Question

You want to investigate whether or not the amount of money spent in advertising (in millions of dollars) has an effect on sales (millions of units). You gather the following data: Advertising Sales Mean Standard Deviation Correlation coeft 124.80 90.61 14.40 5.50 0.88 Which is the explanatory variable?

Answer 1

4427 1.1.1 you want to investing whether or amount of money spent in Tin millions of dollars) has Sales million of units The is given not the adve. an offect on Adevertising X (explanatory 230 variable) goles response y 45 10 17 152 180 total = 624 explanatory Variable response variable a total= Adevertising sales (million 18 13 72 million of of unit) Holland Bo Exiyi - Eu Syizxi 10739 624 X 72 SXP2 - Exi) 2 110110- 162412 11 B = 1753.4 328428 Y- Iyi = 14.4, 3=1248 OSURA = 0.0534 BOY- Bix Bo = 14.4-124.880 0534 Bos7.7357 Intercept = 7.7357

a $80 in advertising If our company were predicted model to spend dater ir Y = Bot Bix Bo=79357 Pi00534 = 7.3357 + 0.0534 X 80 ŷ = 12.0077 If the company were to spend $80 in advertiring so our model would predict sceler of 12 million of units /

Also i will pro0vide the R codes

x
[1] 230 45 17 152 180
> y
[1] 22 10 9 18 13
> model=lm(y~x)
> model

Call:
lm(formula = y ~ x)

Coefficients:
(Intercept) x
7.73722 0.05339

> summary(model)

Call:
lm(formula = y ~ x)

Residuals:
1 2 3 4 5
1.9836 -0.1397 0.3552 2.1479 -4.3470

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.73722 2.49012 3.107 0.0530 .
x 0.05339 0.01673 3.190 0.0497 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.033 on 3 degrees of freedom
Multiple R-squared: 0.7724, Adjusted R-squared: 0.6965
F-statistic: 10.18 on 1 and 3 DF, p-value: 0.0497

> predectedmodelforsales=7.73722 +0.05339 *80
> predectedmodelforsales
[1] 12.00842