Understanding Linear Regression Techniques

Should we use Simple Linear Regression or Multiple Linear Regression in this example?

• Simple Linear Regression

• Multiple Linear Regression

What degree should our polynomial be in this example?

• 1

• 2

• 3

• 4

When do we use Multiple Linear Regression?

• There are multiple explanatory variables for one response variable

• There are multiple response variables for one explanatory variable

• There are multiple outcomes for one possibility

• Not sure

Which scenario would you use Multiple Linear Regression?

• Housing price vs square footage

• Exam scores vs hours spent studying

• Advertising spend and revenue

• Fuel efficiency vs type of vehicle

• Y-intercept

• Test statistic

• P-Value

• Explanatory variable

• The proportion of variance in the dependent explained by the independent

• The average error in the predictions

• The strength of the relationship between two explanatory variables

• Standard deviation

• True

• False

• The independent variable

• The dependent variable

• The residual error

• Explanatory variable

A degree 2 polynomial represents a...

• Quadratic relationship

• Linear relationship

• Cubic relationship

Which of the following steps should Evan take before running a multiple linear regression analysis, and why?

• Use predictors without modification because each has a linear relationship with rent

• Standardize the predictors to ensure they are on the same scale

• Remove predictors that are correlated to avoid multicollinearity

• Use polynomial regression to capture potential non-linear relationships