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LinkedIn Learning

Excel Statistics Essential Training: 1

via LinkedIn Learning

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

Taught by

Joseph Schmuller

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