Statistics in R - The R Language for Statistical Analysis


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Torrent Size : 505.77 MB


Statistics in R - The R Language for Statistical Analysis
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Torrent File Content (31 files)


Statistics in R - The R Language for Statistical Analysis
    02 Inferential Statistics
          002 Z Test.mp4 -
37.23 MB

    01 Welcome
          002 Summary Statistics.mp4 -
13.36 MB

          003 Variable Types.mp4 -
17.37 MB

    02 Inferential Statistics
          001 Inferential Statistics - Script.pdf -
426.22 KB

          001 R Statistical Packages.mp4 -
20.61 MB

    01 Welcome
          001 Course Intro.mp4 -
33.35 MB

    02 Inferential Statistics
          003 Tests for Normality.mp4 -
19.65 MB

          004 Exercise and Solution Normality Tests.mp4 -
19.62 MB

          005 1 Sample T Test.mp4 -
13.63 MB

          006 2 Sample Independent T Test - Welch Test.mp4 -
17.27 MB

          007 Exercise and Solution T Test.mp4 -
8.89 MB

          008 Mann Whitney U Test - Wilcoxon Test.mp4 -
18.03 MB

          009 ANOVA.mp4 -
13.52 MB

          010 Post Hoc Tests - Tukey HSD.mp4 -
9.29 MB

          011 Chi Squared Test and Kruskal Wallis Test.mp4 -
11.95 MB

          012 Exercise ANOVA.mp4 -
4.76 MB

          013 Solution ANOVA.mp4 -
24.17 MB

          014 Exercise and Solution Chi Squared Test.mp4 -
9.09 MB

          015 Correlations.mp4 -
11.77 MB

          016 Summary Sections 1 and 2.mp4 -
27.51 MB

    03 Regressions
          001 Linear Regression.mp4 -
20.02 MB

          001 Regressions - Script.pdf -
79.5 KB

          002 Multiple Linear Regression.mp4 -
18.05 MB

          003 Exercise and Solution Linear Regression.mp4 -
8.71 MB

          004 Logistic Regression.mp4 -
19.17 MB

          005 Exercise and Solution Logistic Regression.mp4 -
11.68 MB

          006 Summary Regression.mp4 -
28.31 MB

    04 Bonus Section Material from the Machine Learning Course
          001 Course Catalogue and Coupons.pdf -
1.78 MB

          001 KNN - K Nearest Neighbors Classifier.mp4 -
30.75 MB

          002 Random Forests.mp4 -
35.75 MB

     Torrent downloaded from www.DNoid.me - Demonoid.txt -
56 bytes



Description



Miscellaneous


Course Description
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

What are the requirements?
solid understanding of R programming - up to my "R Level 1" course
R and R Studio ready on your computer
basic understanding of statistics (descriptive, inferential, regression)
high interest in data analysis
What am I going to get from this course?
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
What is the target audience?
students who need data analysis in their work
data analysts
entrepreneurs with quantitative interests
everybody interested in statistics
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