STAT 161

U of A

Course Overview

Lessons & Practice

I. Welcome

1 min

1. Collecting Data & Sampling

44min

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

2. Experiments and Observational Studies

23min

2.1.1. Observational Studies4 min

2.1.2. Experimental Studies3 min

2.1.3. Example5 min

2.1.4. Practice

2.2.1. Types of Experimental Designs7 min

2.2.2. Principles of Experimental Design4 min

2.2.3. Example

2.2.4. Practice

2.2.5. Practice

3. Displaying & Summarizing Quantitative Data

42min

3.1.1. Boxplots and Five Number Summary9 min

3.1.2. Practice4 min

3.1.3. Practice

3.2.1. Histograms4 min

3.2.2. Practice2 min

3.2.3. Practice2 min

3.2.4. Practice

3.3.1. Dot Plots5 min

3.3.2. Practice

3.4.1. Frequency Polygons2 min

3.4.2. Practice4 min

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

3.5.2. Practice3 min

3.6.1. Times Series Plots2 min

3.6.2. Practice2 min

4. Displaying & Summarizing Categorical Data

20min

4.1.1. Pie Chart3 min

4.1.2. Practice

4.2.1. Bar Chart3 min

4.2.2. Practice

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

4.3.2. Practice4 min

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

4.4.2. Simpson’s Paradox3 min

4.4.3. Practice

5. Exploratory Data Analysis

49min

5.1.1. Measures of Central Tendency7 min

5.1.2. Mean and Median Example2 min

5.1.3. Practice

5.1.4. Practice2 min

5.1.5. Practice3 min

5.2.1. Skewness and Symmetry 6 min

5.2.2. Practice

5.3.1. Percentiles4 min

5.3.2. Example2 min

5.3.3. Practice

5.4.1. Range and Interquartile Range11 min

5.4.2. Variance and Standard Deviation6 min

5.4.3. Practice3 min

5.4.4. Practice3 min

5.4.5. Practice

6. Scatterplots, Association, and Correlation

37min

6.1.1. Scatterplots5 min

6.1.2. Practice

6.2.1. Correlation5 min

6.2.2. Practice

6.3.1. Solving for Correlation2 min

6.4.1. Coefficient of Determination (R Squared)7 min

6.4.2. Assessing Goodness of Fit6 min

6.4.3. Practice

7. Simple Linear Regression

45min

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

7.1.2. Example7 min

7.1.3. Practice (Version 1)3 min

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

7.1.5. Example5 min

7.1.6. Practice (Version 2)5 min

7.2.1. Predictions3 min

7.2.2. Residuals (Errors)5 min

7.2.3. Practice

7.2.4. Residual Plots2 min

7.2.5. Regression Diagnostics 5 min

7.2.6. Practice

7.2.7. Standard Deviation of the Residuals5 min

7.2.8. 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

8.7.1. Summary of Key Definitions6 min

9. Counting Techniques

29min

9.1.1. Fundamental Counting Principle6 min

9.1.2. Permutations and Combinations6 min

9.1.3. Practice1 min

9.1.4. Practice54 sec

9.1.5. More Combination Rules3 min

9.1.6. Example4 min

9.1.7. Practice3 min

9.1.8. Practice

9.2.1. Counting and Probability3 min

9.2.2. Example

9.2.3. Practice1 min

10. Discrete Random Variables

1.9hr

10.1.1. Discrete Random Variables8 min

10.1.2. Practice

10.1.3. Probability Distribution Function4 min

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

10.1.5. Example5 min

10.1.6. Example4 min

10.1.7. Practice6 min

10.1.8. Practice4 min

10.1.9. Practice

10.2.1. Transforming Data: Shifting & Scaling11 min

10.2.2. Practice2 min

10.2.3. Linear Transformation3 min

10.2.4. Example4 min

10.2.5. Practice2 min

10.3.1. Cumulative Distribution Function (CDF)3 min

10.3.2. Example6 min

10.3.3. Practice2 min

10.4.1. Combining Random Variables7 min

10.4.2. Example2 min

10.4.3. Practice2 min

10.4.4. Example3 min

10.4.5. Practice

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

10.4.7. Example3 min

10.4.8. Practice

10.4.9. Practice2 min

10.5.1. Conditional Probability & Joint Distribution7 min

10.5.2. Example5 min

10.5.3. Practice

10.5.4. Random Variables & Joint Distribution7 min

12. Continuous Probability Distributions

1.7hr

12.1.1. Continuous Probability Distribution Theory10 min

12.2.1. Uniform Distribution1 min

12.2.2. Practice3 min

12.3.1. Exponential Experiment3 min

12.3.2. Exponential Distribution7 min

12.3.3. Practice7 min

12.4.1. Normal Distribution11 min

12.4.2. Practice5 min

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

12.5.2. Standardizing a Normal Random Variable4 min

12.5.3. Example10 min

12.5.4. Practice6 min

12.5.5. Practice3 min

12.6.1. Normal Approximation to Binomial2 min

12.6.2. Practice5 min

12.6.3. Practice4 min

12.6.4. Practice4 min

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

12.7.2. Practice2 min

13. Sampling Distribution

59min

13.1.1. Sampling Variability & Sampling Distribution12 min

13.2.1. Central Limit Theorem 5 min

13.2.2. Sampling Distribution for a Mean2 min

13.2.3. Example9 min

13.2.4. Sampling Distribution for a Mean Question5 min

13.3.1. Sampling Distribution of Proportions5 min

13.3.2. Example3 min

13.3.3. Practice4 min

13.4.1. Drawing Samples From a Finite Population8 min

13.4.2. Example4 min

13.4.3. Practice3 min

14. Estimating with Confidence Intervals

48min

14.1.1. Introduction to Confidence Intervals14 min

14.2.1. Confidence Interval for a Mean13 min

14.2.2. Example4 min

14.2.3. Practice3 min

14.3.1. Confidence Interval for a Proportion2 min

14.3.2. Example5 min

14.3.3. Practice4 min

14.4.1. Margin of Error and Sample Size3 min

14.4.2. Practice

15. Hypothesis Testing with One Sample

1.9hr

15.1.1. Introduction to Hypothesis Testing 15 min

15.1.2. Hypothesis Testing Steps25 min

15.2.1. Hypothesis Test for a Mean3 min

15.2.2. Example16 min

15.2.3. Example8 min

15.2.4. Practice4 min

15.3.1. Hypothesis Test for a Proportion10 min

15.3.2. Example4 min

15.3.3. Practice5 min

15.3.4. Practice9 min

15.4.1. Type I and Type II Errors5 min

15.4.2. Type I and Type II Errors1 min

16. Inference for Two Population Means

1.6hr

16.1.1. Comparing Two Independent Means9 min

16.1.2. Hypothesis Test Steps: Comparing Two Independent Means11 min

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

16.2.2. Example (Unpooled)11 min

16.2.3. Practice (Unpooled)5 min

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

16.3.2. Example (Pooled)7 min

16.3.3. Practice (Pooled)7 min

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

16.4.2. Confidence Interval for Two Means Containing Zero7 min

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

16.5.1. Confidence Interval Unpooled t2 min

16.5.2. Example: Confidence Interval (Unpooled)5 min

16.5.3. Practice (Unpooled)

16.6.1. Confidence Interval Pooled t2 min

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

16.6.3. Practice (Pooled)

17. Match Pairs (Dependent Means)

37min

17.1.1. Match Pairs for Dependent Means12 min

17.2.1. Hypothesis Test for Matched Pairs11 min

17.2.2. Example7 min

17.2.3. Practice3 min

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

17.3.2. Practice

18. Inference for Two Population Proportions

49min

18.1.1. Hypothesis Testing for Two Independent Population Proportions9 min

18.1.2. Hypothesis Test Steps: Comparing Two Independent Proportions8 min

18.1.3. Example13 min

18.1.4. Practice (Pooled)6 min

18.2.1. Confidence Interval for Differences in Population Proportions3 min

18.2.2. Confidence Interval for Two Proportions1 min

18.2.3. Example4 min

18.2.4. Practice4 min

19. Chi-Square

48min

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

20. Analysis of Variance

38min

20.1.1. One-Way ANOVA8 min

20.1.2. One-Way ANOVA Table10 min

20.1.3. Example9 min

20.1.4. Practice

20.2.1. One-Way ANOVA Hypothesis Test6 min

20.2.2. Example4 min

20.2.3. Practice

Mock Exams

ME.1. Mock Exam21 Qns

ME.2. Mock Exam18 Qns

ME.3. Mock Exam27 Qns

ME.4. Midterm - Mock Assessment50 Qns

ME.5. Final Exam - Mock Assessment55 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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This course is still in beta, meaning that we need your help to make it even better! If you notice any missing topics or topics that don't belong in the course, please let us know by clicking on the speech bubble in the bottom right corner of the screen, then we'll get right on it!

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STAT 161

Course Overview

1. Collecting Data & Sampling

2. Experiments and Observational Studies

3. Displaying & Summarizing Quantitative Data

4. Displaying & Summarizing Categorical Data

5. Exploratory Data Analysis

6. Scatterplots, Association, and Correlation

7. Simple Linear Regression

8. Probability

9. Counting Techniques

10. Discrete Random Variables

11. Discrete Probability Distributions

12. Continuous Probability Distributions

13. Sampling Distribution

14. Estimating with Confidence Intervals

15. Hypothesis Testing with One Sample

16. Inference for Two Population Means

17. Match Pairs (Dependent Means)

18. Inference for Two Population Proportions

19. Chi-Square

20. Analysis of Variance

Mock Exams

I Welcome

Free Activity

Ask a Question

Anonymous

Lawron L

Liz F

Lawron L

sophia c

Lawron L

Anonymous

Jessica Y

Welcome to Wize's Statistics Course!