Statistical analysis with r

How to Analyse data using R?

Preprocessing – Cleaning the data is mandatory to put it in a structured format before performing analysis.

1. Removing outliers( noisy data)
2. Removing null or irrelevant values in the columns
3. If there is any missing data, either ignore the tuple or fill it with a mean value of the column

How to do statistical analysis using R?

In this chapter, Pearson's correlation coefficient (also known as Pearson's r), the chi-square test, the t-test, and the ANOVA will be covered.
Pearson's correlation coefficient (r) is used to demonstrate whether two variables are correlated or related to each other..

How to do statistical analysis using R?

R is a programming language created by statisticians for statistics, specifically for working with data.
It is a language for statistical computing and data visualizations used widely by business analysts, data analysts, data scientists, and scientists..

Quantitative statistical tools

R, an open-source tool, empowers data enthusiasts to explore, analyze, and visualize data with precision.
Whether you're delving into descriptive statistics, probability distributions, or sophisticated regression models, R's versatility and extensive packages facilitate seamless statistical exploration._{Aug 30, 2023}.

What statistical test to use in R?

Many data scientists use R while analyzing data because it has static graphics that produce good-quality data visualizations.
Moreover, the programming language has a comprehensive library that provides interactive graphics and makes data visualization and representation easy to analyze..

What statistical test uses R value?

In this chapter, Pearson's correlation coefficient (also known as Pearson's r), the chi-square test, the t-test, and the ANOVA will be covered.
Pearson's correlation coefficient (r) is used to demonstrate whether two variables are correlated or related to each other..