Question

A
multiple linear regression model based on a sample of 24 weeks is
developed to predict standby hours based on the total staff present
and remote hours. The SSR is 31,193.47 and the SSE is 33,923.99.
complete parts a through d.

a. Determine whether there is a significant relationship
between standby hours and the two independent variables at the .05
level of significance.

test statistic

pvalue

state the conclusion

b. Interpret the meaning of the p-value.

c. compute the coefficent of multiple determination r^2 amd
intrepret the meaning

d. compute the adjusted r^2

Answer #1

A multiple linear regression model based on a sample of 17 weeks
is developed to predict standby hours based on the total staff
present and remote hours. The SSR is 20,905.02 and the SSE is
25,434.29. (use 0.05 level of significance)
H0: B1 = B2 = 0
H1: At least one Bj does not equal 0, j = 1, 2
1. Calculate the test statistic.
Fstat= _____
2. Find the p-value.
p-value= _____
3. Compute the coefficient of multiple determination,...

The owner of a movie theater company used multiple regression
analysis to predict gross revenue (y) as a
function of television advertising
(x1) and newspaper
advertising (x2). The
estimated regression equation was
ŷ = 83.8 + 2.26x1 +
1.50x2.
The computer solution, based on a sample of eight weeks,
provided SST = 25.8 and SSR = 23.385.
(a) Compute and
interpret R2 and
Ra2.
(Round your answers to three decimal places.)
The proportion of the variability in the dependent variable that
can be...

A multiple linear regression model with 2 regressors is fit to a
data set of 45 observations. If SSE=220SSE=220 and the FF statistic
for testing the significance of this model is 19.5, then to three
decimal places, the coefficient of multiple determination is
R^2=

1. For the following multiple regression which was
conducted to attempt to predict the variable based on the
independent variables shown, answer the following
questions.
Regression Statistics
Multiple R
0.890579188
R Square
0.793131289
Adjusted R Square
0.7379663
Standard Error
30.28395534
Observations
20
ANOVA
df
SS
MS
F
Regression
4
52743.23074
13185.81
14.37743932
Residual
15
13756.76926
917.1179509
Total
19
66500
Coefficients
Standard Error
t Stat
P-value
Intercept
73.33291
62.25276
1.17799
0.25715
X1
-0.13882
0.05353
-2.59326
0.02037
X2
3.73984
0.95568
3.91328
0.00138...

The owner of a movie theater company used multiple regression
analysis to predict gross revenue (y)
as a function of television advertising
(x1) and newspaper advertising
(x2). The estimated regression
equation was
ŷ = 82.5 + 2.26x1 +
1.30x2.
The computer solution, based on a sample of eight weeks,
provided SST = 25.3 and SSR = 23.415.
(a)Compute and interpret R2
and Ra2.
(Round your answers to three decimal places.)
The proportion of the variability in the dependent variable that...

We have developed a multiple regression model to predict the
number of volunteers for a monthly charity event. One of the
independent variables is Rain: a binary variable equal to 1 if it
rains on the day of the event, and 0 otherwise. In the regression
output, the coefficient for Rain is -22. What does that coefficient
mean, precisely, with regard to the model’s predictions?

You want to construct a multiple linear regression model. The
dependent variable is Y and independent variables are x1 and x2.
The samples and STATA outputs are provided:
Y
X1
X2
3
2
1
4
1
2
6
3
3
6
3
4
7
4
5
STATA
Y
Coef.
Std. Err.
t
P> abs. value (t)
95% confidence interval
X1
0.25
0.4677072
0.53
0.646
-1.762382 , 2.262382
X2
0.85
0.3372684
2.52
0.128
-.601149 , 2.301149
_cons
2
0.7245688
2.76
0.110...

You want to construct a multiple linear regression model. The
dependent variable is Y and independent variables are x1 and x2.
The samples and STATA outputs are provided:
Y
X1
X2
3
2
1
4
1
2
6
3
3
6
3
4
7
4
5
STATA
Y
Coef.
Std. Err.
t
P> abs. value (t)
95% confidence interval
X1
0.25
0.4677072
0.53
0.646
-1.762382 , 2.262382
X2
0.85
0.3372684
2.52
0.128
-.601149 , 2.301149
_cons
2
0.7245688
2.76
0.110...

A number of different multiple
regression equations could be developed to estimate the percentage
of alumni that make a donation. The following estimated regression
equation using just two independent variables, Graduation Rate and
Student/Faculty Ratio, is shown below.
From this model, if universities want
to increase the percentage of alumni who make a donation, what is
your recommendation?
Regression Statistics
Multiple R
0.8364
R Square
0.6996
Adjusted R Square
0.6863
Standard Error
7.5284
Observations
48
ANOVA
df
SS
MS
F...

A microcomputer manufacturer has developed a regression model
relating his sales (Y in $10,000s) with three independent
variables. The three independent variables are price per unit
(Price in $100s), advertising (ADV in $1,000s) and the number of
product lines (Lines). Part of the regression results is shown
below.
Coefficient
Standard Error
Intercept
1.0211
22.8752
Price (X1)
-.1523
-.1411
ADV (X2)
.8849
.2886
Lines(X3)
-.1463
1.5340
Source
D.F.
S.S.
Regression
3
2708.651
Error
14
2840.51
Total
17
5549.12
(a) What has...

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