Question

A regional planner employed by a public university is studying the demographics of nine counties in the eastern region of an Atlantic seaboard state. She has gathered the following data:

County	Median Income			Median Age	Coastal
A	$	48,952		48.3	1
B		46,669		58.8	1
C		47,780		48.0	0
D		46,855		39.2	1
E		37,724		51.9	1
F		35,414		56.2	1
G		34,389		49.1	0
H		38,128		30.3	0
I		30,384		38.9	0

Include the aspect that the county is "coastal" or not in a multiple linear regression analysis using a "dummy" variable. (Negative amounts should be indicated by a minus sign. Round your answers to 2 decimal places.)

Income = ________+ ____________ Median Age + ___________ Coastal

Test each of the individual coefficients to see if they are significant. (Negative amounts should be indicated by a minus sign. Leave no cells blank - be certain to enter "0" wherever required. Round your answers to 2 decimal places.)

Predictor t p-value

Constant

Median Age

Coastal

Answer 1

Using R for the analysis,

Code :

data = read.csv('C:\\Users\\Temp\\Desktop\\coastal.csv')
model = lm(income~., data = data)
summary(model)

Output :

Model : Income = 39669.20 - 48.08* median age + 5899.94* coastal

Call:
lm(formula = income ~ ., data = data)

Residuals:
Min 1Q Median 3Q Max
-7453.0 -5349.8 -84.4 3927.0 10418.7

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 39669.20 14380.78 2.758 0.0329 * (< 0.05, is significant)
age -48.08 334.77 -0.144 0.8905 (> 0.05, not significant)
coastal 5899.94 5768.20   1.023 0.3458 (>0.05, not significant)
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 7237 on 6 degrees of freedom
Multiple R-squared: 0.1761,   Adjusted R-squared: -0.09859 (shows that model is not good enough)
F-statistic: 0.641 on 2 and 6 DF, p-value: 0.5594

None of the variables is significant from above table.

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