Lynda - Statistics with Excel Part One.2016 TUTORiAL

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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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