Price,P.(S) Weekly Demand,S(P)(1,000s) 211 207 192 188 183 175 150 170 190 210 2

Question

Price,P.(S) Weekly Demand,S(P)(1,000s) 211 207 192 188 183 175 150 170 190 210 230 250 the linear model that best fits this data using regression and enter the model below Note: Round the linear parameter value to nearest 0.01 and constant parameter to nearest 1. White the anewer in proper Function Notation) Function: Preniew B. The squared correlation coefficient was Seecta answe 0.95 (note: values less than 0 95 MAY mean the model in not appropriate for maicing predicticna) C. Usang the function rom Part A what does the model predict wall be tihe meekdty demand if the price of an ipod touch in $245 (Round to the nearest 100) The weekly demand for an ipod priced at $245 will be approximarely D According to the model ar whar should the pnce be set an order to bave a weekdy demand of 198,100 spod Touches? Round to the nearest dolla To have a weeldly demand of 195,700, the price of the ipod should be se: at approximately

Explanation / Answer

Use R software to build a model:

weeklydemand <- c(211,207,192,188,183,175)
price <- c(150,170,190,210,230,250)
mod1 <- lm(weeklydemand~price)
summary(mod1)

output:

Coefficients:
Estimate Std. Error t value Pr(>|t|)   
(Intercept) 265.80952 6.97652 38.10 2.83e-06 ***
price -0.36571 0.03438 -10.64 0.000442 ***

weeklydemand=265.8-0.37*price

Solutionb:

From output,Multiple R-squared: 0.9658

R sq=0.97(roundng to nearest integer)

Solutionc:

weeklydemand=265.8-0.37*price

price=245

Weekly demand=265.8-0.37*245

Weekly Demand=175.15

Weekly demand=175

Solutiond:

weeklydemand=265.8-0.37*price

we have weekly demand=198.7

198.7=265.8-0.37*price

0.37*price=265.8-198.7

0.37*price=67.1

price=67.1/0.37

price=181.3514

price=181

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