R Tutorial For Beginners

R Tutorial For Beginners. Basic Statistical Tests Using Rstudio. We provide Step-by-Step R Tutorials, it is absolutely FREE! Please scroll down and enjoy our Free Online SPSS Resources for RStudio.

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Table of Content

Introduction

When it comes to analyzing data, R is one of the most popular statistical software programs available. However, running a statistical analysis in R can be a bit daunting for those who are not familiar with the program. On this page, R Tutorial for Beginners, we will walk you through the process of running a basic statistical analysis using R step-by-step. We will also provide some tips on how to interpret your results. By the end of this article, you will be equipped with the knowledge you need to run statistical analysis in R-studio like a pro!

1. Parametric Tests

Parametric tests are used when data follow a particular distribution (e.g., a normal distribution—a bell-shaped distribution where the median, mean, and mode are all equal). These tests are generally more powerful. Please click the following R Tutorial for Beginners page to see how to run Parametric Tests in Rstudio.

Paired Sample t Test

Independent Sample t Test

One-Way ANOVA Test

2. Non-parametric Tests

Nonparametric tests are used when a particular distribution cannot be assumed; they rank data rather than taking absolute differences into account. Please click the following R Tutorial for Beginners page to see how to run Nonparametric Tests in R.

Chi Square Test

Wilcoxon Signed Rank Test

Mann Whitney U Test

Kruskal Wallis Test

3. Analysis of Variance

We use the Analysis of variance (ANOVA) in statistics that splits an observed aggregate variability found inside a data set into two parts: systematic factors and random factors. The systematic factors have a statistical influence on the given data set, while the random factors do not. Analysts use the ANOVA test to determine the influence that independent variables have on the dependent variable in a regression study. Please click the following Free R Tutorial for Beginners page to see how to run Analysis of Variance Tests in SPSS.

Repeated ANOVA Test

4. Correlation Analysis

Correlation analysis is a method of statistical evaluation used to study the strength of a relationship between two, numerically measured, continuous variables. There are two types of correlation analysis. The Pearson correlation evaluates the linear relationship between two continuous variables while the Spearman correlation coefficient is based on the ranked values for each variable rather than the raw data. Please click the following R Tutorial for Beginners page to see how to run Correlayin Analysis Tests in R.

Spearman’s Rank Correlation Test

Pearson Correlation Test

5. Regression Analysis

Regression analysis is a powerful statistical method that allows you to examine the relationship between two or more variables of interest. While there are many types of regression analysis, at their core they all examine the influence of one or more independent variables on a dependent variable. Please click the following R Tutorial for Beginners page to see how to run Regression Analysis in R.

Simple Linear Regression Test

Multiple Regression Test

Binary Logistic Regression Test

What is R-Studio?

R-studio is a powerful and easy way to interact with R programming, considered an Integrated Development Environment (IDE) that provides a one-stop solution for all the statistical computing and graphics. It is a free open-source software program for statistical analysis based on the S language.

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