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# Statistics for Data Science

## Ridge Regression and Its Application

In this article, we will be learning the practical implementation, advantages, and disadvantages of Ridge Regression. Ordinary least squares regression chooses the parameters of a linear function of a set of explanatory variables by the…

## Queueing Theory and Its Application

Introduction In this article, we will learn about Queueing Theory and its practical applications. We all have experienced the annoyance of having to wait in a queue. We wait in line at supermarkets to check out, we wait in line in banks and…

## AB Testing With R: An Example Of Marketing Campaign

In this article, we will learn the concepts and implementation of AB Testing using R. Marketing campaigns are meant to influence a targeted audience and encourage them to purchase a product. In this process, a lot of questions arise into…

## Obtaining A Critical Region And p-Value

In this article, we will discuss how important obtaining a critical region is in the analytical and data science problem-solving. The critical region is the set of all values of the test statistic that causes us to reject the null…

## Missing Value Imputation Techniques In R

Missing values occur when no data is available for a column of an observation. They are expressed by a symbol “NA” which means “Not Available” in R. Missing values introduces vagueness and miss interpretability in any form of statistical…

## Multivariate Analysis Of Variance Or MANOVA

Introduction In ANOVA we examine if there is any statistically significant effect of independent variables on a continuous dependent variable using the sum of squares. But here we only have one dependent variable. It’s very simple, but in…

## Hypothesis Testing Examples

Whenever any sample is collected and interpreted it is required at the same time to check its reliability and consistency with the population or to make any inference about the population. Statistical hypothesis testing does this for us.…

## Which Non Parametric Tests to Apply When

While dealing with hypothesis testing, we come across situations where nothing can be assumed about the population distribution, or when the data is not present in representable numerical form (ordinal or nominal data). In such situations,…

## Correlation Coefficient In Data Science

In your everyday life, have you ever wondered if there is any type of relationship between your height and your weight, your income and your expenditure, your age and your working ability and so on? The concept of correlation deals with…

## Experimental Design And Its Application

Experimental Design is a vast concept. In this article, we will learn about: The various terminologies required to build the concept ( experimental units, treatments, experimental error, blocks). The three principles of Experimental Design…