MGCR 271

McGill

Course Overview

Lessons & Practice

I. Welcome

1. Data

11min

1.1.1. Introduction to Statistics4 min

1.1.2. Measurement Scales7 min

1.1.3. Practice

2. Displaying & Summarizing Categorical Data

20min

2.1.1. Pie Chart3 min

2.1.2. Practice

2.2.1. Bar Chart3 min

2.2.2. Practice

2.3.1. Segmented Bar Chart (or Stacked Columns)3 min

2.3.2. Practice4 min

2.4.1. Contingency Table (Two-Way Table)4 min

2.4.2. Simpson’s Paradox3 min

2.4.3. Practice

3. Displaying & Summarizing Quantitative Data

1.3hr

4. Normal Distribution

1hr

4.1.1. Continuous Probability Distribution Theory10 min

4.2.1. Normal Distribution11 min

4.2.2. Practice5 min

4.3.1. Standard Normal Distribution (Z-Scores)11 min

4.3.2. Standardizing a Normal Random Variable4 min

4.3.3. Example10 min

4.3.4. Practice6 min

4.3.5. Practice3 min

5. Scatterplots, Association, and Correlation

37min

5.1.1. Scatterplots5 min

5.1.2. Practice

5.2.1. Correlation5 min

5.2.2. Practice

5.3.1. Solving for Correlation2 min

5.3.2. Example6 min

5.3.3. Practice

5.3.4. Practice4 min

5.3.5. Example3 min

5.4.1. Coefficient of Determination (R Squared)7 min

5.4.2. Assessing Goodness of Fit6 min

5.4.3. Practice

6. Sampling and Producing Data

33min

6.1.1. Sample vs. Population7 min

6.1.2. Practice

6.2.1. Statistical Sampling28 sec

6.2.2. Convenience Sampling1 min

6.2.3. Simple Random Sampling3 min

6.2.4. Systematic Sampling2 min

6.2.5. Stratified Sampling vs. Cluster Sampling6 min

6.2.6. Two-Stage Cluster Sampling2 min

6.2.7. Practice

6.2.8. Practice

6.2.9. Practice

6.3.1. Sampling Bias7 min

6.3.2. Recipe for a Good Sample5 min

6.3.3. Practice

6.3.4. Practice

7. Experiments and Observational Studies

23min

7.1.1. Observational Studies4 min

7.1.2. Experimental Studies3 min

7.1.3. Example5 min

7.1.4. Practice

7.2.1. Types of Experimental Designs7 min

7.2.2. Principles of Experimental Design4 min

7.2.4. Practice

7.2.5. Practice

8. Probability

1.9hr

8.1.1. Events & Sample Spaces6 min

8.1.2. Types of Probability6 min

8.1.3. Probability Language & Notation4 min

8.1.4. Example3 min

8.1.5. Independent vs. Mutually Exclusive Events4 min

8.1.6. Example2 min

8.1.7. Practice

8.2.1. Venn Diagrams3 min

8.2.2. Tree Diagrams4 min

8.2.3. Two-way Table5 min

8.3.1. Interpretation of Probability2 min

8.3.2. Complement Rule3 min

8.3.3. Practice1 min

8.3.4. The Addition Rule4 min

8.4.1. Probability of Independent Events4 min

8.4.2. Example3 min

8.4.3. Practice2 min

8.5.1. Probability of Dependent Events (Conditional Probability)7 min

8.5.2. Conditional Probability: Tree Diagram5 min

8.5.3. Bayes’ Theorem4 min

8.5.4. Example4 min

8.5.5. Practice3 min

8.6.1. Example6 min

8.6.2. Example7 min

8.6.3. Example7 min

8.6.4. Practice4 min

8.6.5. Practice5 min

8.7.1. Summary of Key Definitions6 min

9. Discrete Random Variables

36min

9.1.1. Discrete Random Variables8 min

9.1.2. Probability Distribution Function4 min

9.1.3. Example4 min

9.1.4. Practice

9.1.5. Practice6 min

9.1.6. Pratice4 min

9.2.1. Expected Value, Variance, and Standard Deviation of Discrete Random Variables6 min

9.2.2. Example5 min

9.2.3. Practice

9.3.1. Linear Combinations of Random Variables

9.3.4. Practice

10. Discrete Probability Distributions

51min

10.1.1. Bernoulli Trials4 min

10.1.2. Practice4 min

10.1.3. Binomial Experiment4 min

10.1.4. Binomial Distribution8 min

10.1.5. Example10 min

10.1.6. Example1 min

10.1.7. Practice6 min

10.1.8. Practice

10.2.1. Normal Approximation to Binomial2 min

10.2.2. Practice5 min

10.2.3. Normal Approximation to the Binomial Distribution Question4 min

10.2.4. Normal Approximation to the Binomial Distribution Question4 min

11. Sampling Distribution

45min

11.1.1. Sampling Variability & Sampling Distribution12 min

11.2.1. Central Limit Theorem 5 min

11.2.2. Sampling Distribution for a Mean2 min

11.2.3. Example9 min

11.2.4. Sampling Distribution for a Mean Question5 min

11.3.1. Sampling Distribution of Proportions5 min

11.3.2. Example3 min

11.3.3. Sampling Distribution of Proportions4 min

12. Estimating with Confidence Intervals

37min

12.1.1. Introduction to Confidence Intervals14 min

12.2.1. Confidence Interval for a Mean13 min

12.2.2. Example4 min

12.2.3. Practice3 min

12.3.1. Margin of Error and Sample Size3 min

12.3.2. Practice

13. Hypothesis Testing with One Sample

1.4hr

13.1.1. Introduction to Hypothesis Testing 15 min

13.1.2. Hypothesis Testing Steps25 min

13.2.1. Hypothesis Test for a Mean3 min

13.2.2. Example16 min

13.2.3. Example8 min

13.2.4. Practice4 min

13.3.1. Type I and Type II Errors5 min

13.3.2. Type I and Type II Errors1 min

13.3.3. Practice

13.3.4. Practice

13.3.5. Power7 min

13.3.6. Practice

14. Inference for Two Population Means

1.6hr

14.1.1. Confidence Interval for Differences in Population Means (Pooled and Unpooled)6 min

14.1.2. Confidence Interval for Two Means Containing Zero7 min

14.1.3. Confidence Interval for Two Means (Independent Populations)9 min

14.2.1. Confidence Interval Unpooled t2 min

14.2.2. Example: Confidence Interval (Unpooled)5 min

14.2.3. Practice (Unpooled)

14.3.1. Confidence Interval Formula Pooled t2 min

14.3.2. Example: Confidence Interval w/ Pooled T-test5 min

14.3.3. Practice (Pooled)

14.4.1. Comparing Two Independent Means9 min

14.4.2. Hypothesis Test Steps: Comparing Two Independent Means11 min

14.5.1. Unpooled t-test (Unequal Variances)4 min

14.5.2. Example (Unpooled)11 min

14.5.3. Practice (Unpooled)5 min

14.6.1. Pooled t-test (Equal Variances)3 min

14.6.2. Example (Pooled)7 min

14.6.3. Practice (Pooled)7 min

15. Simple Linear Regression

45min

15.1.1. Solving for the Regression Line (r, Sy, Sx Method)5 min

15.1.2. Example7 min

15.1.3. Solving for the Regression Line (Least Squares Method)2 min

15.1.4. Practice (Version 1)3 min

15.1.5. Practice (Version 2)5 min

15.1.6. Example5 min

15.2.1. Predictions3 min

15.2.2. Residuals (Errors)5 min

15.2.3. Practice

15.2.4. Residual Plots2 min

15.2.5. Regression Diagnostics 5 min

15.2.6. Practice

15.2.7. Standard Deviation of the Residuals5 min

15.2.8. Practice

16. Inference for Regression

2.2hr

16.1.1. Estimating the Coefficients of the Linear Regression Model9 min

16.1.2. Measures of Variation in Regression9 min

16.1.3. Practice

16.2.1. Standard Error and R Square Formulas7 min

16.2.2. Example5 min

16.2.3. Practice4 min

16.3.1. Confidence Interval for Linear Regression5 min

16.3.2. Example9 min

16.3.3. Slope: The Significance of "Zero" (or Lack Thereof)6 min

16.3.4. Practice3 min

16.3.5. Practice

16.4.1. Hypothesis Testing for Linear Regression14 min

16.4.2. Example17 min

16.4.3. Example13 min

16.4.4. Practice: Linear Regression Analysis6 min

16.5.1. Prediction Interval of Y7 min

16.5.2. Example12 min

16.5.3. Practice10 min

16.5.4. Practice

17. Multiple Regression

1.7hr

17.1.1. Assessing a Multiple Regression Model5 min

17.1.2. F-test for Multiple Regression5 min

17.1.3. Solving for the F-score9 min

17.1.4. Example2 min

17.1.5. Practice8 min

17.2.1. Adjusted R Square

17.2.2. Adjusted R Square

17.3.1. Hypothesis Testing for Multiple Regression (F-test)7 min

17.3.2. Example13 min

17.3.3. Example26 min

17.3.4. Practice13 min

17.3.5. Practice12 min

18. Inference for Proportions

1.5hr

18.1.1. Confidence Interval for a Proportion2 min

18.1.2. Example5 min

18.1.3. Practice4 min

18.2.1. Hypothesis Test for a Proportion10 min

18.2.2. Example4 min

18.2.3. Practice5 min

18.2.4. Practice9 min

18.3.1. Confidence Interval for Differences in Population Proportions3 min

18.3.2. Confidence Interval for Two Proportions1 min

18.3.3. Example4 min

18.3.4. Practice4 min

18.4.1. Hypothesis Testing for Two Independent Population Proportions9 min

18.4.2. Hypothesis Test Steps: Comparing Two Independent Proportions8 min

18.4.3. Example13 min

18.4.4. Practice (Pooled)6 min

19. Inference for Categorical Data (Chi-Square)

1.1hr

19.1.1. Types of Chi Square Tests8 min

19.1.2. Chi-Square Test for "Goodness of Fit"4 min

19.1.3. Example7 min

19.1.4. Pratice9 min

19.1.5. Chi-Square Test for Independence5 min

19.1.6. Example8 min

19.1.7. Practice4 min

19.1.8. More on Chi-Square Tests3 min

19.2.1. Contingency Table4 min

19.2.2. Marginal and Conditional Distributions5 min

19.2.3. Contingency Table (Two-way Table)5 min

19.2.4. Conditional Probability: Two-Way Table6 min

Mock Exams

ME.1. Midterm Exam 2 - Mock Assessment32 Qns

ME.2. Midterm Exam - Mock Assessment44 Qns

ME.3. Final Exam - Mock Assessment44 Qns

Midterm Exam Overview

Course Notes

Wizeprep's course notes with hundreds of easy-to-understand explanations, diagrams, and practice problems.

Want a tour?