**Excel Statistics Essential Training: 1**

English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 3h 37m | 480 MB

Data isn’t valuable until you put it to good use. Statistics transforms data into meaningful information, enabling organizations to make better decisions and predictions. That’s why statistics—collecting, analyzing, and presenting data—is a valuable skill for anyone in business or academia. In this course, Joseph Schmuller teaches the fundamentals of descriptive and inferential statistics and shows you how to apply them in Microsoft Excel—an inexpensive and accessible application that offers an array of powerful statistical tools. Using the built-in functions, and charts, along with the Analysis Toolpak add-on, Joe explains how to organize and present data, understand sampling distributions, test hypotheses, and draw conclusions. He covers probabilities, averages, variability, distribution, estimation, variance, regression testing, and more. By the end of the course, you should be able to fully understand and apply basic statistical concepts to a wide variety of data.

Topics include:

- Using Excel functions and graphics
- Data types and variables
- Calculating probability
- Mean, median, and mode
- Calculating variability
- Organizing and graphing distributions
- Visualizing normal distributions
- Sampling distributions
- Making estimations
- Testing hypotheses: mean, z- and t-testing, and more
- Analyzing variance
- Performing repeated measure testing
- Regression testing
- Hypotheses testing with correlation

**Table of Contents**

1 What is data

2 The big picture

3 Using Excel functions

4 Understanding Excel statistics functions

5 Working with Excel graphics

6 Installing the Excel Analysis Toolpak

7 Differentiating data types

8 Independent and dependent variables

9 Defining probability

10 Calculating probability

11 Understanding conditional probability

12 The mean and its properties

13 Working with the median

14 Working with the mode

15 Understanding variance

16 Understanding standard deviation

17 Z-scores

18 Organizing and graphing a distribution

19 Graphing frequency polygons

20 Properties of distributions

21 Probability distributions

22 Meeting the normal distribution family

23 The standard normal distribution

24 Standard normal distribution probability

25 Visualizing normal distributions

26 Introducing sampling distributions

27 Understanding the central limit theorem

28 Meeting the t-distribution

29 Confidence in estimation

30 Calculating confidence intervals

31 The logic of hypothesis testing

32 Type I errors and Type II errors

33 Applying the central limit theorem

34 The z-test and the t-test

35 The chi-squared distribution

36 Understanding independent samples

37 Distributions for independent samples

38 The z-test for independent samples

39 The t-test for independent samples

40 Understanding matched samples

41 Distributions for matched samples

42 The t-test for matched samples

43 Working with the F-test

44 Testing more than two parameters

45 Introducing ANOVA

46 Applying ANOVA

47 Types of post-ANOVA testing

48 Post-ANOVA planned comparisons

49 What is repeated measures

50 Applying repeated measures ANOVA

51 Statistical interactions

52 Two-factor ANOVA

53 Performing two-factor ANOVA

54 Understanding the regression line

55 Variation around the regression line

56 Analysis of variance for regression

57 Multiple regression analysis

58 Understanding correlation

59 The correlation coefficient

60 Correlation and regression

61 Hypothesis testing with correlation

62 Next steps

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