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Inference for Linear Regression in R

AdvancedSkill Level
4.8+
50 reviews
Updated 12/2021
In this course you'll learn how to perform inference using linear models.
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RProbability & Statistics4 hours15 videos59 Exercises4,650 XP14,686Statement of Accomplishment

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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.

Prerequisites

Foundations of Inference in RIntermediate Regression in R
1

Inferential ideas

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2

Simulation-based inference for the slope parameter

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3

t-Based Inference For the Slope Parameter

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4

Technical Conditions in linear regression

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5

Building on Inference in Simple Linear Regression

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Inference for Linear Regression in R
Course
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Don’t just take our word for it

*4.8
from 50 reviews
90%
8%
2%
0%
0%
  • Vitalii
    10 days

  • Thi Thu Ha
    16 days

    Clear explanations and well-structured exercises. Helped me understand confidence intervals, hypothesis testing, and model assumptions in linear regression. Great for beginners and intermediate learners

  • SANDRA
    22 days

  • ΑΝΔΡΕΑΣ
    about 1 month

    Great course for regression models

  • Irvin Sinué
    about 1 month

  • ERNESTO
    about 1 month

    Excellent course of R inference

Vitalii

"Clear explanations and well-structured exercises. Helped me understand confidence intervals, hypothesis testing, and model assumptions in linear regression. Great for beginners and intermediate learners"

Thi Thu Ha

SANDRA

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