Homework: Chapter 14 HW Score: 0.45 of 1 pt 14.1.7-T Hw Score: 66.52%, 2.66 of 4
ID: 3315477 • Letter: H
Question
Homework: Chapter 14 HW Score: 0.45 of 1 pt 14.1.7-T Hw Score: 66.52%, 2.66 of 4 pt mvoned-an, mctvevi and wi at factors were related to standby hours A study of standby hours he a ng table was conducted for 26 weeks. The variablos in the study are described below and the data from the study are shown i in which unionized graphic artists at the station are paid but are not actualy g below weeks. Standby hours (Y-Total number of standby hours in a week Total staff present (x1)-Weeky total of Click the icon to view the data table 330.(18)x(-0.1)X2 (Round to one decimal place as needed.) b. Interpeet the meaning of the slopes, by and b2, in this problem. Choose O A For a given number of remote t the coect answer below respectively in remote hours of by and by units, number of remote hours, each increase of one unit of total stafl present is estimated to resuit in a mean increase in standby hours of b, units For a gven rumber of totl st ncrease of one unit in remmote hours is estmated to result in a mean decrease in standby hours of the ateolute value of by units Each increase in standby hours is estimated to result in a mean increase in total staff present of b units and a mean decrease in remote hours of the absolute value of b units D. The slopes, by and by, cannot be intorpneted individuaily. why the regression coefficient, bo,has no practical The ent to has no practcai meaning n this context because it estimates the standby hours when toro are ro staff present and no rerole hours. B. The ooefficient bo has ro practical meaning in this context because i correspods to the number of staf present C The coficlent bo has no pracical meaning in this contest because Y depends on not only bg, but by and b as weitl and their mearing cannot be saparated D. The coeffiont bg has no pracical meaning in this context because it is not close in value to any of the data values in the standby hours coum and the remote hours when there are no standby hours. Clear AlExplanation / Answer
Result:
e). 95% confidence interval =(141.76, 179.89)
Regression Analysis
R²
0.490
Adjusted R²
0.446
n
26
R
0.700
k
2
Std. Error
35.386
Dep. Var.
y
ANOVA table
Source
SS
df
MS
F
p-value
Regression
27,664.1901
2
13,832.0950
11.05
.0004
Residual
28,800.4253
23
1,252.1924
Total
56,464.6154
25
Regression output
confidence interval
variables
coefficients
std. error
t (df=23)
p-value
95% lower
95% upper
Intercept
-330.6785
116.4766
-2.839
.0093
-571.6288
-89.7283
x1
1.7650
0.3790
4.657
.0001
0.9809
2.5492
x2
-0.1391
0.0589
-2.364
.0269
-0.2609
-0.0174
Predicted values for: y
95% Confidence Interval
95% Prediction Interval
x1
x2
Predicted
lower
upper
lower
upper
Leverage
310
400
160.829
141.763
179.895
85.185
236.473
0.068
Regression Analysis
R²
0.490
Adjusted R²
0.446
n
26
R
0.700
k
2
Std. Error
35.386
Dep. Var.
y
ANOVA table
Source
SS
df
MS
F
p-value
Regression
27,664.1901
2
13,832.0950
11.05
.0004
Residual
28,800.4253
23
1,252.1924
Total
56,464.6154
25
Regression output
confidence interval
variables
coefficients
std. error
t (df=23)
p-value
95% lower
95% upper
Intercept
-330.6785
116.4766
-2.839
.0093
-571.6288
-89.7283
x1
1.7650
0.3790
4.657
.0001
0.9809
2.5492
x2
-0.1391
0.0589
-2.364
.0269
-0.2609
-0.0174
Predicted values for: y
95% Confidence Interval
95% Prediction Interval
x1
x2
Predicted
lower
upper
lower
upper
Leverage
310
400
160.829
141.763
179.895
85.185
236.473
0.068
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