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102,29 €
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Linear Regression
Linear Regression
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102,29 €
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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 - Bina…
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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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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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