Question
In: Statistics and Probability
Supplementary Problem 14.27 (Essay) Given here are the data for a dependent variable, y, and independent...
Supplementary Problem 14.27 (Essay)
Given here are the data for a dependent variable, y,
and independent variables. Use these data to develop a regression
model to predict y. Discuss the output. Which variable is
an indicator variable? Was it a significant predictor of
y? (Set the alpha level of significance at 0.10.)
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Solutions
Expert Solution
| SUMMARY OUTPUT | ||||||||
| Regression Statistics | ||||||||
| Multiple R | 0.890429 | |||||||
| R Square | 0.792864 | |||||||
| Adjusted R Square | 0.715188 | |||||||
| Standard Error | 44.69014 | |||||||
| Observations | 12 | |||||||
| ANOVA | ||||||||
| df | SS | MS | F | Significance F | ||||
| Regression | 3 | 61158.58 | 20386.19 | 10.20735 | 0.004137 | |||
| Residual | 8 | 15977.67 | 1997.208 | |||||
| Total | 11 | 77136.25 | ||||||
| Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Lower 95.0% | Upper 95.0% | |
| Intercept | 553.8005 | 441.438 | 1.254537 | 0.245058 | -464.157 | 1571.758 | -464.157 | 1571.758 |
| x1 | -28.2439 | 31.8106 | -0.88788 | 0.400513 | -101.599 | 45.11149 | -101.599 | 45.11149 |
| x2 | -6.03901 | 1.471032 | -4.10529 | 0.003413 | -9.43122 | -2.64681 | -9.43122 | -2.64681 |
| x3 | -15.5343 | 23.29371 | -0.66689 | 0.523609 | -69.2496 | 38.18114 | -69.2496 | 38.18114 |
variable x1 is indicator variable, and it is not significant because p value for x1 = 0.400 < α=0.10
X2 is significant predictor
x3 is not significant predictor
orchestra answered 1 year agoRelated Solutions
Given here are the data from a dependent variable and two independent variables. The second independent...
Given here are the data from a dependent variable and two
independent variables. The second independent variable is an
indicator variable with several categories. Hence, this variable is
represented by x2, x3, and
x4. How many categories are there for this
independent
variable? Use a computer to perform a multiple regression analysis
on this data to predict y from the x values.
Discuss the output and pay particular attention to the dummy
variables.
y
x1
x2
x3
x4
11
1.9...
You are given the following information about y and x: y x Dependent Variable Independent Variable...
You are given the following information about y and
x:
y
x
Dependent Variable
Independent Variable
5
15
7
12
9
10
11
7
Calculate the value of SSR, using the
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Suppose we have the following data on variable X (independent) and variable Y (dependent): X Y...
Suppose we have the following data on variable X (independent)
and variable Y (dependent):
X
Y
2
70
0
70
4
130
a.) By hand, determine the simple regression equation relating Y
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d.) Test to see whether X and Y are significantly related using
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level.
e.) Test to see whether X and Y...
Suppose we have the following data on variable X (independent) and variable Y (dependent): X Y...
Suppose we have the following data on variable X (independent)
and variable Y (dependent):
X Y
2 70
0 70
4 130
Test to see whether X and Y are significantly related using a
t-test on the slope of X. Test this at the 0.05 level.
Test to see whether X and Y are significantly related using an
F-test on the slope of X. Test this at the 0.05 level.
Suppose we have the following data on variable X (independent) and variable Y (dependent): X Y...
Suppose we have the following data on variable X (independent)
and variable Y (dependent):
X
Y
2
70
0
70
4
130
a.) By hand, determine the simple regression equation relating Y
and X.
b.) Calculate the R-Square measure and interpret the result.
c.) Calculate the adjusted R-Square.
d.) Test to see whether X and Y are significantly related using
a test on the population correlation. Test this at the 0.05
level.
e.) Test to see whether X and Y...
Suppose we have the following data on variable X (independent) and variable Y (dependent): X Y...
Suppose we have the following data on variable X (independent)
and variable Y (dependent):
X Y
2 70
0 70
4 130
By hand, determine the simple regression equation relating Y
and X.
Calculate the R-Square measure and interpret the result.
Calculate the adjusted R-Square.
Test to see whether X and Y are significantly related using a
test on the population correlation. Test this at the 0.05
level.
Test to see whether X and Y are significantly related using a...
Suppose we have the following data on variable X (independent) and variable Y (dependent): X Y...
Suppose we have the following data on variable X (independent)
and variable Y (dependent):
X
Y
2
70
0
70
4
130
(SOLVE ALL BY HAND, NOT BY USING EXCEL)
By hand, determine the simple regression equation relating Y
and X.
Calculate the R-Square measure and interpret the result.
Calculate the adjusted R-Square.
Test to see whether X and Y are significantly related using a
test on the population correlation. Test this at the 0.05
level.
Test to see whether...
The data shown below for the dependent variable, y, and the independent variable, x, have been...
The data shown below for the dependent variable, y, and the
independent variable, x, have been collected using simple random
sampling.
x
10
13
16
11
20
17
16
13
16
17
y
90
50
30
80
10
10
40
70
20
30
a.
Develop a simple linear regression equation for these data.
b.
Calculate the sum of squared residuals, the total sum of
squares, and the coefficient of determination.
c.
Calculate the standard error of the estimate.
d.
Calculate...
The data shown below for the dependent variable, y, and the independent variable, x, have been...
The data shown below for the dependent variable, y, and the
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160 140 180 190 a. Develop a simple linear regression equation for
these data. b. Calculate the sum of squared residuals, the total
sum of squares, and the coefficient of determination. c. Calculate
the standard error of the estimate. d. Calculate...
The following data for the dependent variable, y, and the independent variable, x, have been collected...
The following data for the dependent variable, y, and the
independent variable, x, have been collected using simple random
sampling:
X
Y
10
120
14
130
16
170
12
150
20
200
18
180
16
190
14
150
16
160
18
200
Construct a scatter plot for these data. Based on the scatter
plot, how would you describe the relationship between the two
variables?
Compute the correlation coefficient.
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