Excel Statistics Essential Training: 1

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Free Online Course: Excel Statistics Essential Training: 1 provided by LinkedIn Learning is a comprehensive online course, which lasts for 3-4 hours worth of material. The course is taught in English and is free of charge. Upon completion of the course, you can receive an e-certificate from LinkedIn Learning. Excel Statistics Essential Training: 1 is taught by Joseph Schmuller.

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