Question: A multiple regression model is used to develop an equation to account for seasonal effects and any linear trend in the data on average monthly

A multiple regression model is used to develop an equation to account for seasonal effects and any linear trend in the data on average monthly distance traveled (in billion miles) by vehicles on urban highways for five different years. To capture seasonal effects, dummy variables are used as Jan = 1 if month is January, 0 otherwise; Feb = 1 if month is February, 0 otherwise; ; Nov = 1 if month is November, 0 otherwise; and create a variable t such that t = 1 for January of year 1, t = 2 for February of year 1, , t = 60 for December of year 5. The excel output is following

What is the forecast for September of year 8 in billion miles?

		5.060
		5.213
		none of the other options
		5.256
		5.182

SUMMARY OUTPUT Regression Statistics Multiple R 0.969064688 R Square 0.939086369 Adjusted R Square 0.923533953 Standard Error 0.104384637 Observations 60 ANOVA df F Significance F 60.38202358 2.20819E-24 Regression Residual Total 12 47 59 SS MS 7.895180833 0.657932 0.512119167 0.010896 8.4073 Intercept Jan Feb Mar Apr May June Jul Aug Sep Oct Coefficients Standard Error t Stat 4.57575 0.054739766 83.59097 -0.438034722 0.066593988 -6.57769 0.680513889 0.066494492 10.23414 0.5850625 0.066404343 8.810606 0.483611111 0.066323579 7.291692 0.152159722 0.066252235 2.296673 -0.213291667 0.066190342 -3.2224 -0.148743056 0.066137925 -2.24898 -0.172194444 0.066095008 -2.60526 -0.159645833 0.066061608 -2.41662 -0.099097222 0.066037741 -1.50061 -0.058548611 0.066023417 -0.88679 0.009451389 0.000794081 11.9023 P-value Lower 9596 Upper 95% Lower 95.0% 8.89956E-53 4.465627796 4.6858722 4.465627796 3.57753E-08 -0.572004547 -0.3040649 -0.572004547 1.50936E-13 0.546744226 0.81428355 0.546744226 1.61658E-11 0.451474194 0.71865081 0.451474194 2.95587E-09 0.35018528 0.61703694 0.35018528 0.026140045 0.018877417 0.28544203 0.018877417 0.00231125 -0.346449458 -0.0801339 -0.346449458 0.029239658 -0.281795398 -0.0156907 -0.281795398 0.012255352 -0.305160449 -0.0392284 -0.305160449 0.019601345 -0.292544647 -0.026747 -0.292544647 0.140143636 -0.231948021 0.03375358 -0.231948021 0.379709655 -0.191370593 0.07427337 -0.191370593 8.67895E-16 0.007853904 0.01104887 0.007853904 Upper 95.0% 4.685872204 -0.304064898 0.814283552 0.718650806 0.617036942 0.285442028 -0.080133875 -0.015690713 -0.03922844 -0.02674702 0.033753577 0.074273371 0.011048873 t

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