2. Graded Quiz: Regression

Question 1 What are the requirements for independent and dependent variables in regression?

• • • Independent variables can be either categorical or continuous. Dependent variables must be continuous.

    • Independent and dependent variables must be continuous.

    • Independent variables must be continuous. Dependent variables can be either categorical or continuous.

    • Independent and dependent variables can be either categorical or continuous.

Question 2 The key difference between simple and multiple regression is:

• Multiple linear regression introduces polynomial features.

• Simple linear regression compresses multidimensional space into one dimension.

• To estimate a single dependent variable, simple regression uses one independent variable whereas multiple regression uses multiple.

• Simple regression assumes a linear relationship between variables, whereas this assumption is not necessary for multiple regression.

Question 3 Recall that we tried to predict CO2 emission with car information. Say that now we can describe the relationship as: CO2_emission = 130 - 2.4*cylinders + 8.3*fuel_consumption

What is TRUE of this relationship?

• • • When “cylinders” decreases by 1 while fuel_consumption remains constant, CO2_emission increases by 2.4 units.

    • When “cylinders” increases by 1 while fuel_consumption remains constant, CO2_emission increases by 2.4 units.

    • Since the coefficient for “fuel_consumption” is greater than that for “cylinders”, “fuel_consumption” has lower impact on CO2_emission.

    • When both “cylinders” and “fuel_consumption” increase by 1 unit, CO2_emission decreases.

Question 4 What could be the cause of a model yielding high training accuracy and low out-of-sample accuracy?

• • • The model is training on the entire dataset, so it is underfitting.

    • The model is training on a small training set, so it is underfitting.

    • The model is training on a small training set, so it is overfitting.

    • When we perform multiple train/test splits using the same dataset, it will cause overfitting.

Question 5 Multiple Linear Regression is appropriate for:

• • Predicting the sales amount based on month.

  • Predicting tomorrow’s rainfall amount based on the wind speed and temperature.

  • Predicting whether a drug is effective for a patient based on her characteristics.