**Dummy Variable Trap** occurs when predictors are multicollinear, that is one predictor can predict another.

**Statistical Analysis of Dummy Variable Trap**

For example, if I am having a data set like below(only 15 rows shown here), where the first 4 features are used to predict Profit. The First 3 Columns contain numerical data and the fourth column D contains categorical data. Before building a model categorical data is to be encoded.

**Linear regression** is used when the dataset has a linear correlation. Before building a linear regression model **assumptions** are to be **validated**. If the assumptions are violated, different methodologies must be used.

**Simple linear regression **has one independent variable (predictor) and a dependent variable(response) and **multiple linear regression** has more than one predictor to predict response.

The simple linear regression equation is represented as

Multiple linear regression equation is represented as

Assumptions of Linear Regression Analysis are :

- Linearity
- No Heteroskedasticity
- No omitted variable bias
- Normality of error
- No autocorrelation
- No multicollinearity

**1. Linearity**

For linear regression analysis, there must…

**Regression analysis** is the most widely used method of prediction.** Linear regression** is used when the dataset has a linear correlation and as the name suggests, **simple linear regression **has one independent variable (predictor) and one dependent variable(response).

The simple linear regression equation is represented as** y = a+bx** where x is the explanatory variable, y is the dependent variable, b is coefficient and a is the intercept.

In linear regression, the best fit line will be one that will have minimum vertical distance with data points. One of the popular methods is the** least square method **and here the…

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