Statistics for Psychology Using R: A Linear Models Perspective
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Statistics for Psychology Using R: A Linear Models Perspective
Lisi, Matteo; Clarke, Alasdair
Open University Press
08/2025
320
Mole
Inglês
9780335252626
15 a 20 dias
Descrição não disponível.
List of figures
List of tables
Introduction
1 Straight lines and the R programming language
1.1 Linear relationships
1.2 The equation of a straight line
1.3 Introduction to R
1.4 Summary
2 Probability and the normal distribution
2.1 Probability space
2.2 The psychology of probabilities
2.3 Probability distributions
2.4 Working with the normal distribution
2.5 Summary
3 Fitting linear models to data
3.1 First, some geometry
3.2 Importing data
3.3 Linear regression
3.4 Which line fits best?
3.5 Example: 'Tips from the Top'
3.6 Summary
4 Linear models with categorical predictors
4.1 Variables in R
4.2 Linear models for categorical predictors
4.3 The t-test: a linear model in disguise
4.4 More than two categorical levels
4.5 Summary
5 Logarithms, exponentials and data transformations
5.1 Exponentials and logarithms
5.2 Example: gender representation in cinema
5.3 Visualizing skewed data
5.4 Importing, reshaping and cleaning data
5.5 Example: visual search
5.6 Summary
6 The bigger picture: contextualizing statistical methods in psychology
6.1 What do our statistics actually represent?
6.2 Statistical errors and power analysis
6.3 Simulation and sensitivity analysis
6.4 Data visualization
6.5 Summary
7 Linear models with more than one predictor
7.1 Regression with multiple predictors
7.2 Interactions between variables
7.3 Summary
8 Linear models in the real world: overfitting, collinearity, confounding
and sampling biases
8.1 Problems with adding new predictors
8.2 Causal reasoning for beginners
8.3 Summary
9 Repeated measures and multilevel models
9.1 Example: 'Tips from the Top' again
9.2 Fixed versus random effects
9.3 More complex random effect structures
9.4 Summary
10 Models for binary dependent variables
10.1 Generalized linear models for binary outcomes
10.2 Working with multiple predictors
10.3 Multilevel logistic regression
10.4 Summary
Epilogue
Glossary of terms
References
List of tables
Introduction
1 Straight lines and the R programming language
1.1 Linear relationships
1.2 The equation of a straight line
1.3 Introduction to R
1.4 Summary
2 Probability and the normal distribution
2.1 Probability space
2.2 The psychology of probabilities
2.3 Probability distributions
2.4 Working with the normal distribution
2.5 Summary
3 Fitting linear models to data
3.1 First, some geometry
3.2 Importing data
3.3 Linear regression
3.4 Which line fits best?
3.5 Example: 'Tips from the Top'
3.6 Summary
4 Linear models with categorical predictors
4.1 Variables in R
4.2 Linear models for categorical predictors
4.3 The t-test: a linear model in disguise
4.4 More than two categorical levels
4.5 Summary
5 Logarithms, exponentials and data transformations
5.1 Exponentials and logarithms
5.2 Example: gender representation in cinema
5.3 Visualizing skewed data
5.4 Importing, reshaping and cleaning data
5.5 Example: visual search
5.6 Summary
6 The bigger picture: contextualizing statistical methods in psychology
6.1 What do our statistics actually represent?
6.2 Statistical errors and power analysis
6.3 Simulation and sensitivity analysis
6.4 Data visualization
6.5 Summary
7 Linear models with more than one predictor
7.1 Regression with multiple predictors
7.2 Interactions between variables
7.3 Summary
8 Linear models in the real world: overfitting, collinearity, confounding
and sampling biases
8.1 Problems with adding new predictors
8.2 Causal reasoning for beginners
8.3 Summary
9 Repeated measures and multilevel models
9.1 Example: 'Tips from the Top' again
9.2 Fixed versus random effects
9.3 More complex random effect structures
9.4 Summary
10 Models for binary dependent variables
10.1 Generalized linear models for binary outcomes
10.2 Working with multiple predictors
10.3 Multilevel logistic regression
10.4 Summary
Epilogue
Glossary of terms
References
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<span style="font-family: Arial;font-size: 13.3333px;white-space-collapse: preserve;background-color: rgb(255, 255, 255);">R; R studio; t-test; ANOVA; social science; stats; data science; research methods; quantitative methods</span>
List of figures
List of tables
Introduction
1 Straight lines and the R programming language
1.1 Linear relationships
1.2 The equation of a straight line
1.3 Introduction to R
1.4 Summary
2 Probability and the normal distribution
2.1 Probability space
2.2 The psychology of probabilities
2.3 Probability distributions
2.4 Working with the normal distribution
2.5 Summary
3 Fitting linear models to data
3.1 First, some geometry
3.2 Importing data
3.3 Linear regression
3.4 Which line fits best?
3.5 Example: 'Tips from the Top'
3.6 Summary
4 Linear models with categorical predictors
4.1 Variables in R
4.2 Linear models for categorical predictors
4.3 The t-test: a linear model in disguise
4.4 More than two categorical levels
4.5 Summary
5 Logarithms, exponentials and data transformations
5.1 Exponentials and logarithms
5.2 Example: gender representation in cinema
5.3 Visualizing skewed data
5.4 Importing, reshaping and cleaning data
5.5 Example: visual search
5.6 Summary
6 The bigger picture: contextualizing statistical methods in psychology
6.1 What do our statistics actually represent?
6.2 Statistical errors and power analysis
6.3 Simulation and sensitivity analysis
6.4 Data visualization
6.5 Summary
7 Linear models with more than one predictor
7.1 Regression with multiple predictors
7.2 Interactions between variables
7.3 Summary
8 Linear models in the real world: overfitting, collinearity, confounding
and sampling biases
8.1 Problems with adding new predictors
8.2 Causal reasoning for beginners
8.3 Summary
9 Repeated measures and multilevel models
9.1 Example: 'Tips from the Top' again
9.2 Fixed versus random effects
9.3 More complex random effect structures
9.4 Summary
10 Models for binary dependent variables
10.1 Generalized linear models for binary outcomes
10.2 Working with multiple predictors
10.3 Multilevel logistic regression
10.4 Summary
Epilogue
Glossary of terms
References
List of tables
Introduction
1 Straight lines and the R programming language
1.1 Linear relationships
1.2 The equation of a straight line
1.3 Introduction to R
1.4 Summary
2 Probability and the normal distribution
2.1 Probability space
2.2 The psychology of probabilities
2.3 Probability distributions
2.4 Working with the normal distribution
2.5 Summary
3 Fitting linear models to data
3.1 First, some geometry
3.2 Importing data
3.3 Linear regression
3.4 Which line fits best?
3.5 Example: 'Tips from the Top'
3.6 Summary
4 Linear models with categorical predictors
4.1 Variables in R
4.2 Linear models for categorical predictors
4.3 The t-test: a linear model in disguise
4.4 More than two categorical levels
4.5 Summary
5 Logarithms, exponentials and data transformations
5.1 Exponentials and logarithms
5.2 Example: gender representation in cinema
5.3 Visualizing skewed data
5.4 Importing, reshaping and cleaning data
5.5 Example: visual search
5.6 Summary
6 The bigger picture: contextualizing statistical methods in psychology
6.1 What do our statistics actually represent?
6.2 Statistical errors and power analysis
6.3 Simulation and sensitivity analysis
6.4 Data visualization
6.5 Summary
7 Linear models with more than one predictor
7.1 Regression with multiple predictors
7.2 Interactions between variables
7.3 Summary
8 Linear models in the real world: overfitting, collinearity, confounding
and sampling biases
8.1 Problems with adding new predictors
8.2 Causal reasoning for beginners
8.3 Summary
9 Repeated measures and multilevel models
9.1 Example: 'Tips from the Top' again
9.2 Fixed versus random effects
9.3 More complex random effect structures
9.4 Summary
10 Models for binary dependent variables
10.1 Generalized linear models for binary outcomes
10.2 Working with multiple predictors
10.3 Multilevel logistic regression
10.4 Summary
Epilogue
Glossary of terms
References
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
