The chairman of the market department at St. Joseph’s University asked 30 gradua

Question

The chairman of the market department at St. Joseph’s University asked 30 graduating seniors to answer a survey so they could collect information on regression of salary with regards to GPA. Sadly they got less than half of the respondents.

Regression Analysis

r²

0.812

n

13

r

(a)

k

1

Std. Error

2.61

Dep. Var.

Y

Which is a good description of the slope?

a.         For every $1000 higher starting salary the GPA is $7,587 more.

            b.         For every $1000 higher starting salary the GPA is $8,133 more.

            c.         For every point higher in the GPA the starting salary is $7,587 more.

            d.         For every point higher in the GPA the starting salary is $8,133 more.

Regression Analysis

r²

0.812

n

13

r

(a)

k

1

Std. Error

2.61

Dep. Var.

Y

ANOVA table Source df MS F p-value 1 325.6561 11 6.8395 12 47.61 2.59E-05 Regression 325.6561 75.2346 Total 400.8908 Residual Regression output confidence interval 95% 95% variables coefficients error t (df 11) p-value lowerupper 0734 0.8505 16.0245 std Intercept 7.5870 3.8335 1.979 2.59E- GPA 8.1327 1.1786 ) 05 5.5386 10.7268 Predicted values for: Y X1 Predicted lower upper Lowerupper Leverage 3.25 34.0182 32.4151 35.6213 28.0430 39.9934 0.078

Explanation / Answer

Slope m in linear regression model y=mx+c always represent change between x and y, here slope=8.1327.

Thats means for every $1000 higher starting salary tje GPA the starting salary is $81327.

Hence option B is correct

Related Questions

Navigate

• Browse (All)
• Browse T
• Subjects

---

---

Integrity-first tutoring: explanations and feedback only — we do not complete graded work. Learn more.