MAT 2377

uOttawa

Course Overview

Lessons & Practice

I. Welcome

1 min

1. Introduction to Statistics and Probability

1.4hr

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. Measures of Central Tendency7 min

1.4.2. Mean and Median Example2 min

1.4.3. Practice

1.4.4. Practice2 min

1.4.5. Practice3 min

1.5.1. Skewness and Symmetry 6 min

1.5.2. Practice

1.6.1. Range and Interquartile Range11 min

1.6.2. Variance and Standard Deviation6 min

1.6.3. Practice3 min

1.6.4. Practice3 min

1.6.5. Practice

2. Random Variables, Distributions, and Expectations

1.9hr

2.1.1. Discrete Random Variables8 min

2.1.2. Practice

2.1.3. Probability Distribution Function4 min

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

2.2.1. Transforming Data: Shifting & Scaling11 min

2.2.2. Practice2 min

2.2.3. Linear Transformation3 min

2.2.4. Example4 min

2.2.5. Practice2 min

2.3.1. Cumulative Distribution Function (CDF)3 min

2.3.2. Example6 min

2.3.3. Practice2 min

2.4.1. Combining Random Variables7 min

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

2.4.7. Example3 min

2.4.8. Practice

2.4.9. Practice2 min

2.5.1. Conditional Probability & Joint Distribution7 min

2.5.2. Example5 min

2.5.3. Practice

2.5.4. Random Variables & Joint Distribution7 min

3. Some Probability Distributions

3hr

3.1.1. Bernoulli Trials4 min

3.1.2. Practice4 min

3.1.3. Binomial Experiment4 min

3.1.4. Binomial Distribution8 min

3.2.1. Hypergeometric Experiment5 min

3.2.2. Hypergeometric Distribution9 min

3.2.3. Example4 min

3.2.4. Practice4 min

3.2.5. Practice Question

3.3.1. Poisson Experiment5 min

3.3.2. Poisson Distribution1 min

3.3.3. Example3 min

3.3.4. Practice5 min

3.3.5. Practice5 min

3.4.1. Approximating the Binomial Distribution with Poisson2 min

3.4.2. Example6 min

3.4.3. Practice

3.5.1. Continuous Probability Distribution Theory10 min

3.6.1. Uniform Distribution1 min

3.6.2. Practice3 min

3.7.1. Normal Distribution11 min

3.7.2. Practice5 min

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

3.8.2. Standardizing a Normal Random Variable4 min

3.8.3. Example10 min

3.8.4. Practice6 min

3.8.5. Practice3 min

3.9.1. Normal Approximation to Binomial2 min

3.9.2. Practice5 min

3.9.3. Practice4 min

3.9.4. Practice4 min

3.10.1. Exponential Experiment3 min

3.10.2. Exponential Distribution7 min

3.10.3. Practice7 min

4. Sampling Distributions and Data Descriptions

59min

4.1.1. Sampling Variability & Sampling Distribution12 min

4.2.1. Central Limit Theorem 5 min

4.2.2. Sampling Distribution for a Mean2 min

4.2.3. Example9 min

4.2.4. Sampling Distribution for a Mean Question5 min

4.3.1. Sampling Distribution of Proportions5 min

4.3.2. Example3 min

4.3.3. Practice4 min

4.4.1. Drawing Samples From a Finite Population8 min

4.4.2. Example4 min

4.4.3. Practice3 min

4.5.1. Central Limit Theorem Theory

5. One and Two Sample Estimation Problems

1.9hr

5.1.1. Introduction to Confidence Intervals14 min

5.2.1. Confidence Interval for a Mean13 min

5.2.2. Example4 min

5.2.3. Practice3 min

5.3.1. Confidence Interval for a Proportion2 min

5.3.2. Example5 min

5.3.3. Practice4 min

5.4.1. Margin of Error and Sample Size3 min

5.4.2. Practice

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

5.5.2. Confidence Interval for Two Means Containing Zero7 min

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

5.6.1. Confidence Interval Unpooled t2 min

5.6.2. Example: Confidence Interval (Unpooled)5 min

5.6.3. Practice (Unpooled)

5.7.1. Confidence Interval Pooled t2 min

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

5.7.3. Practice (Pooled)

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

5.8.2. Practice

5.9.1. Confidence Interval for Differences in Population Proportions3 min

5.9.2. Confidence Interval for Two Proportions1 min

5.9.3. Example4 min

5.9.4. Practice4 min

5.10.1. Confidence Interval for Population Variance5 min

5.10.2. Example6 min

5.10.3. Pratice3 min

5.10.4. Practice

6. One and Two Sample Tests of Hypotheses

5hr

6.1.1. Introduction to Hypothesis Testing 15 min

6.1.2. Hypothesis Testing Steps25 min

6.2.1. Hypothesis Test for a Mean3 min

6.2.2. Example16 min

6.2.3. Example8 min

6.2.4. Practice4 min

6.3.1. Hypothesis Test for a Proportion10 min

6.3.2. Example4 min

6.3.3. Practice5 min

6.3.4. Practice9 min

6.4.1. Type I and Type II Errors5 min

6.4.2. Type I and Type II Errors1 min

6.5.1. Comparing Two Independent Means9 min

6.5.2. Hypothesis Test Steps: Comparing Two Independent Means11 min

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

6.6.2. Example (Unpooled)11 min

6.6.3. Practice (Unpooled)5 min

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

6.7.2. Example (Pooled)7 min

6.7.3. Practice (Pooled)7 min

6.8.1. Match Pairs for Dependent Means12 min

6.9.1. Hypothesis Test for Matched Pairs11 min

6.9.2. Example7 min

6.9.3. Practice3 min

6.10.1. Hypothesis Testing for Two Independent Population Proportions9 min

6.10.2. Hypothesis Test Steps: Comparing Two Independent Proportions8 min

6.10.3. Example13 min

6.10.4. Practice (Pooled)6 min

6.11.1. Inference for Population Variance & Chi-square Distribution 8 min

6.12.1. Hypothesis Test for Population Variance8 min

6.12.2. Example8 min

6.12.3. Hypothesis Test for Population Variance

6.12.4. Practice4 min

6.13.1. Test of Equality of Two Variances7 min

6.14.1. F-Test for Equality of Two Variances11 min

6.14.2. Example6 min

6.14.3. F-Test for Equality of Two Variances

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

6.15.2. Example7 min

6.15.3. Practice

7. Linear Regression

4hr

7.1.1. Scatterplots5 min

7.1.2. Practice

7.2.1. Correlation5 min

7.2.2. Practice

7.3.1. Solving for Correlation2 min

7.4.1. Coefficient of Determination (R Squared)7 min

7.4.2. Assessing Goodness of Fit6 min

7.4.3. Practice

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

7.5.2. Example7 min

7.5.3. Practice (Version 1)3 min

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

7.5.5. Example5 min

7.5.6. Practice (Version 2)5 min

7.6.1. Predictions3 min

7.6.2. Residuals (Errors)5 min

7.6.3. Practice

7.6.4. Residual Plots2 min

7.6.5. Regression Diagnostics 5 min

7.6.6. Practice

7.6.7. Standard Deviation of the Residuals5 min

7.6.8. Practice

7.7.1. Estimating the Coefficients of the Linear Regression Model9 min

7.7.2. Measures of Variation in Regression9 min

7.7.3. Practice

7.8.1. Standard Error and R Square Formulas7 min

7.8.2. Example5 min

7.8.3. Practice4 min

7.9.1. Confidence Interval for Linear Regression5 min

7.9.2. Example9 min

7.9.3. Practice3 min

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

7.9.5. Practice

7.10.1. Hypothesis Testing for Linear Regression14 min

7.10.2. Example17 min

7.10.3. Example13 min

7.10.4. Practice: Linear Regression Analysis6 min

7.11.1. Prediction Interval of Y7 min

7.11.2. Example12 min

7.11.3. Practice10 min

7.11.4. Practice

8. One-Factor Experiments: General

38min

8.1.1. One-Way ANOVA8 min

8.1.2. One-Way ANOVA Table10 min

8.1.3. Example9 min

8.1.4. Practice

8.2.1. One-Way ANOVA Hypothesis Test6 min

8.2.2. Example4 min

8.2.3. Practice

Mock Exams

ME.1. Mock Exam21 Qns

ME.2. Mock Exam18 Qns

ME.3. Mock Exam27 Qns

ME.4. Midterm 2 - Mock Assessment34 Qns

ME.5. Midterm 1 - Mock Assessment27 Qns

ME.6. Final Exam - Mock Assessment44 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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