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Computers
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Analyzing Data
Analyzing Data
Curriculum
12 Sections
82 Lessons
10 Weeks
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Chapter 1: The Confidence Game: Estimation
4
1.1
Understanding Sampling Distributions
1.2
An EXTREMELY Important Idea: The Central Limit Theorem
1.3
Confidence: It Has Its Limits!
1.4
Fit to a t
Chapter 2: One-Sample Hypothesis Testing
11
2.1
Hypotheses, Tests, and Errors
2.2
Hypothesis Tests and Sampling Distributions
2.3
Catching Some Z’s Again
2.4
Z Testing in R
2.5
t for One
2.6
t Testing in R
2.7
Working with t-Distributions
2.8
Visualizing t-Distributions
2.9
Testing a Variance
2.10
Working with Chi-Square Distributions
2.11
Visualizing Chi-Square Distributions
Chapter 3: Two-Sample Hypothesis Testing
10
3.1
Hypotheses Built for Two
3.2
Sampling Distributions Revisited
3.3
t for Two
3.4
Like Peas in a Pod: Equal Variances
3.5
t-Testing in R
3.6
A Matched Set: Hypothesis Testing for Paired Samples
3.7
Paired Sample t-testing in R
3.8
Testing Two Variances
3.9
Working with F Distributions
3.10
Visualizing F Distributions
Chapter 4: Testing More than Two Samples
5
4.1
Testing More than Two
4.2
ANOVA in R
4.3
Another Kind of Hypothesis, Another Kind of Test
4.4
Getting Trendy
4.5
Trend Analysis in R
Chapter 5: More Complicated Testing
5
5.1
Cracking the Combinations
5.2
Two-Way ANOVA in R
5.3
Two Kinds of Variables … at Once
5.4
After the Analysis
5.5
Multivariate Analysis of Variance
Chapter 6: Regression: Linear, Multiple, and the General Linear Model
8
6.1
The Plot of Scatter
6.2
Graphing Lines
6.3
Regression: What a Line!
6.4
Linear Regression in R
6.5
Juggling Many Relationships at Once: Multiple Regression
6.6
ANOVA: Another Look
6.7
Analysis of Covariance: The Final Component of the GLM
6.8
But Wait — There’s More
Chapter 7: Correlation: The Rise and Fall of Relationships
9
7.1
Understanding Correlation
7.2
Correlation and Regression
7.3
Testing Hypotheses about Correlation
7.4
Correlation in R
7.5
Multiple Correlation
7.6
Partial Correlation
7.7
Partial Correlation in R
7.8
Semipartial Correlation
7.9
Semipartial Correlation in R
Chapter 8: Curvilinear Regression: When Relationships Get Complicated
7
8.1
What Is a Logarithm?
8.2
What Is e?
8.3
Power Regression
8.4
Exponential Regression
8.5
Logarithmic Regression
8.6
Polynomial Regression: A Higher Power
8.7
Which Model Should You Use?
Chapter 9: In Due Time
4
9.1
A Time Series and Its Components
9.2
Forecasting: A Moving Experience
9.3
Forecasting: Another Way
9.4
Working with Real Data
Chapter 10: Non-Parametric Statistics
5
10.1
Independent Samples
10.2
Matched Samples
10.3
Correlation: Spearman’s rS
10.4
Correlation: Kendall’s Tau
10.5
A Heads-Up
Chapter 11: Introducing Probability
11
11.1
What Is Probability?
11.2
Compound Events
11.3
Conditional Probability
11.4
Large Sample Spaces
11.5
R Functions for Counting Rules
11.6
Random Variables: Discrete and Continuous
11.7
Probability Distributions and Density Functions
11.8
The Binomial Distribution
11.9
The Binomial and Negative Binomial in R
11.10
Hypothesis Testing with the Binomial Distribution
11.11
More on Hypothesis Testing: R versus Tradition
Chapter 12: Probability Meets Regression: Logistic Regression
3
12.1
Getting the Data
12.2
Doing the Analysis
12.3
Visualizing the Results
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