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Inference for Linear Regression in R

In this course you'll learn how to perform inference using linear models.

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4 Hours15 Videos59 Exercises10,930 Learners4650 XPStatistical Inference Track

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Course Description

Previously, you learned the fundamentals of both statistical inference and linear models; now, the next step is to put them together. This course gives you a chance to think about how different samples can produce different linear models, where your goal is to understand the underlying population model. From the estimated linear model, you will learn how to create interval estimates for the effect size as well as how to determine if the effect is significant. Prediction intervals for the response variable will be contrasted with estimates of the average response. Throughout the course, you'll gain more practice with the dplyr and ggplot2 packages, and you will learn about the broom package for tidying models; all three packages are invaluable in data science.
  1. 1

    Inferential ideas

    Free

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

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    Variability in regression lines
    50 xp
    Regression output: example I
    100 xp
    First random sample, second random sample
    100 xp
    Superimpose lines
    100 xp
    Research question
    50 xp
    Regression hypothesis
    50 xp
    Variability of coefficients
    50 xp
    Original population - change sample size
    100 xp
    Hypothetical population - less variability around the line
    100 xp
    Hypothetical population - less variability in x direction
    100 xp
    What changes the variability of the coefficients?
    50 xp

In the following tracks

Statistical Inference

Collaborators

n10i
Nick Carchedi
nicksolomon
Nick Solomon
Jo Hardin Headshot

Jo Hardin

Professor at Pomona College

Jo Hardin is a professor of mathematics and statistics at Pomona College. Her statistical research focuses on developing new robust methods for high throughput data. Recently, she has also worked closely with the statistics education community on ways to integrate data science early into a statistics curriculum. When not working with students or on her research, she loves to put on a pair of running shoes and hit the road.
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