STAT 213

U of C

Course Overview

Grade Boosters

Lessons & Practice

I. Welcome

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. Exploratory Data Analysis

49min

2.1.1. Measures of Central Tendency7 min

2.1.2. Mean and Median Example2 min

2.1.3. Practice

2.1.4. Practice2 min

2.1.5. Practice3 min

2.2.1. Skewness and Symmetry 6 min

2.2.2. Practice

2.3.1. Percentiles4 min

2.3.2. Example2 min

2.3.3. Practice

2.4.1. Range and Interquartile Range11 min

2.4.2. Variance and Standard Deviation6 min

2.4.3. Practice3 min

2.4.4. Practice3 min

2.4.5. Practice

3. Displaying & Summarizing Quantitative Data

37min

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. Frequency Polygons2 min

3.3.2. Practice4 min

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

3.4.2. Practice3 min

3.5.1. Times Series Plots2 min

3.5.2. 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. Probability

1.9hr

5.1.1. Events & Sample Spaces6 min

5.1.2. Types of Probability6 min

5.1.3. Probability Language & Notation4 min

5.1.4. Example3 min

5.1.5. Independent vs. Mutually Exclusive Events4 min

5.1.6. Example2 min

5.1.7. Practice

5.2.1. Venn Diagrams3 min

5.2.2. Tree Diagrams4 min

5.2.3. Two-way Table5 min

5.3.1. Interpretation of Probability2 min

5.3.2. Complement Rule3 min

5.3.3. Practice1 min

5.3.4. The Addition Rule4 min

5.4.1. Probability of Independent Events4 min

5.4.2. Example3 min

5.4.3. Practice2 min

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

5.5.2. Conditional Probability: Tree Diagram5 min

5.5.3. Bayes’ Theorem4 min

5.5.4. Example4 min

5.5.5. Practice3 min

5.6.1. Example6 min

5.6.2. Example7 min

5.6.3. Example7 min

5.6.4. Practice4 min

5.6.5. Practice5 min

5.7.1. Summary of Key Definitions6 min

6. Counting Techniques

29min

6.1.1. Fundamental Counting Principle6 min

6.1.2. Permutations and Combinations6 min

6.1.3. Practice1 min

6.1.4. Practice54 sec

6.1.5. More Combination Rules3 min

6.1.6. Example4 min

6.1.7. Practice3 min

6.1.8. Practice

6.2.1. Counting and Probability3 min

6.2.3. Practice1 min

7. Discrete Random Variables

36min

7.1.1. Discrete Random Variables8 min

7.1.2. Probability Distribution Function4 min

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

7.1.4. Example5 min

7.1.5. Example4 min

7.1.6. Practice

7.1.7. Practice6 min

7.1.8. Pratice4 min

7.1.9. Practice

9. Continuous Probability Distributions

1.7hr

9.1.1. Continuous Probability Distribution Theory10 min

9.2.1. Uniform Distribution1 min

9.2.2. Practice3 min

9.3.1. Exponential Experiment3 min

9.3.2. Exponential Distribution7 min

9.3.3. Practice7 min

9.4.1. Normal Distribution11 min

9.4.2. Practice5 min

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

9.5.2. Standardizing a Normal Random Variable4 min

9.5.3. Example10 min

9.5.4. Practice6 min

9.5.5. Practice3 min

9.6.1. Normal Approximation to Binomial2 min

9.6.2. Practice5 min

9.6.3. Normal Approximation to the Binomial Distribution Question4 min

9.6.4. Normal Approximation to the Binomial Distribution Question4 min

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

9.7.2. Practice2 min

10. Sampling Distribution

59min

10.1.1. Sampling Variability & Sampling Distribution12 min

10.2.1. Central Limit Theorem 5 min

10.2.2. Sampling Distribution for a Mean2 min

10.2.3. Example9 min

10.2.4. Sampling Distribution for a Mean Question5 min

10.3.1. Sampling Distribution of Proportions5 min

10.3.2. Example3 min

10.3.3. Sampling Distribution of Proportions4 min

10.4.1. Drawing Samples From a Finite Population8 min

10.4.2. Example4 min

10.4.3. Practice3 min

11. Estimating with Confidence Intervals

48min

11.1.1. Introduction to Confidence Intervals14 min

11.2.1. Confidence Interval for a Mean13 min

11.2.2. Example4 min

11.2.3. Practice3 min

11.3.1. Confidence Interval for a Proportion2 min

11.3.2. Example5 min

11.3.3. Practice4 min

11.4.1. Margin of Error and Sample Size3 min

11.4.2. Practice

12. Hypothesis Testing with One Sample

1.9hr

12.1.1. Introduction to Hypothesis Testing 15 min

12.1.2. Hypothesis Testing Steps25 min

12.2.1. Hypothesis Test for a Mean3 min

12.2.2. Example16 min

12.2.3. Example8 min

12.2.4. Practice4 min

12.3.1. Hypothesis Test for a Proportion10 min

12.3.2. Example4 min

12.3.3. Practice5 min

12.3.4. Practice9 min

12.4.1. Type I and Type II Errors5 min

12.4.2. Type I and Type II Errors1 min

12.4.3. Practice

12.4.4. Practice

12.4.5. Power7 min

12.4.6. Practice

13. Scatterplots, Association, and Correlation

37min

13.1.1. Scatterplots5 min

13.1.2. Practice

13.2.1. Correlation5 min

13.2.2. Practice

13.3.1. Solving for Correlation2 min

13.3.2. Example6 min

13.3.3. Practice

13.3.4. Practice4 min

13.3.5. Example3 min

13.4.1. Coefficient of Determination (R Squared)7 min

13.4.2. Assessing Goodness of Fit6 min

13.4.3. Practice

14. Simple Linear Regression

45min

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

14.1.2. Example7 min

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

14.1.4. Practice (Version 1)3 min

14.1.5. Practice (Version 2)5 min

14.1.6. Example5 min

14.2.1. Predictions3 min

14.2.2. Residuals (Errors)5 min

14.2.3. Practice

14.2.4. Residual Plots2 min

14.2.5. Regression Diagnostics 5 min

14.2.6. Practice

14.2.7. Practice

14.2.8. Standard Deviation of the Residuals5 min

Mock Exams

ME.1. Mock Exam20 Qns

ME.2. Term Exam 1 - Mock Assessment18 Qns

ME.3. Term Exam 2 - Mock Assessment30 Qns

ME.4. Final Exam - Mock Assessment25 Qns

I Welcome

Free Activity

Crash Course

Midterm Crash Course

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Lawron L

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Releases: Oct 7, 9:18am

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Answered

Anonymous

My midterm is in 2 weeks i've been to all my classes but when it comes to applying knowledge to answer questions is struggle do you think I should go through the midterm crash course or work through the lessons 

L

Lawron L

Instructor

Assuming you have a decent grasp of the material, you should go through the midterm crash course one week before the midterm. Go through topics that you need to review in more detail. 

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