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  3. Inference for Linear Regression in R
  • 1

    Inferential ideas

    Gratuito

    In the first chapter, you will understand how and why to perform inferential (instead of descriptive only) analysis on a regression model.

  • 2

    Simulation-based inference for the slope parameter

    In this chapter you will learn about the ideas of the sampling distribution using simulation methods for regression models.

  • 3

    t-Based Inference For the Slope Parameter

    In this chapter you will learn about how to use the t-distribution to perform inference in linear regression models. You will also learn about how to create prediction intervals for the response variable.

  • 4

    Technical Conditions in linear regression

    Additionally, you will consider the technical conditions that are important when using linear models to make claims about a larger population.

  • 5

    Building on Inference in Simple Linear Regression

    This chapter covers topics that build on the basic ideas of inference in linear models, including multicollinearity and inference for multiple regression models.

Initializing

Ejercicio

Working with R-output (1)

The p-value given by the lm output is a two-sided p-value by default. In the twin study, it might seem more reasonable to follow along the one-sided scientific hypothesis that the IQ scores of the twins are positively associated. Because the p-value is the probability of the observed data or more extreme, the two-sided test p-value is twice as big as the one-sided result. That is, to get a one-sided p-value from the two-sided output in R, divide the p-value by two.

The linear regression model of Foster vs. Biological, model, is provided in the script.

Instrucciones

100 XP
  • Get the Biological coefficient from the model.
    • Tidy the model.
    • Filter for the term "Biological".
  • Add a column, one_sided_p_value, of the one-sided p-value.