Inference for Linear Regression in R

Name: Inference for Linear Regression in R
Brand: Careervira
Price: 150 USD
Availability: InStock
Rating: 1 (1 reviews)

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DataCamp

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Course Features

Duration

4 hours

Delivery Method

Online

Available on

Limited Access

Accessibility

Mobile, Desktop, Laptop

Language

English

Subtitles

English

Level

Intermediate

Teaching Type

Self Paced

Video Content

4 hours

Course Description

You've already learned the basics about linear modeling and statistical inference. It is now time to combine them. This course will help you understand how different samples can produce different linear models. This course is designed to help you understand the basic population model. The course will teach you how to estimate intervals and calculate the significance of the effect of the estimated linear model. Comparisons will be made between the prediction intervals of the response variable as well as the estimates for the average response. For cleaning up models, you can use ggplot2, dplyr and the broom packages. These three tools are vital in data science.

Course Overview

Virtual Labs

International Faculty

Post Course Interactions

Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

Intermediate Regression in R

Foundations of Inference

What You Will Learn

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

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

In this chapter you will learn about how to use the t-distribution to perform inference in linear regression models

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

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

You will learn how to create interval estimates for the effect size as well as how to determine if the effect is significant

Course Content

Expand all sections

Module 1: Inferential ideas

Module 2: Simulation-based inference for the slope parameter

Module 3: t-Based Inference For the Slope Parameter

Module 4: Technical Conditions in linear regression

Course Instructors

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 clo...