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# Excel Statistics Essential Training: 1

## Overview

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

## Syllabus

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

Joseph Schmuller

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