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Stats 2 - chapter 10-15, Chapter 14 - regression models for analyzing the…
Stats 2 - chapter 10-15
Chapter 10 - statistical methods for analyzing relationships between categorical and quantitative variables
Means and proportions for two independent groups: independent samples t-tests + chi-squared tests for two independent proportions
Inference for two dependent groups: statistical methods for comparing means and proportions between two dependent groups
- Paired samples t-tests + McNemar's test for two dependent proportions
Multiple comparisons: = used to compare means across multiple groups
Analysis of covariance (ANCOVA): = used to test for differences in means across two or more groups while controlling for a continuous covariate.
Chapter 11 - statistical methods for analyzing relationships between two or more quantitative variables
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Multiple regression: = used to model the relationship between dependent variable and two or more independent variables
Model selection: = the process of selecting the best regression model from a set of candidate models
- Methods = forward selection, backward elimination, and stepwise selection
Chapter 12 - statistical methods for analyzing relationships between a categorical response variable and one or more quantitative predictor variables - ANOVA
One-way ANOVA: = statistical method used to test for differences in means across two or more groups when the response variable is categorical and the predictor variable is quantitative.
- F-test for equality of means
- Post hoc comparison
Two-way ANOVA: = statistical method used to test for differences in means across two or more groups when there are two predictor variables, one of which is categorical and the other is quantitative.
Analysis of covariance (ANCOVA): = used to test for differences in means across two or more groups while controlling for a continuous covariate
Repeated measures ANOVA: = used to test for differences in means across two or more groups when there are repeated measurements on the same subject
- The interpretation of the ANOVA table
- The use of post hoc comparisons
Chapter 13 - regression models for analyzing the relationship between quantitative response variable and one or more predictor variables.
Introduction: regression analysis, including the use of scatterplots and regression lines to visualize the relationship between the response and predictor variables
Simple linear regression: = used to model the linear relationship between a quantitative response variable and a single predictor variable.
- The estimation of regression coefficients
- Interpretation of the slope and intercept
- The use of regression equation for prediction
Multiple regression: = used to model the linear relationship between a quantitative response variable and multiple predictor variables.
Model building: the process of model building, including the selection of predictor variables and the use of diagnostic plots to check for violations of regression assumptions
Regression with categorical variables: regression models that include categorical predictor variables, indicator variables, dummy variables, and effect coding
Chapter 14 - regression models for analyzing the relationship between a quantitative response variable and one or more predictor variables when there is nonlinearity, interaction, or both in the relationship
Nonlinear regression: = regression models that allow for nonlinear relationships between the response and predictor variables.
Interaction: interaction in regression models = occurs when the effect of one predictor variable on the response depends on the value of another predictor variable
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Chapter 15 - regression models for analyzing the relationship between a binary response variable and one or more predictor variable.
Introduction to binary regression: logistic regression to model the probability of a binary response variable
Logistic regression: = used to model the relationship between a binary response variable and one or more predictor variables.
- The use of logistic equation for prediction
Multiple logistic regression: = used to model the relationship between a binary response variable and multiple predictor variables
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