Question

Interpretation of the Simple Linear Regression equation. 300 words

Answer 1

A linear regression is a statistical technique where relationship between two variables can be studied / determined using a straight line. While plotting line graphs with X axis and Y axis, the X variable is called dependent or predictor variable whereas Y variable is called independent or criterion variable.

Regression analysis is used to find equations that complement the data. The equations are used to make predictions about the dependent variable, given the independednt or X variable.

The equation is of the form Y = A + BX, where Y is the dependent variable and X is the independent variable, B is the slope and A is the Y intercept.

Some examples can be height - weight, where once height increases, weight may also increase, alcohol consumption and blood level alcohol where with the increase in consumption of alcohol, blood level alcohol also increases.

Regression analysis aids us in finding the correlation between 2 variables, but it's limitation is that correlation does not gaurantee causation. Which means that happening of one event may not trigger the happening of another.

A regression line may have positive linear relationship, negative linear relationshipor no relationship. For no relationship, the regression line us flat. For positive relationship, the regression line slopes upward with the lower end of the line at the Y axis and the upper end of the line extends up away from the X axis meaning that as one variable increases other also increases. Negative relation means that the one end of line rises with Y axis the other end moves toward the X axis meaning that as one variable increases, the other variable decreases.