Chapman and Hall/CRC; 2nd Edition
Analysis of Variance; Design; and Regression: Linear Modeling for Unbalanced Data; 2nd Edition; (PDF) offers linear structures for modeling data with a focus on how to include specific ideas (hypotheses) about the structure of the data into a linear model for the data. The ebook carefully evaluates small data sets by using tools that are easily scaled to large data. The tools also apply to small relevant data sets that are taken from big data.
New to the 2nd Edition
- Unbalanced split-plot analyses
- Examination of homologous factors
- Extensions to generalized linear models
- Reorganized to focus on unbalanced data
- Introductions to nonparametric and lasso regression
- R; Minitab®; and SAS code on the author’s website
- Introductions to general additive and generalized additive models
- Reworked balanced analyses using methods for unbalanced data
The text can be used in a number of courses; including ANOVA or a data analysis course for upper-division statistics and a yearlong graduate course on regression for students and graduate students from other fields. It places a strong focus on interpreting the range of computer output faced when dealing with unbalanced data.
“… written in a lucid and clear style … an excellent option for a beginning level graduate textbook on statistical methods … a helpful reference for practitioners.” ? Zentralblatt für Mathematik
“Being dedicated to students mainly; each chapter has illustrative examples and exercises. The most significant thing about this ebook is that it offers traditional tools for future approaches in the big data domain since; as the author says; the machine learning techniques are straightaway based on the fundamental statistical methods.” ? Marina Gorunescu (Craiova)
NOTE: The product includes the ebook; Analysis of Variance; Design; and Regression: Linear Modeling for Unbalanced Data; 2nd Edition; in PDF. No access codes are included.
Chapman and Hall/CRC; 2nd Edition
Table of contents
Table of contents :
Content: Introduction Probability Random variables and expectations Continuous distributions The binomial distribution The multinomial distribution One Sample Example and introduction Parametric inference about mu Prediction intervals Model testing Checking normality Transformations Inference about sigma2 General Statistical Inference Model-based testing Inference on single parameters: assumptions Parametric tests Confidence intervals P values Validity of tests and confidence intervals Theory of prediction intervals Sample size determination and power The shape of things to come Two Samples Two correlated samples: Paired comparisons Two independent samples with equal variances Two independent samples with unequal variances Testing equality of the variances Contingency Tables One binomial sample Two independent binomial samples One multinomial sample Two independent multinomial samples Several independent multinomial samples Lancaster-Irwin partitioning Simple Linear Regression An example The simple linear regression model The analysis of variance table Model-based inference Parametric inferential procedures An alternative model Correlation Two-sample problems A multiple regression Estimation formulae for simple linear regression Model Checking Recognizing randomness: Simulated data with zero correlation Checking assumptions: Residual analysis Transformations Lack of Fit and Nonparametric Regression Polynomial regression Polynomial regression and leverages Other basis functions Partitioning methods Splines Fisher’s lack-of-fit test Multiple Regression: Introduction Example of inferential procedures Regression surfaces and prediction Comparing regression models Sequential fitting Reduced models and prediction Partial correlation coefficients and added variable plots Collinearity More on model testing Additive effects and interaction Generalized additive models Final comment Diagnostics and Variable Selection Diagnostics Best subset model selection Stepwise model selection Model selection and case deletion Lasso regression Multiple Regression: Matrix Formulation Random vectors Matrix formulation of regression models Least squares estimation of regression parameters Inferential procedures Residuals, standardized residuals, and leverage Principal components regression One-Way ANOVA Example Theory Regression analysis of ANOVA data Modeling contrasts Polynomial regression and one-way ANOVA Weighted least squares Multiple Comparison Methods “Fisher’s” least significant difference method Bonferroni adjustments Scheffe’s method Studentized range methods Summary of multiple comparison procedures Two-Way ANOVA Unbalanced two-way analysis of variance Modeling contrasts Regression modeling Homologous factors ACOVA and Interactions One covariate example Regression modeling ACOVA and two-way ANOVA Near replicate lack-of-fit tests Multifactor Structures Unbalanced three-factor analysis of variance Balanced three-factors Higher-order structures Basic Experimental Designs Experiments and causation Technical design considerations Completely randomized designs Randomized complete block designs Latin square designs Balanced incomplete block designs Youden squares Analysis of covariance in designed experiments Discussion of experimental design Factorial Treatments Factorial treatment structures Analysis Modeling factorials Interaction in a Latin square A balanced incomplete block design Extensions of Latin squares Dependent Data The analysis of split-plot designs A four-factor example Multivariate analysis of variance Random effects models Logistic Regression: Predicting Counts Models for binomial data Simple linear logistic regression Model testing Fitting logistic models Binary data Multiple logistic regression ANOVA type logit models Ordered categories Log-Linear Models: Describing Count Data Models for two-factor tables Models for three-factor tables Estimation and odds ratios Higher-dimensional tables Ordered categories Offsets Relation to logistic models Multinomial responses Logistic discrimination and allocation Exponential and Gamma Regression: Time-to-Event Data Exponential regression Gamma regression Nonlinear Regression Introduction and examples Estimation Statistical inference Linearizable models Appendix A: Matrices and Vectors Appendix B: Tables Exercises appear at the end of each chapter.