Lynda - Statistics with Excel Part One 2016 TUTORiAL | 576 MB
Understanding statistics is more important than ever. Statistical operations are the basis for decision making in fields from business to academia. However, many statistics courses are taught in cookbook fashion, with an emphasis on a bewildering array of tests, techniques, and software applications. In this course, part one of a series, Joseph Schmuller teaches the fundamental concepts of descriptive and inferential statistics and shows you how to apply them using Microsoft Excel.

He explains how to organize and present data and how to draw conclusions from data using Excel's functions, calculations, and charts, as well as the free and powerful Excel Analysis ToolPak. The objective is for the learner to fully understand and apply statistical concepts-not to just blindly use a specific statistical test for a particular type of data set. Joseph uses Excel as a teaching tool to illustrate the concepts and increase understanding, but all you need is a basic understanding of algebra to follow along.

Introduction
Welcome
Using exercise files
1. Excel Statistics Essentials
Excel functions
Excel statistical functions
Excel graphics
Excel Analysis ToolPak
2. Understanding Data
Types of data
Independent and dependent variables
3. Probability
Probability definitions
Calculating probability
Counting rules and probability
Conditional probability
Bayesian probability
4. Central Tendency
Mean and its properties
Median
Mode
5. Variability
Variance
Standard deviation
6. Distributions
What is a distribution?
Organize and graph a distribution
Graph frequency polygons
Properties of distributions
Probability distributions
7. Normal Distributions
The normal distribution family
The standard normal distribution
Standard normal distribution probability
Normal distribution graph
8. Sampling Distributions
Sampling distribution overview
Central limit theorem
Meet 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. Mean Hypothesis Testing
Applying the central limit theorem
The z-test and the t-test
12. Variance Hypothesis Testing
Chi-square distribution
13. z and t Hypothesis Testing
Understanding independent samples
Distributions for independent samples
The z-test for independent samples
The t-test for independent samples
14. Matched Sample Hypothesis Testing
Matched samples
Distributions for matched samples
The t-test for matched samples
15. F-Test Hypothesis Testing
F-test overview
16. Analysis of Variance
More than two parameters
ANOVA
Applying ANOVA
17. After the Analysis of Variance
Types of post-ANOVA testing
Post-ANOVA planned comparisons
18. Repeated Measures Testing
What is repeated measures?
Perform repeated measures ANOVA
19. Hypothesis Testing with Two Factors
Statistical interactions
Two-factor ANOVA
Perform two-factor ANOVA
20. Regression
Regression line overview
Variation around the regression line
Analysis of variance for regression
Multiple regression analysis
21. Correlation
Up next
Correlation coefficient
Correlation and regression
Hypothesis testing with correlation
Conclusion
Up next

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