Question

In: Statistics and Probability

Individual Bettendorf Experience (X1) Education (X2) Sex (X3) 1 53600 5.5 4 F 2 52500 9...

Individual Bettendorf Experience (X1) Education (X2) Sex (X3)
1 53600 5.5 4 F
2 52500 9 4 M
3 58900 4 5 F
4 59000 8 4 M
5 57500 9.5 5 M
6 55500 3 4 F
7 56000 7 3 F
8 52700 1.5 4.5 F
9 65000 8.5 5 M
10 60000 7.5 6 F
11 56000 9.5 2 M
12 54900 6 2 F
13 55000 2.5 4 M
14 60500 1.5 4.5 M

1. What is the coefficient of determination between the three predictors taken as a group and annual salary.
Select one:

a..323

b..772

c..522

d..771

2. Let X1 = experience, X2 = Education, and X3 = Sex, what is the multiple regression equation?

Select one:

a.Y= 2809 + 228.5(X1) + 560.6(X2) + 1287.4(X3)

b.Y=41462.6 + 337.3(X1) + 2169.3(X2) + 3097.0(X3)

c.Y=48951.9 + 195.3(X1) + 1480.6(X2) + 1595.1(X3)

d.Y= 42410.2 + 403.5(X1) + 1856.4(X2) + 2964.4(X3)

Solutions

Expert Solution

The R output of the given regresssion problem is given below.

Bettendorf<-c(53600,52500,58900,59000,57500,55500,56000,52700,65000,60000,56000,54900,55000,60500)
Experience<-c(5.5,9,4,8,9.5,3,7,1.5,8.5,7.5,9.5,6,2.5,1.5)
Education<-c(4,4,5,4,5,4,3,4.5,5,6,2,2,4,4.5)
Sex<-c(1,2,1,2,2,1,1,1,2,1,2,1,2,2)
model <- lm(Bettendorf~ Experience + Education +Sex)# Coding 1 for F and 2 for M
summary(model)
[1]

Call:
lm(formula = Bettendorf ~ Experience + Education + Sex)

Residuals:
    Min      1Q  Median      3Q     Max 
-5727.4 -2218.7   667.8  1632.5  5389.6 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  47356.8     4491.8  10.543 9.77e-07 ***
Experience     195.3      329.7   0.593   0.5667    
Education     1480.6      809.0   1.830   0.0971 .  
Sex           1595.1     1857.6   0.859   0.4106    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3249 on 10 degrees of freedom
Multiple R-squared:  0.3234,    Adjusted R-squared:  0.1204 
F-statistic: 1.593 on 3 and 10 DF,  p-value: 0.2521

(1) From this R code We get coefficient of determination (Multiple ) as 0.32340.323. Therefore the corerct option is (a)0.323 .

(2) From the coefficients we get the estimates of the coefficients as follows,

Variables Estimates
Intercept 47356.8
Experience(X1) 195.3
Education(X2) 1480.6
Sex(X3) 1595.1

Therefore the equation of the regression line is . Hence the correct option is (c).

(In the given answer they wrongly given the intercept as 48951.9.)


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