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Learning Statistics with R - A tutorial for Psychology Students and other Beginners

Learning Statistics with R covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software. The book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. Then the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered afterward.

Applied Statistics Statistics

Tác giả: DJ Navarro
Nhà xuất bản: University of New South Wales
Nguồn: LibreTexts
Giấy phép: CC BY-SA
Ngôn ngữ: EN
Danh mục: Khoa học cơ bản

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Mục lục· 188 mục

01: Why Do We Learn Statistics
1.01: On the Psychology of Statistics
1.02: The Cautionary Tale of Simpsons Paradox
1.03: Statistics in Psychology
1.04: Statistics in Everyday Life
1.05: Theres More to Research Methods Than Statistics
02: A Brief Introduction to Research Design
2.01: Introduction to Psychological Measurement
2.02: Scales of Measurement
2.03: Assessing the Reliability of a Measurement
2.04: The Role of Variables- Predictors and Outcomes
2.05: Experimental and Non-experimental Research
2.06: Assessing the Validity of a Study
2.07: Confounds Artifacts and Other Threats to Validity
2.08: Summary
03: Getting Started with R
3.01: Installing R
3.02: Typing Commands at the R Console
3.03: Doing Simple Calculations with R
3.04: Storing a Number As a Variable
3.05: Using Functions to Do Calculations
3.06: Letting RStudio Help You with Your Commands
3.07: Storing Many Numbers As a Vector
3.08: Storing Text Data
3.09: Storing True or False Data
3.10: Indexing Vectors
3.11: Quitting R
3.12: Summary
04: Additional R Concepts
4.01: Using Comments
4.02: Installing and Loading Packages
4.03: Managing the Workspace
4.04: Navigating the File System
4.05: Loading and Saving Data
4.06: Useful Things to Know about Variables
4.07: Factors
4.08: Data frames
4.09: Lists
4.10: Formulas
4.11: Generic Functions
4.12: Getting help
4.13: Summary
05: Descriptive Statistics
5.01: Measures of Central Tendency
5.02: Measures of Variability
5.03: Skew and Kurtosis
5.04: Getting an Overall Summary of a Variable
5.05: Descriptive Statistics Separately for each Group
5.06: Standard Scores
5.07: Correlations
5.08: Handling Missing Values
5.09: Summary
5.10: Epilogue- Good Descriptive Statistics Are Descriptive
06: Drawing Graphs
6.01: An Overview of R Graphics
6.02: An Introduction to Plotting
6.03: Histograms
6.04: Stem and Leaf Plots
6.05: Boxplots
6.06: Scatterplots
6.07: Bar Graphs
6.08: Saving Image Files Using R and Rstudio
6.09: Summary
07: Pragmatic Matters
7.01: Tabulating and Cross-tabulating Data
7.02: Transforming and Recoding a Variable
7.03: A few More Mathematical Functions and Operations
7.04: Extracting a Subset of a Vector
7.05: Extracting a Subset of a Data Frame
7.06: Sorting Flipping and Merging Data
7.07: Reshaping a Data Frame
7.08: Working with Text
7.09: Reading Unusual Data Files
7.10: Coercing Data from One Class to Another
7.11: Other Useful Data Structures
7.12: Miscellaneous Topics
7.13: Summary
08: Basic Programming
8.01: Scripts
8.02: Loops
8.03: Conditional Statements
8.04: Writing Functions
8.05: Implicit Loops
8.06: Summary
09: Introduction to Probability
9.01: How are Probability and Statistics Different
9.02: What Does Probability Mean
9.03: Basic Probability Theory
9.04: The Binomial Distribution
9.05: The Normal Distribution
9.06: Other Useful Distributions
9.07: Summary
10: Estimating Unknown Quantities from a Sample
10.01: Samples Populations and Sampling
10.02: The Law of Large Numbers
10.03: Sampling Distributions and the Central Limit Theorem
10.04: Estimating Population Parameters
10.05: Estimating a Confidence Interval
10.06: Summary
11: Hypothesis Testing
11.01: A Menagerie of Hypotheses
11.02: Two Types of Errors
11.03: Test Statistics and Sampling Distributions
11.04: Making Decisions
11.05: The p value of a test
11.06: Reporting the Results of a Hypothesis Test
11.07: Running the Hypothesis Test in Practice
11.08: Effect Size Sample Size and Power
11.09: Some Issues to Consider
11.10: Summary
12: Categorical Data Analysis
12.01: The 2 Goodness-of-fit Test
12.02: The 2 test of independence (or association)
12.03: The Continuity Correction
12.04: Effect Size
12.05: Assumptions of the Test(s)
12.06: The Most Typical Way to Do Chi-square Tests in R
12.07: The Fisher Exact Test
12.08: The McNemar Test
12.09: Whats the Difference Between McNemar and Independence
12.10: Summary
13: Comparing Two Means
13.01: The one-sample z-test
13.02: The One-sample t-test
13.03: The Independent Samples t-test (Student Test)
13.04: The Independent Samples t-test (Welch Test)
13.05: The Paired-samples t-test
13.06: One Sided Tests
13.07: Using the t.test() Function
13.08: Effect Size
13.09: Checking the Normality of a Sample
13.10: Testing Non-normal Data with Wilcoxon Tests
13.11: Summary
14: Comparing Several Means (One-way ANOVA)
14.01: Summary
14.02: An Illustrative Data Set
14.03: How ANOVA Works
14.04: Running an ANOVA in R
14.05: Effect Size
14.06: Multiple Comparisons and Post Hoc Tests
14.07: Assumptions of One-way ANOVA
14.08: Checking the Homogeneity of Variance Assumption
14.09: Removing the Homogeneity of Variance Assumption
14.10: Checking the Normality Assumption
14.11: Removing the Normality Assumption
14.12: On the Relationship Between ANOVA and the Student t Test
15: Linear Regression
15.01: What Is a Linear Regression Model
15.02: Estimating a Linear Regression Model
15.03: Multiple Linear Regression
15.04: Quantifying the Fit of the Regression Model
15.05: Hypothesis Tests for Regression Models
15.06: Testing the Significance of a Correlation
15.07: Regarding Regression Coefficients
15.08: Assumptions of Regression
15.09: Model Checking
15.10: Model Selection
15.11: Summary
16: Factorial ANOVA
16.01: Factorial ANOVA 1- Balanced Designs No Interactions
16.02: Factorial ANOVA 2- Balanced Designs Interactions Allowed
16.03: Effect Size Estimated Means and Confidence Intervals
16.04: Assumption Checking
16.05: The F test as a model comparison
16.06: ANOVA As a Linear Model
16.07: Different Ways to Specify Contrasts
16.08: Post Hoc Tests
16.09: The Method of Planned Comparisons
16.10: Factorial ANOVA 3- Unbalanced Designs
16.11: Summary
17: Bayesian Statistics
17.01: Probabilistic Reasoning by Rational Agents
17.02: Bayesian Hypothesis Tests
17.03: Why Be a Bayesian
17.04: Evidentiary Standards You Can Believe
17.05: The p-value Is a Lie.
17.06: Bayesian Analysis of Contingency Tables
17.07: Bayesian t-tests
17.08: Bayesian Regression
17.09: Bayesian ANOVA
17.10: Summary
18: Epilogue
18.01: The Undiscovered Statistics
18.02: Statistical Models Missing from the Book
18.03: Learning the Basics and Learning Them in R
18.04: Other Ways of Doing Inference
18.05: Section 5-
18.06: Section 6-