Nguồn ngoài

Intermediate Statistics with R

Introductory statistics courses prepare students to think statistically but cover relatively few statistical methods. Building on the basic statistical thinking emphasized in an introductory course, a second course in statistics at the undergraduate level can explore a large number of statistical methods. This text covers more advanced graphical summaries, One-Way ANOVA with pair-wise comparisons, Two-Way ANOVA, Chi-square testing, and simple and multiple linear regression models.

Advanced Statistics Statistics

Tác giả: Mark Greenwood
Nhà xuất bản: Montana State University
Nguồn: LibreTexts
Giấy phép: CC BY-NC
Ngôn ngữ: EN
Danh mục: Khoa học cơ bản

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01: Preface
1.01: Overview of methods
1.02: Getting started in R
1.03: Basic summary statistics histograms and boxplots using R
1.04: R Markdown
1.05: Grammar of Graphics
1.06: Exiting RStudio
1.07: Chapter summary
1.08: Summary of important R code
1.09: Practice problems
02: (R)e-Introduction to statistics
2.01: Data wrangling and density curves
2.02: Pirate-plots
2.03: Models hypotheses and permutations for the two sample mean situation
2.04: Permutation testing for the two sample mean situation
2.05: Hypothesis testing (general)
2.06: Connecting randomization (nonparametric) and parametric tests
2.07: Second example of permutation tests
2.08: Reproducibility Crisis - Moving beyond p 0.05 publication bias and multiple testing issues
2.09: Confidence intervals and bootstrapping
2.10: Bootstrap confidence intervals for difference in GPAs
2.11: Chapter summary
2.12: Summary of important R code
2.13: Practice problems
03: One-Way ANOVA
3.01: Situation
3.02: Linear model for One-Way ANOVA (cell means and referencecoding)
3.03: One-Way ANOVA Sums of Squares Mean Squares and F-test
3.04: ANOVA model diagnostics including QQ-plots
3.05: Guinea pig tooth growth One-Way ANOVA example
3.06: Multiple (pair-wise) comparisons using Tukeys HSD and the compact letter display
3.07: Pair-wise comparisons for the Overtake data
3.08: Chapter summary
3.09: Summary of important R code
3.10: Practice problems
04: Two-Way ANOVA
4.01: Situation
4.02: Designing a two-way experiment and visualizing results
4.03: Two-Way ANOVA models and hypothesis tests
4.04: Guinea pig tooth growth analysis with Two-Way ANOVA
4.05: Observational study example - The Psychology of Debt
4.06: Pushing Two-Way ANOVA to the limit - Un-replicated designs and Estimability
4.07: Chapter summary
4.08: Summary of important R code
4.09: Practice problems
05: Chi-square tests
5.01: Situation contingency tables and tableplots
5.02: Homogeneity test hypotheses
5.03: Independence test hypotheses
5.04: Models for R by C tables
5.05: Permutation tests for the X2 statistic
5.06: Chi-square distribution for the X2 statistic
5.07: Examining residuals for the source of differences
5.08: General protocol for X2 tests
5.09: Political party and voting results - Complete analysis
5.10: Is cheating and lying related in students
5.11: Analyzing a stratified random sample of California schools
5.12: Chapter summary
5.13: Summary of important R code
5.14: Practice problems
06: Correlation and Simple Linear Regression
6.01: Relationships between two quantitative variables
6.02: Estimating the correlation coefficient
6.03: Relationships between variables by groups
6.04: Inference for the correlation coefficient
6.05: Are tree diameters related to tree heights
6.06: Describing relationships with a regression model
6.07: Least Squares Estimation
6.08: Measuring the strength of regressions - R2
6.09: Outliers - leverage and influence
6.10: Residual diagnostics setting the stage for inference
6.11: Old Faithful discharge and waiting times
6.12: Chapter summary
6.13: Summary of important R code
6.14: Practice problems
07: Simple linear regression inference
7.01: Model
7.02: Confidence interval and hypothesis tests for the slope and intercept
7.03: Bozeman temperature trend
7.04: Randomization-based inferences for the slope coefficient
7.05: Transformations part I - Linearizing relationships
7.06: Transformations part II - Impacts on SLR interpretations - log(y) log(x) and both log(y) and log(x)
7.07: Confidence interval for the mean and prediction intervals for a new observation
7.08: Chapter summary
7.09: Summary of important R code
7.10: Practice problems
08: Multiple linear regression
8.01: Going from SLR to MLR
8.02: Validity conditions in MLR
8.03: Interpretation of MLR terms
8.04: Comparing multiple regression models
8.05: General recommendations for MLR interpretations and VIFs
8.06: MLR inference - Parameter inferences using the t-distribution
8.07: Overall F-test in multiple linear regression
8.08: Case study - First year college GPA and SATs
8.09: Different intercepts for different groups - MLR with indicator variables
8.10: Additive MLR with more than two groups - Headache example
8.11: Different slopes and different intercepts
8.12: F-tests for MLR models with quantitative and categorical variables and interactions
8.13: AICs for model selection
8.14: Case study - Forced expiratory volume model selection using AICs
8.15: Chapter summary
8.16: Summary of important R code
8.17: Practice problems
09: Case studies
9.01: Overview of material covered
9.02: The impact of simulated chronic nitrogen deposition on the biomass and N2-fixation activity of two boreal feather mosscyanobacteria associations
9.03: Ants learn to rely on more informative attributes during decision-making
9.04: Multi-variate models are essential for understanding vertebrate diversification in deep time
9.05: What do didgeridoos really do about sleepiness
9.06: General summary
10: Appendix
10.01: A- Bibliography
10.02: B- Index