COMM 162

Queen's

Course Overview

Lessons & Practice

I. Welcome

1 min

1. Introduction to Statistics

18min

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

2. Displaying & Summarizing Quantitative Data

52min

2.I. Chapter Intro5 min

2.1.1. Boxplots and Five Number Summary9 min

2.1.2. Practice4 min

2.1.3. Practice

2.2.1. Histograms4 min

2.2.2. Practice2 min

2.2.3. Practice2 min

2.2.4. Practice

2.3.1. Frequency Polygons2 min

2.3.2. Practice4 min

2.4.1. Stemplots (Stem-and-Leaf Plots)2 min

2.4.2. Practice3 min

2.5.1. Times Series Plots2 min

2.5.2. Practice2 min

2.6.1. Scatterplots5 min

2.6.2. Practice: Scatterplots

2.7.1. Dot Plots5 min

2.7.2. Practice

3. Displaying & Summarizing Categorical Data

20min

3.1.1. Pie Chart3 min

3.1.2. Practice

3.2.1. Bar Chart3 min

3.2.2. Practice

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

3.3.2. Practice4 min

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

3.4.2. Simpson’s Paradox3 min

3.4.3. Practice

4. Exploratory Data Analysis

49min

4.1.1. Measures of Central Tendency7 min

4.1.2. Mean and Median Example2 min

4.1.3. Practice

4.1.4. Practice2 min

4.1.5. Practice3 min

4.2.1. Skewness and Symmetry 6 min

4.2.2. Practice

4.3.1. Percentiles4 min

4.3.2. Example2 min

4.3.3. Practice

4.4.1. Range and Interquartile Range11 min

4.4.2. Variance and Standard Deviation6 min

4.4.3. Practice3 min

4.4.4. Practice3 min

4.4.5. Practice

5. Sampling Methods

26min

5.1.1. Statistical Sampling28 sec

5.1.2. Convenience Sampling1 min

5.1.3. Simple Random Sampling3 min

5.1.4. Systematic Sampling2 min

5.1.5. Stratified Sampling vs. Cluster Sampling6 min

5.1.6. Two-Stage Cluster Sampling2 min

5.1.7. Practice

5.1.8. Practice

5.1.9. Practice

5.2.1. Sampling Bias7 min

5.2.2. Recipe for a Good Sample5 min

5.2.3. Practice

5.2.4. Practice

6. Discrete Random Variables

1.9hr

6.1.1. Discrete Random Variables8 min

6.1.2. Practice

6.1.3. Probability Distribution Function4 min

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

6.2.1. Transforming Data: Shifting & Scaling11 min

6.2.2. Practice2 min

6.2.3. Linear Transformation3 min

6.2.4. Example4 min

6.2.5. Practice2 min

6.3.1. Cumulative Distribution Function (CDF)3 min

6.3.2. Example6 min

6.3.3. Practice2 min

6.4.1. Combining Random Variables7 min

6.4.6. Functions of Two Independent Random Variables X and Y5 min

6.4.7. Example3 min

6.4.8. Practice

6.4.9. Practice2 min

6.5.1. Conditional Probability & Joint Distribution7 min

6.5.2. Example5 min

6.5.3. Practice

6.5.4. Random Variables & Joint Distribution7 min

7. Discrete Probability Distributions

56min

7.1.1. Bernoulli Trials4 min

7.1.2. Practice4 min

7.1.3. Binomial Experiment4 min

7.1.4. Binomial Distribution8 min

7.2.1. Poisson Experiment5 min

7.2.2. Poisson Distribution1 min

7.2.3. Example3 min

7.2.4. Practice5 min

7.2.5. Practice5 min

8. Continuous Probability Distributions

1.2hr

8.1.1. Continuous Probability Distribution Theory10 min

8.2.1. Uniform Distribution1 min

8.2.2. Practice3 min

8.3.1. Normal Distribution11 min

8.3.2. Practice5 min

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

8.4.2. Standardizing a Normal Random Variable4 min

8.4.3. Example10 min

8.4.4. Practice6 min

8.4.5. Practice3 min

8.5.1. Tchebysheff’s Theorem (Chebyshev's Theorem)3 min

8.5.2. Practice2 min

9. Sampling Distribution

47min

9.1.1. Sampling Variability & Sampling Distribution12 min

9.2.1. Central Limit Theorem 5 min

9.2.2. Sampling Distribution for a Mean2 min

9.2.3. Example9 min

9.2.4. Sampling Distribution for a Mean Question5 min

9.3.1. Drawing Samples From a Finite Population8 min

9.3.2. Example4 min

9.3.3. Practice3 min

10. Estimating with Confidence Intervals

34min

10.1.1. Introduction to Confidence Intervals14 min

10.2.1. Confidence Interval for a Mean13 min

10.2.2. Example4 min

10.2.3. Practice3 min

11. Sampling Distribution (Proportion)

12min

11.1.1. Sampling Distribution of Proportions5 min

11.1.2. Example3 min

11.1.3. Sampling Distribution of Proportions4 min

12. Confidence Interval (Proportion)

14min

12.1.1. Confidence Interval for a Proportion2 min

12.1.2. Example5 min

12.1.3. Practice4 min

12.2.1. Margin of Error and Sample Size3 min

12.2.2. Practice

13. Hypothesis Testing with One Sample

1.9hr

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. Hypothesis Test for a Proportion10 min

13.3.2. Example4 min

13.3.3. Practice5 min

13.3.4. Practice9 min

13.4.1. Type I and Type II Errors5 min

13.4.2. Type I and Type II Errors1 min

14. Test of Equality of Two Variances

35min

14.1.1. Test of Equality of Two Variances7 min

14.2.1. F-Test for Equality of Two Variances11 min

14.2.2. Example6 min

14.2.3. F-Test for Equality of Two Variances

14.3.1. Levene’s Test for the Equality of Two Variances4 min

14.3.2. Example7 min

14.3.3. Practice

15. Inference for Two Population Means

1.6hr

15.1.1. Comparing Two Independent Means9 min

15.1.2. Hypothesis Test Steps: Comparing Two Independent Means11 min

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

15.2.2. Example (Unpooled)11 min

15.2.3. Practice (Unpooled)5 min

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

15.3.2. Example (Pooled)7 min

15.3.3. Practice (Pooled)7 min

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

15.4.2. Confidence Interval for Two Means Containing Zero7 min

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

15.5.1. Confidence Interval Unpooled t2 min

15.5.2. Example: Confidence Interval (Unpooled)5 min

15.5.3. Practice (Unpooled)

15.6.1. Confidence Interval Pooled t2 min

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

15.6.3. Practice (Pooled)

16. Match Pairs (Dependent Means)

37min

16.1.1. Match Pairs for Dependent Means12 min

16.2.1. Hypothesis Test for Matched Pairs11 min

16.2.2. Example7 min

16.2.3. Practice3 min

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

16.3.2. Practice

17. Inference for Two Population Proportions

49min

17.1.1. Hypothesis Testing for Two Independent Population Proportions9 min

17.1.2. Hypothesis Test Steps: Comparing Two Independent Proportions8 min

17.1.3. Example13 min

17.1.4. Practice (Pooled)6 min

17.2.1. Confidence Interval for Differences in Population Proportions3 min

17.2.2. Confidence Interval for Two Proportions1 min

17.2.3. Example4 min

17.2.4. Practice4 min

18. Scatterplots, Association, and Correlation

37min

18.1.1. Scatterplots5 min

18.1.2. Practice

18.2.1. Correlation5 min

18.2.2. Practice

18.3.1. Solving for Correlation2 min

18.3.2. Example6 min

18.3.3. Practice4 min

18.3.4. Example3 min

18.3.5. Practice

18.4.1. Coefficient of Determination (R Squared)7 min

18.4.2. Assessing Goodness of Fit6 min

18.4.3. Practice

19. Simple Linear Regression

41min

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

19.1.2. Example7 min

19.1.3. Practice (Version 1)3 min

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

19.1.5. Example5 min

19.1.6. Practice (Version 2)5 min

19.2.1. Predictions3 min

19.2.2. Residuals (Errors)5 min

19.2.3. Practice

19.2.4. Residual Plots2 min

19.2.5. Regression Diagnostics 5 min

19.2.6. Practice

20. Inference for Linear Regression

1.8hr

20.1.1. Estimating the Coefficients of the Linear Regression Model9 min

20.1.2. Measures of Variation in Regression9 min

20.1.3. Practice

20.2.1. Standard Error and R Square Formulas7 min

20.2.2. Example5 min

20.2.3. Practice4 min

20.3.1. Confidence Interval for Linear Regression5 min

20.3.2. Example9 min

20.3.3. Practice3 min

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

20.3.5. Practice

20.4.1. Hypothesis Testing for Linear Regression14 min

20.4.2. Example17 min

20.4.3. Example13 min

20.4.4. Practice: Linear Regression Analysis6 min

21. Multiple Regression

1.7hr

21.1.1. Assessing a Multiple Regression Model5 min

21.1.2. F-test for Multiple Regression5 min

21.1.3. Solving for the F-score9 min

21.1.4. Example2 min

21.1.5. Practice8 min

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

21.2.2. Example26 min

21.2.3. Example13 min

21.2.4. Practice13 min

21.2.5. Practice12 min

Mock Exams

ME.1. Mock Exam21 Qns

ME.2. Mock Exam18 Qns

ME.3. Mock Exam27 Qns

ME.4. Midterm Exam - Mock Assessment26 Qns

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Answered

Anonymous

Does this cover every topic needed for the final exam? Including combination/permutation, and other small sections?

Lawron L

Instructor

Hello - combination/permutation is found under the 'Counting Rules' chapter. Thank you.

Answered

Liz F

Do you have example questions on how to calculate frequency, relative cumulative frequency, and cumulative relative frequency?

Lawron L

Instructor

Yes, please see the sectiom about Frequency Polygons. 

Notes:
Frequency = counts
Cumulative Frequency = counts accumulated from the minimum 
Relative Cumulative Frequency = % accumulated from the minimum 

Answered

sophia c

do you have a video that helps me understand z scores

Lawron L

Instructor

z scores are introduced in "Continuous Probability Distribution" > "Normal Distributions" and "Standardizing"

Answered

Anonymous

Who came with this concept? 

Jessica Y

Instructor

I don't think one specific person came up with the concept of statistic. It's a very useful branch of math, so I think people saw its application and developed different tools that help us answer important questions :)

Welcome to Wize's Statistics Course!
My name is Lawron and I'm here to help you ace your course! Feel free to go through this resource at your own pace
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