SSTRVOL CPDIST5 CPWVOL5 logprice EDUC logprice HHLARGE data oj Residuals Min 1Q

Sstrvol cpdist5 cpwvol5 logprice educ logprice

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SSTRVOL + CPDIST5 + CPWVOL5 + log(price) * EDUC + log(price) * ## HHLARGE, data = oj) ## ## Residuals: ## Min 1Q Median 3Q Max ## -5.5569 -0.3783 -0.0058 0.3655 2.9176 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 13.083750 0.329134 39.752 < 2e-16 *** ## log(price) -3.047208 0.078854 -38.644 < 2e-16 *** ## brandminute.maid 0.099788 0.043154 2.312 0.0208 * ## brandtropicana 0.828836 0.047048 17.617 < 2e-16 *** ## feat 1.045910 0.035215 29.701 < 2e-16 *** ## AGE60 2.048070 0.124602 16.437 < 2e-16 *** ## EDUC -1.902940 0.141700 -13.429 < 2e-16 *** ## ETHNIC 0.663499 0.036398 18.229 < 2e-16 *** ## INCOME -0.263737 0.032570 -8.098 5.82e-16 *** ## HHLARGE 3.371095 0.426995 7.895 3.01e-15 *** ## WORKWOM -0.354677 0.143504 -2.472 0.0135 * ## HVAL150 0.385016 0.040857 9.424 < 2e-16 *** ## SSTRDIST -0.019436 0.001432 -13.573 < 2e-16 *** ## SSTRVOL -0.051164 0.009546 -5.360 8.40e-08 *** ## CPDIST5 0.061727 0.006146 10.043 < 2e-16 *** ## CPWVOL5 -0.499262 0.025116 -19.878 < 2e-16 *** ## log(price):brandminute.maid 0.749391 0.056766 13.201 < 2e-16 *** ## log(price):brandtropicana 0.669588 0.052543 12.744 < 2e-16 *** ## log(price):feat -0.383392 0.068458 -5.600 2.16e-08 *** ## brandminute.maid:feat 1.122814 0.075729 14.827 < 2e-16 *** ## brandtropicana:feat 0.713521 0.091233 7.821 5.42e-15 *** ## log(price):EDUC 3.516905 0.126909 27.712 < 2e-16 *** ## log(price):HHLARGE -5.347214 0.461653 -11.583 < 2e-16 *** ## log(price):brandminute.maid:feat -1.073288 0.112926 -9.504 < 2e-16 *** ## log(price):brandtropicana:feat -0.974821 0.114631 -8.504 < 2e-16 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Residual standard error: 0.6418 on 28922 degrees of freedom ## Multiple R-squared: 0.6039, Adjusted R-squared: 0.6036 ## F-statistic: 1837 on 24 and 28922 DF, p-value: < 2.2e-162Ci and iithe coefficient of EDUC is 3.516905 which indicates lower price sensitivity. the coefficient of HHLARGE is-5.347214 which indicates higher price sensitivity My estimate make sense with my intuition.iii
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4/19/2017HW3.html6/6the coefficient of EDUC changes from 0.955931 to -1.902940. And price sensitivity changes from low to high. thecoeffcient of HHLARGE changes from -0.920092 to 3.371095. and the price sensitivity changes from high to low.coef(reg2)["HHLARGE"]## HHLARGE ## 3.371095summary(log(oj$price)*(oj$HHLARGE))## Min. 1st Qu. Median Mean 3rd Qu. Max. ## -0.10450 0.06200 0.08659 0.09035 0.11510 0.29280iv3.371095*0.08659=0.291900.11510*3.371095=0.388010.38801-0.29190=0.096113 the value changed by 0.096113. This positive number indicate the low pricesensitivity.Df1 <- oj Df1$week <- Df1$week-1 Df2 <- merge(oj,Df1, by="week")conceptual exercise1a. I will run regression between sales of banana and the price of the orange juice,when the sales of bananaincreasing, the price of the orange decreases. I would run quantitysalesbanana* priceoforangeb. iI believe the sign of those coefficient will be positive. Because I choose the sales of the banana and price of theorange as varibles. Because the price of the orange increases, people are more likely to purchase the banana. Asa result the sales of the banana will increaseiiI believe the sign of those coefficient will be positive. Because I choose the sales of the banana and price of theorange as varibles. Because the price of the orange increases, people are more likely to purchase the banana. Asa result the sales of the banana will increasec. NO I should not continue to offer the bundle at the same price sold separately. Because the advantage of bundleis that bundled package could cost as much as a third less than the each-sold-separately package. So once theprice return to normal price without discount, people will not buy the bundle!
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