In the today’s class I have learned the new topics related to the linear regression and some of the concepts which is related to the Centers for Disease Control and Prevention (CDC) data sets such as skewness, kurtosis, and Heteroskedasticity.

Though, linear regression is an important tool in data analysis and statistics which can used to understand the relationship between variables, make predictions, and explore data.

The simplest form of linear regression is called simple linear regression:

Y = β₀ + β₁X + ɛ

where:

Y is dependent variable

X is independent variable

β₀ and β₁ are unknown constants

ɛ is an error term

The linear model for multiple linear regression:

Y = β0 + β1X1 + β2X2 +…+ βkXk + ɛ

where:

X1, X2,…,Xk are the independent variables each with its own coefficient β1, β2,…, βk.

Skewness: Skewness is a statistical measure that quantifies the asymmetry or lack of symmetry in the probability distribution of a dataset. In other words, it helps us understand the shape of the distribution of data points in a dataset.

Kurtosis: Kurtosis is a statistical measure that quantifies the shape and characteristics of the probability distribution of a dataset, specifically focusing on the tails or extremes of the distribution. It tells us about the degree of outliers in the data compared to a normal distribution.

Heteroskedasticity: A statistical concept known as heteroskedasticity describes the existence of non-constant variability (variance) in a regression model’s errors or residuals. In plainer English, it indicates that in a regression analysis, the spread or dispersion of the residuals is inconsistent across different values of the independent variables.

There are several methods for identifying heteroskedasticity. One method is to have a look at the regression analysis’ residuals. Heteroskedasticity is indicated if the residuals are not distributed uniformly around zero. Statistical tests like the Breusch-Pagan test or the White test can also be used to identify heteroskedasticity.