The median is usually calculated for numeric variables, but may also be calculated for categorical variables that are sequenced, such as the categories in a satisfaction survey: excellent, good, satisfactory and poor.

The basic statistics available for categorical variables are counts and percentages. You can also specify custom summary statistics for totals and subtotals.

The most common measure of dispersion is the range. For nominal variables, since the categories are not ordered, the range is simply the number of categories with at least one response. The 0th and 100th percentile would be the range of data.

Examples include weight, price, profits, counts, etc. Basically, anything you can measure or count is quantitative. Categorical data, in contrast, is for those aspects of your data where you make a distinction between different groups, and where you typically can list a small number of categories.

Categorical data is a collection of information that is divided into groups. I.e, if an organisation or agency is trying to get a biodata of its employees, the resulting data is referred to as categorical.

The Chi Square statistic is commonly used for testing relationships between categorical variables. The null hypothesis of the Chi-Square test is that no relationship exists on the categorical variables in the population; they are independent.

The data used in calculating a chi-square statistic must be random, raw, mutually exclusive, drawn from independent variables, and drawn from a large enough sample. For example, the results of tossing a fair coin meet these criteria. Chi-square tests are often used in hypothesis testing.

A chi-square test is used when you want to see if there is a relationship between two categorical variables.

If two variables are associated, the probability of one will depend on the probability of the other. Chi square tests the hypothesized association between two categorical variables and contingency analysis allows us to quantify their association.

To measure the relationship between numeric variable and categorical variable with > 2 levels you should use eta correlation (square root of the R2 of the multifactorial regression). If the categorical variable has 2 levels, point-biserial correlation is used (equivalent to the Pearson correlation).

Categorical variables contain a finite number of categories or distinct groups. Categorical data might not have a logical order. Continuous variables are numeric variables that have an infinite number of values between any two values. A continuous variable can be numeric or date/time.

The Pearson’s correlation coefficient is calculated as the covariance of the two variables divided by the product of the standard deviation of each data sample. It is the normalization of the covariance between the two variables to give an interpretable score.

Point Biserial Correlation

1. import pandas as pd.
2. import numpy as np.
3. ​
4. num1=np. random. normal(loc=60,scale=5,size=100.
5. df1=pd. DataFrame(num1,columns=[‘Salary’])
6. df1[‘Type’]=’EmpType1′
7. ​
8. num2=np. random. normal(loc=50,scale=5,size=100.

What are the three limitations of correlation and regression? Because although 2 variables may be associated with each other, they may not necessarily be causing each other to change. In other words, a lurking variable may be present. Why does association not imply causation?