_materials/slides/02_populations_and_samples_slides.pdf b/01_materials/slides/02_populations_and_samples_slides.pdf
diff --git a/01_materials/slides/03_simple_probability_samples_slides.pdf b/01_materials/slides/03_simple_probability_samples_slides.pdf
diff --git a/01_materials/slides/04_stratified_sampling_slides.pdf b/01_materials/slides/04_stratified_sampling_slides.pdf
diff --git a/01_materials/slides/05_cluster_sampling_slides.pdf b/01_materials/slides/05_cluster_sampling_slides.pdf
diff --git a/01_materials/slides/06_errors_slides.pdf b/01_materials/slides/06_errors_slides.pdf
diff --git a/01_materials/slides/07_measures_of_quality_and_questionnaire_design.pdf b/01_materials/slides/07_measures_of_quality_and_questionnaire_design.pdf
diff --git a/01_materials/slides/08_ethics_slides.pdf b/01_materials/slides/08_ethics_slides.pdf
diff --git a/01_materials/slides/09_privacy_slides.pdf b/01_materials/slides/09_privacy_slides.pdf
diff --git a/03_instructional_team/markdown_slides/03_simple_probability_samples_slides.md b/03_instructional_team/markdown_slides/03_simple_probability_samples_slides.md
@@ -38,7 +38,7 @@ $ echo "Data Sciences Institute"
 - Consider a population of size *N*
 - Simple random sampling **with replacement**:
   1. Select one unit for measurement, with probability 1/ *N*
-  2. Sampled unit is return to the population
+  2. Sampled unit is returned to the population
   3. Select second unit for measurement, with probability 1/ *N*
   4. Repeat until desired sample size is obtained
 

diff --git a/03_instructional_team/markdown_slides/04_stratified_sampling_slides.md b/03_instructional_team/markdown_slides/04_stratified_sampling_slides.md
@@ -79,7 +79,7 @@ $ echo "Data Sciences Institute"
 
 - The variance of the sample mean can be computed,
 
-  > $$ \hat{V}(\bar{y}) = \sum_{h=1}^{H}\frac{s_h^2}{n_h}(1-\frac{n_h}{N_h})(\frac{N^h}{N})^2 $$
+  > $$ \hat{V}(\bar{y}) = \sum_{h=1}^{H}\frac{s_h^2}{n_h}(1-\frac{n_h}{N_h})(\frac{N_h}{N})^2 $$
 
 (how much our mean will vary across samples)
 
@@ -114,7 +114,7 @@ $ echo "Data Sciences Institute"
 
 - The population mean can be estimated directly using a weighted mean of recorded observations:
 
-  >  $$ \bar{y} = \frac{\sum_{h=1}^{H} \sum_{i=1}^{h} w_{hi}y_{hi}}{\sum_{h=1}^{H} \sum_{i=1}^{h} w_{hi}} $$
+  >  $$ \bar{y} = \frac{\sum_{h=1}^{H} \sum_{i=1}^{n_h} w_{hi}y_{hi}}{\sum_{h=1}^{H} \sum_{i=1}^{n_h} w_{hi}} $$
 
 - In stratified sampling, we need to sum over the weights and units in each stratum, and then sum over all strata.
 

diff --git a/03_instructional_team/markdown_slides/05_cluster_sampling_slides.md b/03_instructional_team/markdown_slides/05_cluster_sampling_slides.md
@@ -187,7 +187,7 @@ $ echo "Data Sciences Institute"
 
 - The sample variance of the PSU totals is,
 
-  > $$ s_t^2=\frac{1}{n-1}\sum_{i=1}^{N}(t_i-\frac{\hat{t}}{N})^2 $$
+  > $$ s_t^2=\frac{1}{n-1}\sum_{i=1}^{n}(t_i-\frac{\hat{t}}{n})^2 $$
 
 - $s_t^2$ can then be used to compute the standard error of the estimated sample mean:
 

diff --git a/03_instructional_team/markdown_slides/09_privacy_slides.md b/03_instructional_team/markdown_slides/09_privacy_slides.md
@@ -14,7 +14,7 @@ $ echo "Data Sciences Institute"
 # Key Texts
 
 - Salganik, M. (2019). Understanding and managing informational risk. In *Bit by bit: Social research in the Digital age* (pp. 307–314). Chapter, Princeton University Press. 
-- Wood, A., Altman, M., Bembenek, A., Bun, M., Gaboardi, M., Honaker, J., Nissim, K., OBrien, D.R., Steinke, T., & Vadhan, S. (2018). *[Differential privacy: A primer for a non-technical audience](https://salil.seas.harvard.edu/publications/differential-privacy-primer-non-technical-audience)* . *Vanderbilt Journal of Entertainment & Technology Law, * 21(1) 209-275.
+- Wood, A., Altman, M., Bembenek, A., Bun, M., Gaboardi, M., Honaker, J., Nissim, K., O'Brien, D.R., Steinke, T., & Vadhan, S. (2018). *[Differential privacy: A primer for a non-technical audience](https://salil.seas.harvard.edu/publications/differential-privacy-primer-non-technical-audience)* . *Vanderbilt Journal of Entertainment & Technology Law, * 21(1) 209-275.
 
 ---
 

diff --git a/.../sampling_multiple_imputation_exerises.py → ...sampling_multiple_imputation_exercises.py b/.../sampling_multiple_imputation_exerises.py → ...sampling_multiple_imputation_exercises.py
diff --git a/README.md b/README.md
@@ -69,13 +69,11 @@ module, with access to live help. Content is not facilitated, but rather
 this time should be driven by participants. We encourage participants to
 come to these work periods with questions and problems to work through.
   Participants are encouraged to engage actively during the learning
-module. They key to developing the core skills in each learning module
+module. The key to developing the core skills in each learning module
 is through practice. The more participants engage in coding along with
 the instructional team, and applying the skills in each module, the more
 likely it is that these skills will solidify.   
 
-## Schedule 
-
 ## Schedule
 
 | Live Learning Session | Topic                                                                                                | Assignments                                                                                    | Resources                                                                                                                                      |   |
@@ -127,7 +125,7 @@ Feel free to use the following as resources:
 -   [LLN Demo](./04_this_cohort/resources/5.1_probability_lln_demo.py)
 -   [Amazon Exercises](./04_this_cohort/resources/amazon_exercises.pdf)
 -   [Multiple Imputation
-    Exercises](./04_this_cohort/resources/sampling_multiple_imputation_exerises.py)
+    Exercises](./04_this_cohort/resources/sampling_multiple_imputation_exercises.py)
 
 ### Videos