Statistics in R – The R Language for Statistical Analysis

Statistics in R – The R Language for Statistical Analysis, available at $49.99, has an average rating of 4.35, with 43 lectures, based on 330 reviews, and has 3421 subscribers.

You will learn about know which statistical test to use for a given question know how to perform the most important statistical tests in R know how to perform regression modeling in R have a very good understand of statistical testing and regressions use R Commander as alternative to RStudio perform stats analysis on outliers This course is ideal for individuals who are students who need data analysis in their work or data analysts or entrepreneurs with quantitative interests or everybody interested in statistics It is particularly useful for students who need data analysis in their work or data analysts or entrepreneurs with quantitative interests or everybody interested in statistics.

Enroll now: Statistics in R – The R Language for Statistical Analysis

Summary

Title: Statistics in R – The R Language for Statistical Analysis

Price: $49.99

Average Rating: 4.35

Number of Lectures: 43

Number of Published Lectures: 43

Number of Curriculum Items: 43

Number of Published Curriculum Objects: 43

Original Price: $44.99

Quality Status: approved

Status: Live

What You Will Learn

know which statistical test to use for a given question
know how to perform the most important statistical tests in R
know how to perform regression modeling in R
have a very good understand of statistical testing and regressions
use R Commander as alternative to RStudio
perform stats analysis on outliers

Who Should Attend

students who need data analysis in their work
data analysts
entrepreneurs with quantitative interests
everybody interested in statistics

Target Audiences

students who need data analysis in their work
data analysts
entrepreneurs with quantitative interests
everybody interested in statistics

Do you want to learn more about statistical programming?

Are you in a quantitative field?

You want to know how to perform statistical tests and regressions?

Do you want to hack the learning curve and stay ahead of your competition?

If YES came to your mind to some of those points – read on!

This tutorial will teach you anything you need to know about descriptive and inferential statistics as well as regression modeling in R.

While planing this course we were focusing on the most important inferential tests that cover the most common statistical questions.

After finishing this course you will understand when to use which specific test and you will also be able to perform these tests in R.

Furthermore you will also get a very good understanding of regression modeling in R. You will learn about multiple linear regressions as well as logistic regressions.

According to the teaching principles of R Tutorials every section is enforced with exercises for a better learning experience. You can download the code pdf of every section to try the presented code on your own.

Should you need a more basic course on R programming we would highly recommend our R Level 1 course. The Level 1 course covers all the basic coding strategies that are essential for your day to day programming.

What R you waiting for?

Martin

Course Curriculum

Chapter 1: Welcome

Lecture 1: Course Intro

Lecture 2: Summary Statistics

Lecture 3: Variable Types

Chapter 2: Inferential Statistics

Lecture 1: Section Script

Lecture 2: R Statistical Packages

Lecture 3: Z Test

Lecture 4: Tests for Normality

Lecture 5: Exercise and Solution Normality Tests

Lecture 6: Further R Exercises

Lecture 7: 1 Sample T Test

Lecture 8: 2 Sample Independent T Test – Welch Test

Lecture 9: Exercise and Solution T Test

Lecture 10: Mann Whitney U Test – Wilcoxon Test

Lecture 11: ANOVA

Lecture 12: Post Hoc Tests – Tukey HSD

Lecture 13: Chi Squared Test and Kruskal Wallis Test

Lecture 14: Exercise ANOVA

Lecture 15: Solution ANOVA

Lecture 16: Exercise and Solution Chi Squared Test

Lecture 17: Correlations

Lecture 18: Summary Sections 1 and 2

Lecture 19: Inferential Statistics – Script

Chapter 3: Statistical Methods for Outlier Detection

Lecture 1: Theory Behind Outlier Detection

Lecture 2: Outlier Detection – Univariate

Lecture 3: Outlier Detection – Multivariate

Lecture 4: Outlier Section Script

Chapter 4: Statistical Modeling and Regressions

Lecture 1: Section Script

Lecture 2: Modeling Theory 1

Lecture 3: Modeling Theory 2

Lecture 4: Modeling Theory 3

Lecture 5: Linear Regression

Lecture 6: Multiple Linear Regression

Lecture 7: Exercise and Solution Linear Regression

Lecture 8: Logistic Regression

Lecture 9: Exercise and Solution Logistic Regression

Lecture 10: Summary Regression

Lecture 11: Regressions – Script

Chapter 5: Advanced Modeling Techniques

Lecture 1: KNN – K Nearest Neighbors Classifier

Lecture 2: Random Forests

Chapter 6: R Commander

Lecture 1: Link Collection useful for R Commander

Lecture 2: R Commander Intro

Lecture 3: Getting Data into Rcmdr

Lecture 4: Modeling with R Commander

Instructors

R-Tutorials Training
Data Science Education

Rating Distribution

1 stars: 15 votes
2 stars: 9 votes
3 stars: 58 votes
4 stars: 117 votes
5 stars: 131 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

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