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Introduction to Statistics with R

Statistics

Author: Edward Chi
Publisher: Cerritos College
Source: LibreTexts
Language: EN
Category: Basic Sciences

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Contents· 253 sections

01: Basics
1.01: Introduction
1.1.01: What Is Statistical Thinking
1.1.02: Dealing with Statistics Anxiety
1.1.03: What Can Statistics Do for Us
1.1.04: The Big Ideas of Statistics
1.1.05: Causality and Statistics
1.02: Working with Data
1.2.01: What Are Data
1.2.02: Data Basics
1.2.03: Scales of Measurement
1.2.04: What Makes a Good Measurement
1.2.05: Overview of Data Collection Principles
1.2.06: Observational Studies and Sampling Strategies
1.2.07: Experiments
1.2.08: How Not to Do Statistics
1.2.09: Exercises
02: Introduction to R
2.01: Why Programming Is Hard to Learn
2.02: Using RStudio
2.03: Installing R
2.04: Getting Started with R
2.05: Variables
2.06: Functions
2.07: Letting RStudio Help You with Your Commands
2.08: Vectors
2.09: Math with Vectors
2.10: Data Frames
2.11: Using R Libraries
2.12: Installing and Loading Packages
2.13: Using Comments
2.14: Navigating the File System
2.15: Loading and Saving Data
2.16: Useful Things to Know about Variables
2.17: Factors
2.18: Data frames
2.19: Suggested Readings and Videos
03: Summarizing Data Visually
3.01: Qualitative Data
3.02: Quantitative Data
3.03: Other Graphical Representations of Data
3.04: Statistical Literacy
04: Summarizing Data Visually Using R
4.01: An Overview of R Graphics
4.02: An Introduction to Plotting
4.03: Histograms
4.04: Stem and Leaf Plots
4.05: Scatterplots
4.06: Bar Graphs
4.07: Saving Image Files Using R and Rstudio
4.08: Summary
05: Summarizing Data With Numbers
5.01: Central Tendency
5.02: What is Central Tendency
5.03: Measures of Central Tendency
5.04: Median and Mean
5.05: Measures of the Location of the Data
5.06: Additional Measures
5.07: Comparing Measures
5.08: Variability
5.09: Measures of Variability
5.10: Shapes of Distributions
5.11: Effects of Linear Transformations
5.12: Variance Sum Law I - Uncorrelated Variables
5.13: Statistical Literacy
5.14: Case Study- Using Stents to Prevent Strokes
5.15: Measures of the Location of the Data (Exercises)
5.E: Summarizing Distributions (Exercises)
06: Describing Data With Numbers Using R
6.01: Measures of Central Tendency
6.02: Measures of Variability
6.03: Skew and Kurtosis
6.04: Getting an Overall Summary of a Variable
6.05: Descriptive Statistics Separately for each Group
6.06: Standard Scores
6.07: Epilogue- Good Descriptive Statistics Are Descriptive
07: Introduction to Probability
7.01: How are Probability and Statistics Different
7.02: What Does Probability Mean
7.03: Basic Probability Theory
7.04: The Binomial Distribution
7.05: The Normal Distribution
7.06: Other Useful Distributions
7.07: Summary
7.08: Statistical Literacy
7.E: Probability (Exercises)
08: Estimating Unknown Quantities from a Sample
8.01: Samples Populations and Sampling
8.02: The Law of Large Numbers
8.03: Sampling Distributions and the Central Limit Theorem
8.04: Estimating Population Parameters
8.05: Estimating a Confidence Interval
8.06: Summary
8.07: Statistical Literacy
8.E: Estimation (Exercises)
09: Hypothesis Testing
9.01: A Menagerie of Hypotheses
9.02: Two Types of Errors
9.03: Test Statistics and Sampling Distributions
9.04: Making Decisions
9.05: The p value of a test
9.06: Reporting the Results of a Hypothesis Test
9.07: Running the Hypothesis Test in Practice
9.08: Effect Size Sample Size and Power
9.09: Some Issues to Consider
9.10: Misconceptions of Hypothesis Testing
9.11: Summary
9.12: Statistical Literacy
9.13: Logic of Hypothesis Testing (Exercises)
10: Categorical Data Analysis
10.01: The 2 Goodness-of-fit Test
10.02: The 2 test of independence (or association)
10.03: The Continuity Correction
10.04: Effect Size
10.05: Assumptions of the Test(s)
10.06: The Most Typical Way to Do Chi-square Tests in R
10.07: The Fisher Exact Test
10.08: The McNemar Test
10.09: Whats the Difference Between McNemar and Independence
10.10: Summary
10.11: Statistical Literacy
10.12: Chi Square (Exercises)
11: Comparing Two Means
11.01: The one-sample z-test
11.02: The One-sample t-test
11.03: The Independent Samples t-test (Student Test)
11.04: The Independent Samples t-test (Welch Test)
11.05: The Paired-samples t-test
11.06: One Sided Tests
11.07: Using the t.test() Function
11.08: Effect Size
11.09: Checking the Normality of a Sample
11.10: Testing Non-normal Data with Wilcoxon Tests
11.11: Summary
11.12: Statistical Literacy
11.E: Tests of Means (Exercises)
12: Comparing Several Means (One-way ANOVA)
12.01: Summary
12.02: An Illustrative Data Set
12.03: How ANOVA Works
12.04: Running an ANOVA in R
12.05: Effect Size
12.06: Multiple Comparisons and Post Hoc Tests
12.07: Assumptions of One-way ANOVA
12.08: Checking the Homogeneity of Variance Assumption
12.09: Removing the Homogeneity of Variance Assumption
12.10: Checking the Normality Assumption
12.11: Removing the Normality Assumption
12.12: On the Relationship Between ANOVA and the Student t Test
13: Introduction to Linear Regression
13.01: Prelude to Linear Regression
13.02: Line Fitting Residuals and Correlation
13.03: Fitting a Line by Least Squares Regression
13.04: Types of Outliers in Linear Regression
13.05: Inference for Linear Regression
13.06: Exercises
14: Multiple and Logistic Regression
14.01: Introduction to Multiple Regression
14.02: Model Selection
14.03: Checking Model Assumptions using Graphs
14.04: Introduction to Logistic Regression
14.05: Exercises
14.06: Statistical Literacy
14.E: Regression (Exercises)
15: Regression in R
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: Correlations
15.07: Handling Missing Values
15.08: Testing the Significance of a Correlation
15.09: Regarding Regression Coefficients
15.10: Assumptions of Regression
15.11: Model Checking
15.12: Model Selection
15.13: Summary
16: Research Design
16.01: Scientific Method
16.02: Measurement
16.03: Data Collection
16.04: Sampling Bias
16.05: Experimental Designs
16.06: Causation
16.07: Statistical Literacy
16.E: Research Design (Exercises)
17: Preparing Datasets and Other Pragmatic Matters
17.01: Tabulating and Cross-tabulating Data
17.02: Transforming and Recoding a Variable
17.03: A few More Mathematical Functions and Operations
17.04: Extracting a Subset of a Vector
17.05: Extracting a Subset of a Data Frame
17.06: Sorting Flipping and Merging Data
17.07: Reshaping a Data Frame
17.08: Working with Text
17.09: Reading Unusual Data Files
17.10: Coercing Data from One Class to Another
17.11: Other Useful Data Structures
17.12: Miscellaneous Topics
17.13: Summary
18: Basic Programming
18.01: Scripts
18.02: Loops
18.03: Conditional Statements
18.04: Writing Functions
18.05: Implicit Loops
18.06: Summary
19: Bayesian Statistics
19.01: Probabilistic Reasoning by Rational Agents
19.02: Bayesian Hypothesis Tests
19.03: Why Be a Bayesian
19.04: Evidentiary Standards You Can Believe
19.05: The p-value Is a Lie.
19.06: Bayesian Analysis of Contingency Tables
19.07: Bayesian t-tests
19.08: Bayesian Regression
19.09: Bayesian ANOVA
19.10: Summary
20: Case Studies and Data
20.01: Angry Moods
20.02: Flatulence
20.03: Physicians Reactions
20.04: Teacher Ratings
20.05: Diet and Health
20.06: Smiles and Leniency
20.07: Animal Research
20.08: ADHD Treatment
20.09: Weapons and Aggression
20.10: SAT and College GPA
20.11: Stereograms
20.12: Driving
20.13: Stroop Interference
20.14: TV Violence
20.15: Obesity and Bias
20.16: Shaking and Stirring Martinis
20.17: Adolescent Lifestyle Choices
20.18: Chocolate and Body Weight
20.19: Bedroom TV and Hispanic Children
20.20: Weight and Sleep Apnea
20.21: Misusing SEM
20.22: School Gardens and Vegetable Consumption
20.23: TV and Hypertension
20.24: Dietary Supplements
20.25: Young People and Binge Drinking
20.26: Sugar Consumption in the US Diet
20.27: Nutrition Information Sources and Older Adults
20.28: Mind Set - Exercise and the Placebo Effect
20.29: Predicting Present and Future Affect
20.30: Exercise and Memory
20.31: Parental Recognition of Child Obesity
20.32: Educational Attainment and Racial Ethnic and Gender Disparity
21: Math Review for Introductory Statistics