Statistics for Data Analysis Using R

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Statistics for Data Analysis Using R 4.5 (1,067 ratings) Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.

What you’ll learn

  • You will first learn the basic statistical concepts, followed by application of these concepts using R Studio. This course is a nice combination of theory and practice.
  • Descriptive Statistics – Mean, Mode, Median, Skew, Kurtosis
  • Inferential Statistics – One and two sample z, t, Chi Square, F Tests, ANOVA, TukeyHSD and more.
  • Probability Distributions – Normal, Binomial and Poisson
  • You will learn R programming from the beginning level.


Perform simple or complex statistical calculations using R Programming! – You don’t need to be a programmer for this 🙂

Learn statistics, and apply these concepts in your workplace using R.

The course will teach you the basic concepts related to Statistics and Data Analysis,  and help you in applying these concepts. Various examples and data-sets are used to explain the application.

I will explain the basic theory first, and then I will show you how to use R to perform these calculations.

Following areas of statistics are covered:

Descriptive Statistics – Mean, Mode, Median, Quartile, Range, Inter Quartile Range, Standard Deviation. (Using base R function and the psych package)

Data Visualization – 3 commonly used charts: Histogram, Box and Whisker Plot and Scatter Plot (using base R commands)

Probability – Basic Concepts, Permutations, Combinations (Basic theory only)

Population and Sampling – Basic concepts (theory only)

Probability Distributions – Normal, Binomial  and Poisson Distributions (Base R functions and the visualize package)

Statistics for Data Analysis Using R, Learn Programming in R & R Studio • Descriptive, Inferential Statistics • Plots for Data Visualization • Data Science Created by Sandeep Kumar English Students also bought Customer Analytics in Python 2020 R

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