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Book 3: Analyzing Data - CEdge Learn
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Book 3: Analyzing Data
Book 3: Analyzing Data
Curriculum
12 Sections
82 Lessons
10 Weeks
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Chapter 1: The Confidence Game: Estimation
4
64.65
Understanding Sampling Distributions
64.66
An EXTREMELY Important Idea: The Central Limit Theorem
64.67
Confidence: It Has Its Limits!
64.68
Fit to a t
Chapter 2: One-Sample Hypothesis Testing
11
69.70
Hypotheses, Tests, and Errors
69.71
Hypothesis Tests and Sampling Distributions
69.72
Catching Some Z’s Again
69.73
Z Testing in R
69.74
t for One
69.75
t Testing in R
69.76
Working with t-Distributions
69.77
Visualizing t-Distributions
69.78
Testing a Variance
69.79
Working with Chi-Square Distributions
69.80
Visualizing Chi-Square Distributions
Chapter 3: Two-Sample Hypothesis Testing
10
81.82
Hypotheses Built for Two
81.83
Sampling Distributions Revisited
81.84
t for Two
81.85
Like Peas in a Pod: Equal Variances
81.86
t-Testing in R
81.87
A Matched Set: Hypothesis Testing for Paired Samples
81.88
Paired Sample t-testing in R
81.89
Testing Two Variances
81.90
Working with F Distributions
81.91
Visualizing F Distributions
Chapter 4: Testing More than Two Samples
5
92.93
Testing More than Two
92.94
ANOVA in R
92.95
Another Kind of Hypothesis, Another Kind of Test
92.96
Getting Trendy
92.97
Trend Analysis in R
Chapter 5: More Complicated Testing
5
98.99
Cracking the Combinations
98.100
Two-Way ANOVA in R
98.101
Two Kinds of Variables … at Once
98.102
After the Analysis
98.103
Multivariate Analysis of Variance
Chapter 6: Regression: Linear, Multiple, and the General Linear Model
8
104.105
The Plot of Scatter
104.106
Graphing Lines
104.107
Regression: What a Line!
104.108
Linear Regression in R
104.109
Juggling Many Relationships at Once: Multiple Regression
104.110
ANOVA: Another Look
104.111
Analysis of Covariance: The Final Component of the GLM
104.112
But Wait — There’s More
Chapter 7: Correlation: The Rise and Fall of Relationships
9
113.114
Understanding Correlation
113.115
Correlation and Regression
113.116
Testing Hypotheses about Correlation
113.117
Correlation in R
113.118
Multiple Correlation
113.119
Partial Correlation
113.120
Partial Correlation in R
113.121
Semipartial Correlation
113.122
Semipartial Correlation in R
Chapter 8: Curvilinear Regression: When Relationships Get Complicated
7
123.124
What Is a Logarithm?
123.125
What Is e?
123.126
Power Regression
123.127
Exponential Regression
123.128
Logarithmic Regression
123.129
Polynomial Regression: A Higher Power
123.130
Which Model Should You Use?
Chapter 9: In Due Time
4
131.132
A Time Series and Its Components
131.133
Forecasting: A Moving Experience
131.134
Forecasting: Another Way
131.135
Working with Real Data
Chapter 10: Non-Parametric Statistics
5
136.137
Independent Samples
136.138
Matched Samples
136.139
Correlation: Spearman’s rS
136.140
Correlation: Kendall’s Tau
136.141
A Heads-Up
Chapter 11: Introducing Probability
11
142.143
What Is Probability?
142.144
Compound Events
142.145
Conditional Probability
142.146
Large Sample Spaces
142.147
R Functions for Counting Rules
142.148
Random Variables: Discrete and Continuous
142.149
Probability Distributions and Density Functions
142.150
The Binomial Distribution
142.151
The Binomial and Negative Binomial in R
142.152
Hypothesis Testing with the Binomial Distribution
142.153
More on Hypothesis Testing: R versus Tradition
Chapter 12: Probability Meets Regression: Logistic Regression
3
154.155
Getting the Data
154.156
Doing the Analysis
154.157
Visualizing the Results
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