Statistics for Data Science and Business Analysis - OLS Assumptions

The video tutorial introduces regression assumptions, emphasizing their importance in regression analysis. It covers five key assumptions: linearity, endogeneity of regressors, normality and homoscedasticity of error terms, no autocorrelation, and no multicollinearity. Each assumption is explained with its mathematical basis and practical implications. The tutorial stresses the necessity of understanding these assumptions to avoid errors in regression analysis.

Why is it important to understand the intuition behind regressions before learning about regression assumptions?

• To apply the assumptions correctly in analysis

• To memorize the assumptions more easily

• To make regression analysis more complex

• To avoid using regression analysis altogether

What does the assumption of linearity in regression imply?

• Each independent variable is multiplied by a coefficient and summed

• Each independent variable is squared

• The relationship between variables is logarithmic

• The relationship between variables is exponential

What does endogeneity of regressors mean in regression analysis?

• Errors are always negative

• The covariance between errors and independent variables is one

• The covariance between errors and independent variables is zero

• Errors are always positive

What does homoscedasticity mean in the context of regression assumptions?

• The variance of errors is constant

• The variance of errors is random

• The variance of errors is increasing

• The variance of errors is decreasing

What is multicollinearity in regression analysis?

• When two or more variables have a high correlation

• When two or more variables have a low correlation

• When variables are independent of each other

• When variables are dependent on each other