Excel Statistics Essential Training: 1

Learn statistics. Dr. Joseph Schmuller uses Microsoft Excel to teach the fundamentals of descriptive and inferential statistics.

Introduction

• What is data?
• The big picture

1. Excel Statistics Fundamentals

• Using Excel functions
• Understanding Excel statistics functions
• Working with Excel graphics
• Installing the Excel Analysis Toolpak

2. Types of Data

• Differentiating data types
• Independent and dependent variables

3. Probability

• Defining probability
• Calculating probability
• Understanding conditional probability

4. Central Tendency

• The mean and its properties
• Working with the median
• Working with the mode

5. Variability

• Understanding variance
• Understanding standard deviation
• Z-scores

6. Distributions

• Organizing and graphing a distribution
• Graphing frequency polygons
• Properties of distributions
• Probability distributions

7. Normal Distributions

• The standard normal distribution
• Meeting the normal distribution family
• Standard normal distribution probability
• Visualizing normal distributions

8. Sampling Distributions

• Introducing sampling distributions
• Understanding the central limit theorem
• Meeting the t-distribution

9. Estimation

• Confidence in estimation
• Calculating confidence intervals

10. Hypothesis Testing

• The logic of hypothesis testing
• Type I errors and Type II errors

11. Testing Hypotheses about a Mean

• Applying the central limit theorem
• The z-test and the t-test

12. Testing Hypotheses about a Variance

• The chi-squared distribution

13. Independent Samples Hypothesis Testing

• Understanding independent samples
• Distributions for independent samples
• The z-test for independent samples
• The t-test for independent samples

14. Matched Samples Hypothesis Testing

• Understanding matched samples
• Distributions for matched samples
• The t-test for matched samples

15. Testing Hypotheses about Two Variances

• Working with the F-test

16. The Analysis of Variance

• Testing more than two parameters
• Introducing ANOVA
• Applying ANOVA

17. After the Analysis of Variance

• Types of post-ANOVA testing
• Post-ANOVA planned comparisons

18. Repeated Measures Analysis

• What is repeated measures?
• Applying repeated measures ANOVA

19. Hypothesis Testing with Two Factors

• Statistical interactions
• Two-factor ANOVA
• Performing two-factor ANOVA

20. Regression

• Understanding the regression line
• Variation around the regression line
• Analysis of variance for regression
• Multiple regression analysis

21. Correlation

• Hypothesis testing with correlation
• Understanding correlation
• The correlation coefficient
• Correlation and regression

Conclusion

• Next steps