We’re going to be using a dataset for Mario Kart sales on eBay on Multiple Linea

Question

We’re going to be using a dataset for Mario Kart sales on eBay on Multiple Linear Regression

1.What is the coefficient for the dummy variable for condition and Interpret the coefficient for the dummy variable for condition?

a.What is the coefficient for the dummy variable for number of bids and Interpret the coefficient for the dummy variable for number of bids?

b. Some of the coefficients in your output may make sense while others don’t. The only ones that we can make conclusive statements about are those that are significant.Which of the three IVs have a significant relationship with total price?

With this data: https://docs.google.com/spreadsheets/d/151ZOqln1io_okSbmQa4q_gFRSe9AaJk-F90SAPGfKYI/edit?usp=sharing

Variable Description ID Auction ID assigned by eBay duration Auction length, in days nBids| Number of bids cond Game condition, either new or used totalTotal price, which equals the auction price plus the shipping price shipSp Shipping speed or method sellerRate The seller's rating on eBay. This is the number of positive ratings minus the number of negative ratings for the seller stockPhota Whether the auction feature photo was a stock photo or not. If the picture was used in many auctions, then it was called a stock photo. wheels Number of Wii wheels included in the auction. These are steering wheel attachments to make it seem as though you are actually driving in the game. When used with the controller, turning the wheel actually causes the character on screer to tum. titleThe title of the auctions Estimate the linear regression model, y =+ 1xit 2X2 + 3Xy, to predict the total price by the auction length, number of bids, and the condition. You will need to use dummy variables for condition (1 new, 0 used) and nB ids (1 high, 0 = low). A high number of bids is defined as any Mario Kart game that sold with AT LEAST 20 bids. You can choose any name for your recoded dummy variables

Explanation / Answer

Let the dummy variables be:-

1. cond_bin for the original variable 'cond'

2. bidLevel for the original variable 'nBids'

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The linear regression model y_hat = Beta0 + Beta1*(duration) + Beta2*(cond_bin) + Beta3*(bidLevel)

------------- R-code --------------

dat3 = as.data.frame(read.csv(file = "Lab 9.csv", sep = ",", header = TRUE))
View(dat3)

dat3$bidLevel = ifelse(dat3$nBids >= 20, 1, 0)
dat3$cond_bin = ifelse(dat3$cond == "new", 1, 0)

View(dat3)

fit = lm(totalPr ~ duration + cond_bin + bidLevel, data = dat3)
summary(fit)

------------- R-code --------------

Beta0 = Intercept = 44.8769 | Significance = HIGH (p-value <<< 0.01)

Beta1 = Slope to 'duration' = -0.4112 | Significance = LOW (p-value = 0.137)

Beta2 = Slope to 'cond_bin' = 9.8859 | Significance = HIGH (p-value <<< 0.01)

Beta3 = Slope to 'bidLevel' = -0.2756 | Significance = LOW (p-value <<< 0.878)

--------------------- INTERPRETATIONS ---------------------

Beta0: At zero days of auction, for an old-conditioned item with number of bids < 20, total price predicted = $44.877

Beta1: For every 1 day passing by, the total price predicted decreases by about $0.4112

Beta2: For every 1 day passing by, the total price predicted increases by about $9.8859

Beta3: For every 1 day passing by, the total price predicted decreases by about $0.2756

--------------------- INTERPRETATIONS ---------------------

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