![]() 7.1.4 Using the linear model for prediction – prediction models.7.1.3 What a regression coefficient means.7.1.2 Learning from the green-down example.7.1 A linear model with a single, continuous X is classical “regression”.6.11 “linear model,”regression model“, or”statistical model"?.6.10 Specific assumptions for inference with a linear model. ![]() 6.9 Assumptions for inference with a statistical model.6.8 Models fit to data in which the \(X\) are treatment variables are regression models.6.5 What do we call the \(X\) and \(Y\) variables?.6.4 Statistical models are used for prediction, explanation, and description.6.3.3 Comparing the two ways of specifying the linear model.6.3.2 The “conditional draw” specification.6.3 Two specifications of a linear model.This, raises the question, what is “an effect”? 6.1 This text is about the estimation of treatment effects and the uncertainty in our estimates.6 An Introduction to Statistical Modeling.Part III: Introduction to Linear Models.5.5.1 Interpretation of a confidence interval.5.4.1 An example of bootstrapped standard errors using vole data.5.3.4 Part IV – Generating fake data with for-loops.5.3.3 part III - how do SD and SE change as sample size (n) increases?.5.3 Using R to generate fake data to explore the standard error.5.2 Using Google Sheets to generate fake data to explore the standard error.5.1 The sample standard deviation vs. the standard error of the mean.5 Variability and Uncertainty (Standard Deviations, Standard Errors, Confidence Intervals).Part II: Some Fundamentals of Statistical Modeling.4.3.3 Adding modeled means and error intervals. ![]()
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