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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