# Descriptive Statistics with Basic Data Visualization in R programming

R programming language is an extremely versatile and user friendly software for programming. It is commonly used for statistical analysis. Lets go over some of the most basic yet frequently used functions in R.

Functions covered:

• Other important functions in different libraries
• Important Plots in R

You…

# Data Science Misconceptions

Given below are a series of statements that I have heard over time! They might make sense initially, but lets analyze it deeper to come to a logical conclusion. If the statements are in fact untrue, lets see why!

# Statistical Distributions for a career in Data Science

Like we discussed, you need to know basics of statistics to be able to analyze the data better. And one topic of statistics, the most important in Data science industry, is the Concept of Distributions.

What are you waiting for! Lets dive into the important theory needed in data science…

# Must’ve skills for a career in Data Science and their free-resources

In my previous article, we went over some of the foundational topics used by Data Science professionals. In order to develop the must-have’s for this profession, we must use our time to the fullest and upskill.

# Must’ve skills

Whenever someone applies for a job opportunity, there are always a set of skills…

# Test for Heteroscedasticity, Multicollinearity and Autocorrelation

In the articles earlier, we understood the importance of observing the three behaviors in the model: Homoscedasticity, Multicollinearity and Autocorrelation.

We would be going over the concept and R application of most used tests in multiple regression modeling. The list is as follows:

1. VIF (Variance Inflation Factor)
2. Runs…

# Multiple Linear Regression

In the last two articles, we explored the concept of Simple Linear Regression Model (i.e., regression involving two variables). Although, the practical situations demand much more complexity, for almost all the situations when we apply this concept in real life, we have numerous variables (which might or might not affect…

# Linear Regression Model-Part 2

In my previous article, we went over the model fitting for the given data of percentage of hardwood pulp in the paper(X) and the tensile strength of the paper (Y).

Once we have established the significance of the regression model and the estimated of the population parameters of the model…

# Simple Linear Regression Modeling-Part 1

Regression Analysis is one of the most acknowledged and useful tools of statistics. It is one of the most efficient ways to understand the relation between certain variables, while being able to make logical predictions for the future.

Lets understand Simple linear regression with an example and R codes. But…

# Testing for Normality

In my previous article, we went over the single variable hypothesis testing. Likewise, we can apply the tests for the double variable testing like, testing for the significance of the difference of means, testing for the difference of population variances and others.

But now, we would move over the topic…

# Test Statistics for Hypothesis Testing in Statistics with Functions in R Programming

In my previous article, we talked about the terminology in the statistical world of hypothesis testing.

Moving on, once we have our hypothesis about the population parameters (based on the sample) in place, we need some sophisticated ways to practically support one claim (either H0 or H1) by using statistical… 