STAT 2060

University of Guelph

Course Overview

Lessons & Practice

I. Welcome

1. Introduction to Statistics

1.1hr

1.1.1. Introduction to Statistics4 min

1.1.2. Sample vs. Population7 min

1.1.3. Practice

1.1.4. Measurement Scales7 min

1.1.5. Practice

1.2.1. Statistical Sampling28 sec

1.2.2. Convenience Sampling1 min

1.2.3. Simple Random Sampling3 min

1.2.4. Systematic Sampling2 min

1.2.5. Stratified Sampling vs. Cluster Sampling6 min

1.2.6. Two-Stage Cluster Sampling2 min

1.2.7. Practice

1.2.8. Practice

1.2.9. Practice

1.3.1. Sampling Bias7 min

1.3.2. Recipe for a Good Sample5 min

1.3.3. Practice

1.3.4. Practice

1.4.1. Observational Studies4 min

1.4.2. Experimental Studies3 min

1.4.3. Example5 min

1.4.4. Practice

1.5.1. Types of Experimental Designs7 min

1.5.2. Principles of Experimental Design4 min

1.5.4. Practice

1.5.5. Practice

3. Descriptive Statistics

1hr

3.1.1. Measures of Central Tendency7 min

3.1.2. Mean and Median Example2 min

3.1.3. Practice

3.1.4. Practice2 min

3.1.5. Practice3 min

3.2.1. Skewness and Symmetry 6 min

3.2.2. Practice

3.3.1. Percentiles4 min

3.3.2. Example2 min

3.3.3. Practice

3.4.1. Range and Interquartile Range11 min

3.4.2. Variance and Standard Deviation6 min

3.4.3. Practice3 min

3.4.4. Practice3 min

3.4.5. Practice

3.5.1. Boxplots and Five Number Summary9 min

3.5.2. Practice4 min

3.5.3. Practice

4. Probability

2.4hr

4.1.1. Events & Sample Spaces6 min

4.1.2. Types of Probability6 min

4.1.3. Probability Language & Notation4 min

4.1.4. Example3 min

4.1.5. Independent vs. Mutually Exclusive Events4 min

4.1.6. Example2 min

4.1.7. Practice

4.2.1. Venn Diagrams3 min

4.2.2. Tree Diagrams4 min

4.2.3. Two-way Table5 min

4.3.1. Interpretation of Probability2 min

4.3.2. Complement Rule3 min

4.3.3. Practice1 min

4.3.4. The Addition Rule4 min

4.4.1. Probability of Independent Events4 min

4.4.2. Example3 min

4.4.3. Practice2 min

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

4.5.2. Conditional Probability: Tree Diagram5 min

4.5.3. Bayes’ Theorem4 min

4.5.4. Example4 min

4.5.5. Practice3 min

4.6.1. Example6 min

4.6.2. Example7 min

4.6.3. Example7 min

4.6.4. Practice4 min

4.6.5. Practice5 min

4.7.1. Summary of Key Definitions6 min

4.8.1. Fundamental Counting Principle6 min

4.8.2. Permutations and Combinations6 min

4.8.3. Practice1 min

4.8.4. Practice54 sec

4.8.5. More Combination Rules3 min

4.8.6. Example4 min

4.8.7. Practice3 min

4.8.8. Practice

4.9.1. Counting and Probability3 min

4.9.3. Practice1 min

6. Continuous Distributions

1.6hr

6.1.1. Continuous Probability Distribution Theory10 min

6.2.1. Uniform Distribution1 min

6.2.2. Practice3 min

6.3.1. Exponential Experiment3 min

6.3.2. Exponential Distribution7 min

6.3.3. Practice7 min

6.4.1. Normal Distribution11 min

6.4.2. Practice5 min

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

6.5.2. Standardizing a Normal Random Variable4 min

6.5.3. Example10 min

6.5.4. Practice6 min

6.5.5. Practice3 min

6.6.1. Normal Approximation to Binomial2 min

6.6.2. Practice5 min

6.6.3. Practice4 min

6.6.4. Practice4 min

7. Sampling and Sampling Distribution

59min

7.1.1. Sampling Variability & Sampling Distribution12 min

7.2.1. Central Limit Theorem 5 min

7.2.2. Sampling Distribution for a Mean2 min

7.2.3. Example9 min

7.2.4. Sampling Distribution for a Mean Question5 min

7.3.1. Sampling Distribution of Proportions5 min

7.3.2. Example3 min

7.3.3. Practice4 min

7.4.1. Drawing Samples From a Finite Population8 min

7.4.2. Example4 min

7.4.3. Practice3 min

8. Statistical Inference: Estimation for Single Populations

48min

8.1.1. Introduction to Confidence Intervals14 min

8.2.1. Confidence Interval for a Mean13 min

8.2.2. Example4 min

8.2.3. Practice3 min

8.3.1. Confidence Interval for a Proportion2 min

8.3.2. Example5 min

8.3.3. Practice4 min

8.4.1. Margin of Error and Sample Size3 min

8.4.2. Practice

9. Statistical Inference: Hypothesis Testing for Single Populations

1.9hr

9.1.1. Introduction to Hypothesis Testing 15 min

9.1.2. Hypothesis Testing Steps25 min

9.2.1. Hypothesis Test for a Mean3 min

9.2.2. Example16 min

9.2.3. Example8 min

9.2.4. Practice4 min

9.3.1. Hypothesis Test for a Proportion10 min

9.3.2. Example4 min

9.3.3. Practice5 min

9.3.4. Practice9 min

9.4.1. Type I and Type II Errors5 min

9.4.2. Type I and Type II Errors1 min

9.4.3. Practice

9.4.4. Practice

9.4.5. Power7 min

9.4.6. Practice

10. Statistical Inferences about Two Populations

3hr

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

10.1.2. Confidence Interval for Two Means Containing Zero7 min

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

10.2.1. Confidence Interval Unpooled t2 min

10.2.2. Example: Confidence Interval (Unpooled)5 min

10.2.3. Practice (Unpooled)

10.3.1. Confidence Interval Pooled t2 min

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

10.3.3. Practice (Pooled)

10.4.1. Comparing Two Independent Means9 min

10.4.2. Hypothesis Test Steps: Comparing Two Independent Means11 min

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

10.5.2. Example (Unpooled)11 min

10.5.3. Practice (Unpooled)5 min

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

10.6.2. Example (Pooled)7 min

10.6.3. Practice (Pooled)7 min

10.7.1. Match Pairs for Dependent Means12 min

10.8.1. Matched Pairs Confidence Interval for Differences in Two Dependent Population Means4 min

10.8.2. Practice

10.9.1. Hypothesis Test for Matched Pairs11 min

10.9.2. Example7 min

10.9.3. Practice3 min

10.10.1. Confidence Interval for Differences in Population Proportions3 min

10.10.2. Confidence Interval for Two Proportions1 min

10.10.3. Example4 min

10.10.4. Practice4 min

10.11.1. Hypothesis Testing for Two Independent Population Proportions9 min

10.11.2. Hypothesis Test Steps: Comparing Two Independent Proportions8 min

10.11.3. Example13 min

10.11.4. Practice (Pooled)6 min

11. Simple Regression Analysis and Correlation

4hr

11.1.1. Scatterplots5 min

11.1.2. Practice

11.2.1. Correlation5 min

11.2.2. Practice

11.3.1. Solving for Correlation2 min

11.3.2. Example6 min

11.3.3. Practice4 min

11.3.4. Example3 min

11.3.5. Practice

11.4.1. Coefficient of Determination (R Squared)7 min

11.4.2. Assessing Goodness of Fit6 min

11.4.3. Practice

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

11.5.2. Example7 min

11.5.3. Practice (Version 1)3 min

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

11.5.5. Example5 min

11.5.6. Practice (Version 2)5 min

11.6.1. Predictions3 min

11.6.2. Residuals (Errors)5 min

11.6.3. Practice

11.6.4. Residual Plots2 min

11.6.5. Regression Diagnostics 5 min

11.6.6. Practice

11.6.7. Standard Deviation of the Residuals5 min

11.6.8. Practice

11.7.1. Estimating the Coefficients of the Linear Regression Model9 min

11.7.2. Measures of Variation in Regression9 min

11.7.3. Practice

11.8.1. Standard Error and R Square Formulas7 min

11.8.2. Example5 min

11.8.3. Practice4 min

11.9.1. Confidence Interval for Linear Regression5 min

11.9.2. Example9 min

11.9.3. Practice3 min

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

11.9.5. Practice

11.10.1. Hypothesis Testing for Linear Regression14 min

11.10.2. Example17 min

11.10.3. Example13 min

11.10.4. Practice: Linear Regression Analysis6 min

11.11.1. Prediction Interval of Y7 min

11.11.2. Example12 min

11.11.3. Practice10 min

11.11.4. Practice

12. Analysis of Variance and Design of Experiments

38min

12.1.1. One-Way ANOVA8 min

12.1.2. One-Way ANOVA Table10 min

12.1.3. Example9 min

12.1.4. Practice

12.2.1. One-Way ANOVA Hypothesis Test6 min

12.2.2. Example4 min

12.2.3. Practice

Mock Exams

ME.1. Mock Exam21 Qns

ME.2. Mock Exam18 Qns

ME.3. Mock Exam27 Qns

ME.4. Midterm 1 - Mock Assessment32 Qns

ME.5. Midterm 2 - Mock Assessment33 Qns

ME.6. Final Exam - Mock Assessment25 Qns

ME.2 Mock Exam

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