ECON 221

Concordia

Course Overview

Lessons & Practice

I. Welcome

1. Using Graphs to Describe Data

1.6hr

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. Histograms4 min

1.4.2. Practice2 min

1.4.3. Practice2 min

1.4.4. Practice

1.5.1. Dot Plots5 min

1.5.2. Practice

1.6.1. Frequency Polygons2 min

1.6.2. Practice4 min

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

1.7.2. Practice3 min

1.8.1. Times Series Plots2 min

1.8.2. Practice2 min

1.9.1. Scatterplots5 min

1.9.2. Practice

1.10.1. Pie Chart3 min

1.10.2. Practice

1.11.1. Bar Chart3 min

1.11.2. Practice

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

1.12.2. Practice4 min

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

1.13.2. Simpson’s Paradox3 min

1.13.3. Practice

2. Using Numerical Measures to Describe Data

1hr

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. Boxplots and Five Number Summary9 min

2.3.2. Practice4 min

2.3.3. Practice

2.4.1. Percentiles4 min

2.4.2. Example2 min

2.4.3. Practice

2.5.1. Range and Interquartile Range11 min

2.5.2. Variance and Standard Deviation6 min

2.5.3. Practice3 min

2.5.4. Practice3 min

2.5.5. Practice

3. Elements of Chance: Probability Methods

1.9hr

3.1.1. Events & Sample Spaces6 min

3.1.2. Types of Probability6 min

3.1.3. Probability Language & Notation4 min

3.1.4. Example3 min

3.1.5. Independent vs. Mutually Exclusive Events4 min

3.1.6. Example2 min

3.1.7. Practice

3.2.1. Venn Diagrams3 min

3.2.2. Tree Diagrams4 min

3.2.3. Two-way Table5 min

3.3.1. Interpretation of Probability2 min

3.3.2. Complement Rule3 min

3.3.3. Practice1 min

3.3.4. The Addition Rule4 min

3.4.1. Probability of Independent Events4 min

3.4.2. Example3 min

3.4.3. Practice2 min

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

3.5.2. Conditional Probability: Tree Diagram5 min

3.5.3. Bayes’ Theorem4 min

3.5.4. Example4 min

3.5.5. Practice3 min

3.6.1. Example6 min

3.6.2. Example7 min

3.6.3. Example7 min

3.6.4. Practice4 min

3.6.5. Practice5 min

3.7.1. Summary of Key Definitions6 min

4. Discrete Probability Distributions

3hr

4.1.1. Discrete Random Variables8 min

4.1.2. Practice

4.1.3. Probability Distribution Function4 min

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

4.1.5. Example5 min

4.1.6. Example4 min

4.1.7. Practice6 min

4.1.8. Practice4 min

4.1.9. Practice

4.2.1. Transforming Data: Shifting & Scaling11 min

4.2.2. Practice2 min

4.2.3. Linear Transformation3 min

4.2.4. Example4 min

4.2.5. Practice2 min

4.3.1. Cumulative Distribution Function (CDF)3 min

4.3.2. Example6 min

4.3.3. Practice2 min

4.4.1. Combining Random Variables7 min

4.4.2. Example2 min

4.4.3. Practice2 min

4.4.4. Example3 min

4.4.5. Practice

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

4.4.7. Example3 min

4.4.8. Practice

4.4.9. Practice2 min

4.5.1. Bernoulli Trials4 min

4.5.2. Practice4 min

4.5.3. Binomial Experiment4 min

4.5.4. Binomial Distribution8 min

4.5.5. Example10 min

4.5.6. Example1 min

4.5.7. Practice6 min

4.5.8. Practice

4.6.1. Poisson Experiment5 min

4.6.2. Poisson Distribution1 min

4.6.3. Example3 min

4.6.4. Practice5 min

4.6.5. Practice5 min

4.7.1. Approximating the Binomial Distribution with Poisson2 min

4.7.2. Example6 min

4.7.3. Practice

4.8.1. Hypergeometric Experiment5 min

4.8.2. Hypergeometric Distribution9 min

4.8.3. Example4 min

4.8.4. Practice4 min

4.8.5. Practice Question

4.9.1. Conditional Probability & Joint Distribution7 min

4.9.2. Example5 min

4.9.3. Practice

4.9.4. Random Variables & Joint Distribution7 min

4.9.5. Example3 min

4.9.6. Practice

4.9.8. Practice

5. Continuous Probability Distributions

1.7hr

5.1.1. Continuous Probability Distribution Theory10 min

5.2.1. Uniform Distribution1 min

5.2.2. Practice3 min

5.3.1. Exponential Experiment3 min

5.3.2. Exponential Distribution7 min

5.3.3. Practice7 min

5.4.1. Normal Distribution11 min

5.4.2. Practice5 min

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

5.5.2. Standardizing a Normal Random Variable4 min

5.5.3. Example10 min

5.5.4. Practice6 min

5.5.5. Practice3 min

5.6.1. Normal Approximation to Binomial2 min

5.6.2. Practice5 min

5.6.3. Practice4 min

5.6.4. Practice4 min

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

5.7.2. Practice2 min

6. Distributions of Sample Statistics

59min

6.1.1. Sampling Variability & Sampling Distribution12 min

6.2.1. Central Limit Theorem 5 min

6.2.2. Sampling Distribution for a Mean2 min

6.2.3. Example9 min

6.2.4. Sampling Distribution for a Mean Question5 min

6.3.1. Sampling Distribution of Proportions5 min

6.3.2. Example3 min

6.3.3. Practice4 min

6.4.1. Drawing Samples From a Finite Population8 min

6.4.2. Example4 min

6.4.3. Practice3 min

7. Confidence Interval Estimation: One Population

48min

7.1.1. Introduction to Confidence Intervals14 min

7.2.1. Confidence Interval for a Mean13 min

7.2.2. Example4 min

7.2.3. Practice3 min

7.3.1. Confidence Interval for a Proportion2 min

7.3.2. Example5 min

7.3.3. Practice4 min

7.4.1. Margin of Error and Sample Size3 min

7.4.2. Practice

8. Confidence Interval Estimation: Further Topics

1.1hr

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

8.1.2. Confidence Interval for Two Means Containing Zero7 min

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

8.2.1. Confidence Interval Unpooled t2 min

8.2.2. Example: Confidence Interval (Unpooled)5 min

8.2.3. Practice (Unpooled)

8.3.1. Confidence Interval Pooled t2 min

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

8.3.3. Practice (Pooled)

8.4.1. Match Pairs for Dependent Means12 min

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

8.5.2. Practice

8.6.1. Confidence Interval for Differences in Population Proportions3 min

8.6.2. Confidence Interval for Two Proportions1 min

8.6.3. Example4 min

8.6.4. Practice4 min

Mock Exams

ME.1. Mock Exam21 Qns

ME.2. Mock Exam18 Qns

ME.3. Mock Exam27 Qns

ME.4. Midterm 1 - Mock Assessment21 Qns

Midterm 1 Overview

Course Notes

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

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