57,09 €
Linear Regression
Linear Regression
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Linear Regression
Linear Regression
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57,09 €
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 variablesand predictor transformations for curvilinear relationships ·…
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  • Publisher:
  • Year: 2022
  • Pages: 180
  • ISBN: 9781529711059
  • ISBN-10: 1529711053
  • ISBN-13: 9781529711059
  • Format: ePub
  • Language: English

Linear Regression (e-book) (used book) | Peter Martin | bookbook.eu

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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 variablesand 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

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  • Author: Peter Martin
  • Publisher:
  • Year: 2022
  • Pages: 180
  • ISBN: 9781529711059
  • ISBN-10: 1529711053
  • ISBN-13: 9781529711059
  • Format: ePub
  • Language: English English

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 variablesand 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

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