Multiple regression was performed on the cars dataset with quality as the response variable and speed and angle as the predictor variables. The following fitted values and residuals were obtained.

import pandas as pd import statsmodels.formula.api as sms

cars = pd.read_csv('http://data-analytics.zybooks.com/Cars.csv')

Response variable

Y = cars['Quality']

Generates the linear regression model

Multiple predictor variables are joined with +

model = sms.ols('Y ~ Speed + Angle', cars).fit()

print(model.fittedvalues) 0 0.756997 1 2.661578 2 2.880407 3 5.003817 4 4.784987 5 7.127226 6 6.689567 7 6.908397 8 9.250636 9 10.717557 10 13.059796 11 11.374046 12 12.840967 13 13.278626 14 14.964377 15 17.087786 16 19.211196 17 15.402036 dtype: float64 print(model.resid) 0 1.243003 1 0.338422 2 -0.880407 3 -1.003817 4 0.215013 5 0.872774 6 -0.689567 7 1.091603 8 -1.250636 9 -0.717557 10 0.940204 11 -0.374046 12 -0.840967 13 -0.278626 14 0.035623 15 0.912214 16 -0.211196 17 0.597964 dtype: float64

Based on the output, what is the second regression fitted value? Type as: #.######