Statistics and Data Science with R: Complete Course
The R video tutorials are organized into 5 different series to make it easier for the students to navigate and explore. These videos provide an introduction to R programming software to help get the beginner going. The goal is to get the students over the initial challenges of working with R, and to provide some guidance on implementing standard set of analytic procedures learned in introductory and intermediate applied statistics course.
In Series 1, (Getting Started with R) you will first get familiar with R and RStudio and learn why we use them; next, you will learn to download and install R and RStudio, customize the look of RStudio and set up working directories in R. These Tutorials will also demonstrate how to import and copy data into R from excel (and other worksheets), how to export data from R into csv., txt. and other formats, how to create vectors and matrices in R, how to subset data using square brackets, how to use Logic statements and cbind and rbind in R, and how to produce scripts for reproducible research. They will also cover the use of apply and tapply functions in R.
In Series 2, (Plots and Descriptive Statisistics with R) we will learn to produce descriptive statistics such as plots and numeric summaries for our data using R programming software. These R video tutorials will show you how to produce bar charts and pie charts, boxplots with groups, stratified boxplots, histograms, stem and leaf plots, stacked bar charts, grouped bar charts, mosaic plots and scatter plots in R. We will also learn to produce numeric summaried by calculating mean, standard deviation and frequencies in R. After watching these R video, you will be able to customize the look of the plots in R and enhance them by adding text and legends to them.
The Series 3 R video tutorials (Probability Distributions in R) will walk us through working with probability distributions in R, specifically, calculating probabilities using probability densities (f(x)) and probability distributions (F(x)), finding quantiles of a distribution and taking random samples from a distribution using R programming language. In this series we will discuss how to compute Binomial distribution, Poisson distribution, Normal distribution, t- Distribution, z-scores and normal probabilities in R.
In Series 4 videos (Bivariate Analysis with R) we will leanr how to conduct bivariate analysis in R (and also a bit of univariate analysis, specifically the one-sample t-test for a population mean). These R video tutorials will discuss the paired and independent t-tests in R, analysis of variance (ANOVA) with R, the chi-square test of independence in R, calculating relative risks and odds ratios wiht R, correlation, simple linear regression, along with the non-parametric equivalents for all of these using R programming software.
Series 5 R videos (Linear Regression with R) show you how to fit a linear regression model in R and how to interpret model output from R. These R tutorials also cover how to assess the model fit, compare competing models (model selection), and other commonly discussed topics in linear regression modelling using the R programming software.The topics introduced in this series include linear regression model and how to check the model assumptions with R, multiple linear regression in R, changing numeric variable to categorical variable in R, dummy or indicator variables in R, how to change the refrene or baseline category for a categorical variable in regression model in R, examples on categorical variables in regression model in R, multiple linear regression with interaction in R, interpreting interactions in linear regression with R, variable selection in linear regression using partial F-test in R and polynomial regression with R.
