DATASCI 1000

UWO

Course Overview

Grade Boosters

Lessons & Practice

I. Welcome

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 Categorical Data

12min

2.1.1. Pie Chart3 min

2.1.2. Practice

2.2.1. Bar Chart3 min

2.2.2. Practice

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

2.3.2. Practice4 min

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. 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. The Normal Distribution

51min

5.1.1. Normal Distribution11 min

5.1.2. Practice5 min

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

5.2.2. Standardizing a Normal Random Variable4 min

5.2.3. Example10 min

5.2.4. Practice6 min

5.2.5. Practice3 min

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

6.3.2. Example6 min

6.3.3. Practice4 min

6.3.4. Example3 min

6.3.5. Practice

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. Two-way Contingency Tables

7min

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

8.1.2. Simpson’s Paradox3 min

8.1.3. Practice

9. Collecting Data & Sampling

26min

9.1.1. Statistical Sampling28 sec

9.1.2. Convenience Sampling1 min

9.1.3. Simple Random Sampling3 min

9.1.4. Systematic Sampling2 min

9.1.5. Stratified Sampling vs. Cluster Sampling6 min

9.1.6. Two-Stage Cluster Sampling2 min

9.1.7. Practice

9.1.8. Practice

9.1.9. Practice

9.2.1. Sampling Bias7 min

9.2.2. Recipe for a Good Sample5 min

9.2.3. Practice

9.2.4. Practice

10. Experiments and Observational Studies

23min

10.1.1. Observational Studies4 min

10.1.2. Experimental Studies3 min

10.1.3. Example5 min

10.1.4. Practice

10.2.1. Types of Experimental Designs7 min

10.2.2. Principles of Experimental Design4 min

10.2.3. Example

10.2.4. Practice

10.2.5. Practice

11. Probability

1.9hr

11.1.1. Events & Sample Spaces6 min

11.1.2. Types of Probability6 min

11.1.3. Probability Language & Notation4 min

11.1.4. Example3 min

11.1.5. Independent vs. Mutually Exclusive Events4 min

11.1.6. Example2 min

11.1.7. Practice

11.2.1. Venn Diagrams3 min

11.2.2. Tree Diagrams4 min

11.2.3. Two-way Table5 min

11.3.1. Interpretation of Probability2 min

11.3.2. Complement Rule3 min

11.3.3. Practice1 min

11.3.4. The Addition Rule4 min

11.4.1. Probability of Independent Events4 min

11.4.2. Example3 min

11.4.3. Practice2 min

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

11.5.2. Conditional Probability: Tree Diagram5 min

11.5.3. Bayes’ Theorem4 min

11.5.4. Example4 min

11.5.5. Practice3 min

11.6.1. Example6 min

11.6.2. Example7 min

11.6.3. Example7 min

11.6.4. Practice4 min

11.6.5. Practice5 min

11.7.1. Summary of Key Definitions6 min

12. Sampling Distribution

59min

12.1.1. Sampling Variability & Sampling Distribution12 min

12.2.1. Central Limit Theorem 5 min

12.2.2. Sampling Distribution for a Mean2 min

12.2.3. Example9 min

12.2.4. Sampling Distribution for a Mean Question5 min

12.3.1. Sampling Distribution of Proportions5 min

12.3.2. Example3 min

12.3.3. Practice4 min

12.4.1. Drawing Samples From a Finite Population8 min

12.4.2. Example4 min

12.4.3. Practice3 min

13. Estimating with Confidence Intervals

48min

13.1.1. Introduction to Confidence Intervals14 min

13.2.1. Confidence Interval for a Mean13 min

13.2.2. Example4 min

13.2.3. Practice3 min

13.3.1. Confidence Interval for a Proportion2 min

13.3.2. Example5 min

13.3.3. Practice4 min

13.4.1. Margin of Error and Sample Size3 min

13.4.2. Practice

Mock Exams

ME.1. Mock Exam21 Qns

ME.2. Mock Exam18 Qns

ME.3. Mock Exam27 Qns

ME.4. Midterm - Mock Assessment36 Qns

ME.5. Final Exam - Mock Assessment43 Qns

DATASCI 1000 Grade Boosters

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Final Mock Exam

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Midterm Test Mock

A realistic practice exam with solution breakdowns.

D

4.8

Booklet Solutions Recording

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