Statistics with R - Beginner Level

Basic statistical analyses using the R program
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  • Lectures 46
  • Length 3 hours
  • Skill Level Beginner Level
  • Languages English
  • Includes Lifetime access
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About This Course

Published 3/2016 English

Course Description

If you want to learn how to perform the basic statistical analyses in the R program, you have come to the right place.

Now you don’t have to scour the web endlessly in order to find how to compute the statistical indicators in R, how to build a cross-table, how to build a scatterplot chart or how to compute a simple statistical test like the one-sample t test. Everything is here, in this course, explained visually, step by step.

So, what will you learn in this course?

First of all, you will learn how to manipulate data in R, to prepare it for the analysis: how to filter your data frame, how to recode variables and compute new variables.

Afterwards, we will take care about computing the main statistical figures in R: mean, median, standard deviation, skewness, kurtosis etc., both in the whole population and in subgroups of the population.

Then you will learn how to visualize data using tables and charts. So we will build tables and cross-tables, as well as histograms, cumulative frequency charts, column and mean plot charts, scatterplot charts and boxplot charts.

Since assumption checking is a very important part of any statistical analysis, we could not elude this topic. So we’ll learn how to check for normality and for the presence of outliers.

Finally, we will perform some basic, one-sample statistical tests and interpret the results. I’m talking about the one-sample t test, the binomial test and the chi-square test for goodness-of-fit.

So after graduating this course, you will know how to perform the essential statistical procedures in the R program. So… enroll today!

What are the requirements?

  • R and R studio
  • knowledge of basic statistics

What am I going to get from this course?

  • manipulate data in R (filter and sort data sets, recode and compute variables)
  • compute statistical indicators (mean, median, mode etc.)
  • determine skewness and kurtosis
  • get statistical indicators by subgroups of the population
  • build frequency tables
  • build cross-tables
  • create histograms and cumulative frequency charts
  • build column charts, mean plot charts and scatterplot charts
  • build boxplot diagrams
  • check the normality assumption for a data series
  • detect the outliers in a data series
  • perform univariate analyses (one-sample t test, binomial test, chi-square test for goodness-of-fit)

What is the target audience?

  • students
  • PhD candidates
  • academic researchers
  • business researchers
  • University teachers
  • anyone looking for a job in the statistical analysis field
  • anyone who is passionate about quantitative analysis

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.

Curriculum

Section 1: Introduction
Introduction
Preview
05:45
Section 2: Data Manipulation in R
07:56

How to filter your data frames with brackets (in base R).

05:07

How to filter your data frames using subsets.

04:03

How to filter your data set using the dplyr package

05:46

How to recode categorical variables in R

05:04

How to recode continuous variables in R

04:10

How to sort data sets using various criteria

01:52

How to compute new variables based on the existing ones

Article

All the codes used in the lectures 2-8, for your reference

Article

Practical exercises for the lectures 2-8

Section 3: Descriptive Statistics
03:36

How to compute the statistical indicators (mean, median, standard deviation etc.) in base R

03:42

How to compute statistical indicators with the psych package

04:56

How to compute statistical indicators using the pastecs package

01:35

How to compute skewness and kurtosis in R

02:15

How to detemine the quantiles of a distribution

01:29

How to compute the mode of a distribution

05:12

How to compute the statistical indicators by groups using the DoBy package

02:42

How to compute the statistical indicators with the DescribeBy package

04:29

How to compute the statistical indicators with the stats package

Article

All the codes used in the lectures 11-19, for your reference

Article

Practical exercises for the lectures 11-19

Section 4: Creating Frequency Tables and Cross Tables
06:40

How to build frequency tables

05:00

How to build frequency tables using the package plyr

01:24

Creating cross-tables with the xtabs command

04:20

Creating cross-tables with the CrossTable command

Article
All the codes used in the lectures 22-25, for your reference
Article
Practical exercises for the lectures 22-25
Section 5: Building Charts
07:09

How to create a histogram for your distribution

11:20

How to create cumulative frequency line charts

05:36

How to build column charts

13:57

How to build mean plot charts

11:22

How to build scatterplot charts

06:02

How to build boxplot charts

Article
All the codes used in the lectures 28-33, for your reference
Article

Practical exercises for the lectures 28-33

Section 6: Checking Assumptions
02:22

How to check for normality using numerical methods

03:36

How to check for normality using graphical methods

02:05

How to detect the extreme values in your data series

Article

All the codes used in the lectures 36-38, for your reference

Article
Practical exercises for the lectures 36-38
Section 7: Performing Univariate Analyses
03:41

How to run and interpret the one-sample t test

06:07

How to run and interpret the binomial test

07:08

How to perform the chi-square test for goodness-of-fit

Article

All the codes used in the lectures 41-43, for your reference

Article
Practical exercises for the lectures 41-43
Section 8: Course Materials
Article

Here you can download the csv files and the R files.

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Instructor Biography

Bogdan Anastasiei, Learn with an Experienced University Teacher

My name is Bogdan Anastasiei and I am an assistant professor at the University of Iasi, Romania, Faculty of Economics and Business Administration. I teach Internet marketing and quantitative methods for business. I am also a business consultant. I have run quantitative risk analyses and feasibility studies for various local businesses and been implied in academic projects on risk analysis and marketing analysis. I have also written courses and articles on Internet marketing and online communication techniques. I have about 20 years experience in teaching and about 10 years experience in business consulting.

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