![]() ![]() 8.1.2 Using a linear model for prediction.8.1.1 Using a linear model to estimate explanatory effects.8.1 A linear model with a single, continuous X is classical “regression”.8 A linear model with a single, continuous X.Part III: Introduction to Linear Models.7.0.3 Correlated X (fake observational data).7.0.2 Categorical X (fake experimental data). ![]() 7.0.1 Continuous X (fake observational data).6.4.5 Some major misconceptions of the \(p\)-value.6.4.3 Two interpretations of the p-value.6.4.2 This book covers frequentist approaches to statistical modeling and when a probability arises, such as the p-value of a test statistic, this will be a frequentist probability.6.4 frequentist probability and the interpretation of p-values.6.3 Statistical modeling instead of hypothesis testing.6.2.3 P-values from the perspective of permutation.4.5.1 Interpretation of a confidence interval.4.4.1 An example of bootstrapped standard errors using vole data.4.3.4 Part IV – Generating fake data with for-loops.4.3.3 part III - how do SD and SE change as sample size (n) increases?.4.3 Using R to generate fake data to explore the standard error.4.2 Using Google Sheets to generate fake data to explore the standard error.4.1 The sample standard deviation vs. the standard error of the mean.4 Variability and Uncertainty (Standard Deviations, Standard Errors, Confidence Intervals).3.8 “Statistical model” or “regression model”?.3.7 Specific assumptions for inference with a linear model.3.6 Assumptions for inference with a statistical model.3.3 Statistical models are used for prediction, explanation, and description.3.2 What do we call the \(X\) and \(Y\) variables?.3.1.3 Comparing the two ways of specifying the linear model.3.1.2 The “conditional draw” specification.3.1 Two specifications of a linear model.3 An Introduction to Statistical Modeling. ![]()
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