USA: +1(669)289-8683 
Sign in
CAN ANY OF YOU ANSWER THEM CORRECTLY , REPLY ASAP.
>ONLINE TEST irst name:
ase Sales
7 32 deviate from the predicted values (or from the regression line) by ______ million cases
Statistics
6 F 5 ∑(ŷ − y̅)² = 668,201.43 (∑xy − nx̅y̅)/(∑x² − nx̅²) =
√MSE
SSE/(n − 2)
∑(y − ŷ)² =
/SST
∑(ŷ − y̅)² =
668,201.43 ∑(y − y̅)² =
se(e)/√∑(x − x̅)² =
b₁/se(b₁)
SSR 160.071587 2.25E-04 0.0002 R² = SSR/SST b₀ + b₁x
617.662 se(e) = √MSE b₁ − MOE
tα/2,df se(b₁)
se(b₁) = 0.692 H₀: β₁ = 0 H₁: β₁ ≠ 0 b₁ = 8.76
7
Last name:
F
Enter your LETTER answers HERE
↓
1
2
3
4
5
6
7
8
9
1
0
Next 5 questions are based on the following data relating cases sold of the brand of soft drink to media expenditure.
Media Expenditure
C
($millions)
(millions)
Use the following calculations in the relevant formulas to answer the questions
65
980
46
960
n =
32
700
x̅ =
28
345
y̅ =
520
25
360
∑xy =
153,3
10
∑(y − y̅)² =
773,450
18
180
∑x² =
9,198
∑(y − ŷ)² =
105,248.57
10
115
∑(x − x̅)² =
2,030
∑(ŷ − y̅)² =
668,201.43
1)
The estimated regression equation predicts that for each additional $1 million in media expenditure, the case sales would increase by ______ million.
A
9.6
B
12.8
C
14.6
D
18.1
2)
The regression model shows that on average observed values of
Case Sales
A
145.1
B
135.2
C
126.5
D
118.3
3)
The regression model indicates that ______% of variations in case sales is explained by media expenditure
A
95.7
B
92.6
C
86.4
D
82.4
4)
To build a confidence interval for the slope coefficient b₁ the standard error of b₁, se(b₁) is
A
3.22
B
2.89
C
1.99
D
1.26
5)
To perform a test of hypothesis that the population slope parameter is zero, the test statistic t is
A
6.52
B
5.63
C
4.68
D
3.89
Use the following Excel regression output to answer the next 5 questions. The output shows the result of running a regression relating costs to production volume. Fill in the highlighted cells first.
SUMMARY OUTPUT
Regression
Multiple R
0.9877
R Square
Adjusted R Square
0.9695
Standard Error
Observations
ANOVA
df
SS
MS
Signif F
Regression 1
160.071587
2.25E-04
Residual
184775.04
Total
7579083.33
Coefficients
Std Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept
617.662
428.31
1.442
0.2227
-571.51
1806.84
X Variable 1
8.755
0.0002
6)
The percentage of the variations in cost explained by production volume is:
A
97.6%
B
93.0%
C
90.3%
D
87.7%
7)
The predicted total cost when production volume is 1,000 is,
A
8,581
B
8,827
C
9,373
D
9,670
8)
Given that the sum of the squared deviations of production volume is 96,470.83, the standard error of the slope coefficient is
A
1.280
B
1.084
C
0.888
D
0.692
9)
The lower end of the 95% confidence interval for the slope coefficient is
A
5.16
B
6.83
C
8.98
D
9.49
10)
The value of the t Stat for the slope coefficient is
A
12.65
B
10.17
C
7.69
D
5.21
K
0
NAME:
D 0 Next 5 questions are based on the following data relating cases sold of the brand of soft drink to media expenditure.
A 0
C 0 Media Expenditure Case Sales
A 0 ($millions) (millions)
B 0 65 980
A 0 46 960
C 0 32 700
D 0 28 345
B 0 25 360
A 0 18 180
10 115
Use the following calculations in the relevant formulas to answer the questions
n = 7 ∑(x − x̅)² = 2,030
x̅ = 32 ∑(y − y̅)² = 773,450
y̅ = 520 ∑(y − ŷ)² = 105,248.57
∑xy =
153,310
∑x² = 9,198
1) The estimated regression equation predicts that for each additional $1 million in media expenditure, the case sales would increase by ______ million.
A 9.6
B 12.8
C 14.6
D 18.1
b₁ =
18.14
2)
The regression model shows that on average observed values of Case Sales deviate from the predicted values (or from the regression line) by ______ million cases
A 145.1
B 135.2
C 126.5
D 118.3
se(e) =
MSE =
SSE =
105248.571428572
MSE =
21049.7142857144
se(e) = 145.1
3) The regression model indicates that ______% of variations in case sales is explained by media expenditure
A 95.7
B 92.6
C 86.4
D 82.4
R² =
SSR
SSR =
SST =
773,450.00
R² =
86.4%
4) To build a confidence interval for the slope coefficient b₁ the standard error of b₁, se(b₁) is
A 3.22
B 2.89
C 1.99
D 1.26
se(b₁) =
3.220
5) To perform a test of hypothesis that the population slope parameter is zero, the test statistic t is
A 6.52
B 5.63
C 4.68
D 3.89
H₀: β₁ = 0
H₁: β₁ ≠ 0
TS =
b₁ = 18.14
se(b₁) = 3.220
TS = 5.63
Use the following Excel regression output to answer the next 5 questions. The output shows the result of running a regression relating costs to production volume. Fill in the highlighted cells first.
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.9877
R Square
0.9756
R² = SSR/SST
Adjusted R Square 0.9695
Standard Error
214.93
se(e) = √MSE
Observations 6
ANOVA
df SS MS F Signif F
Regression 1
7394308.29
Residual 4 184775.04
46193.76
MSE = SSE/(n − 2)
Total 5 7579083.33
Coefficients Std Error t Stat P-value Lower 95% Upper 95%
Intercept 617.662 428.31 1.442 0.2227 -571.51 1806.84
X Variable 1 8.755
0.6920
12.6520966579
6.834
6) The percentage of the variations in cost explained by production volume is:
A 97.6%
B 93.0%
C 90.3%
D 87.7%
SSR = SST − SSE
SST = 7579083.33
SSE = 184775.04
SSR = 7394308.29
R² = SSR/SST =
97.56%
7) The predicted total cost when production volume is 1,000 is,
A 8,581
B 8,827
C 9,373
D 9,670
ŷ =
b₀ =
b₁ = 8.755
x =
1000
ŷ =
9373
8) Given that the sum of the squared deviations of production volume is 96,470.83, the standard error of the slope coefficient is
A 1.280
B 1.084
C 0.888
D 0.692
se(b₁) = se(e)/√∑(x − x̅)²
MSE = SSE/(n − 2)
SSE = 184775.04 MSE = 46193.76
se(e) =
214.9273365582
∑(x − x̅)² =
96470.83
se(b₁) = 0.692
9) The lower end of the 95% confidence interval for the slope coefficient is
A 5.16
B 6.83
C 8.98
D 9.49
L =
MOE =
tα/2,df = t0.025,4 =
2.776
MOE =
1.92
b₁ =
8.76
L = 6.83
10) The value of the t Stat for the slope coefficient is
A 12.65
B 10.17
C 7.69
D 5.21
TS = b₁/se(b₁)
se(b₁) = 0.692 TS = 12.65
