Martin, Peter, <br/><br/>
Linear regression: an introduction to statistical models / 
Peter Martin. 
 - xxii, 178 pages :   illustrations ;   24 cm. 
 - The SAGE quantitative research kit .
<br/><br/>Includes bibliographical references and index. 
<br/><br/>What is a statistical model?<br/>Simple linear regression<br/>Assumptions and transformations<br/>Multiple linear regression: A model for multivariate relationships<br/>Multiple linear regression: Inference, assumptions, and standardization<br/>Where to go from here 
<br/><br/> Part of The SAGE Quantitative Research Kit, this text helps you make the crucial steps towards mastering multivariate analysis of social science data, introducing the fundamental linear and non-linear regression models used in quantitative research. Peter Martin covers both the theory and application of statistical models, and illustrates them with illuminating graphs, discussing: • Linear regression, including dummy variables and predictor transformations for curvilinear relationships • Binary, ordinal and multinomial logistic regression models for categorical data • Models for count data, including Poisson, negative binomial, and zero-inflated regression • Checking model assumptions and the dangers of overfitting 
<br/><br/> College of Education  Bachelor of Secondary Education major in Mathematics 
<br/><br/><br/>In English 
<br/><br/><label>ISBN: </label>9781526424174 
<br/><br/><label>LCCN: </label>2020949998 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Regression analysis.<br/>Mathematical models.<br/>Quantitative research.
<br/><br/><label>Dewey Class. No.: </label>519.536 M36  / 2021