Learning Statistics with R

  • Categories STATISTICS
  • Total Enrolled 0
  • Last Update August 21, 2020


Finding the right guide to commence your statistical analyses journey using R programming will never be daunting anymore now that you are here.

Starting from navigating through the interface, setting up and styling your R studio, importing datasets, building charts and plots, computing simples statistical test and performing regression analyses, the fundamentals you need to know about this course are detailed pictorially, step by step.

So, what will you learn in this course?

We will begin by learning how to import, manipulate data in R, to prepare it for the analysis, filter, record and compute variables.

After which, we will perform simple statistical computations in R like mean, median, mode standard deviation, both in the whole population and in subgroups of the population.

Commence visualizing of our imported data using tables and charts, building cross-tables, histograms, cumulative frequency charts, scatterplot and boxplot charts.

Understanding that assumption checking is a very important part of any statistical analysis, we could not elude this topic by learning how to check for normality and presence of outliers.

And finally perform some basic, one-sample statistical tests and interpret the results, the likes of one-sample t test, the binomial test, the chi-square test for goodness-of-fit, analyses of variance (ANOVA) and regression analyses.

Upon completion of this course, you will know how to perform essential and rudimentary procedures in R program. What are you waiting for? ….. Enroll Now!



Main Features

  • 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)
  • Explore, Infer Binomial and Multinomial Logistic Regression
  • Analyses of Variance with One-way, Pairwise and Two-way ANOVA

What is the target audience?


  • R and R studio
  • knowledge of basic statistics

The Course Target Audience are

  • 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