Question

Please explain me this problem without using Excel please. Thank you.

df | ss | ms | f | significance f | |

regression | 1 | 552.0 | 552.0 | 69.0 | 0.0000 |

residual(error) | 10 | 80.0 | 8.0 | ||

total | 11 | 632.0 |

Coefficients | Standard Error | t Stat | P-Value | |

Intercept | 4.3939 | 1.7569 | 2.5009 | 0.0314 |

X | 1.9650 | 0.2387 | 8.2315 | 0.0000 |

Answer the following questions based on the above information and use a 95% confidence.

a. Is the regression model significant at 95% confidence? Why or why not. Fully explain.

b. Is X significant? Why or why not. Fully explain.

c. Compute the value of R-square.

d. Determine the multiple R.

e. Compute the standard error.

f. What has been the sample size for this problem?

Answer #1

**Answers**

a. Is the regression model significant at 95% confidence? Why or why not. Fully explain.

F-statistic = 69.0

P-value = 0.0000

Since the p-value is less than 0.05, we reject the null hypothesis and conclude that the regression model is significant.

b. Is X significant? Why or why not. Fully explain.

t-statistic = 8.2315

P-value = 0.0000

Since the p-value is less than 0.05, we reject the null hypothesis and conclude that X is significant.

c. Compute the value of R-square.

R-square = SSR/SST = 552.0/632.0 = 0.8734

d. Determine the multiple R.

Multiple R = SQRT (0.8734) = 0.9346

e. Compute the standard error.

Standard error = SQRT (MSE) = SQRT (8.0) = 2.8284

f. What has been the sample size for this problem?

Sample size = Total df + 1 = 11 + 1 = 12

Multiple R=0.81112189
R Square=0.65791872
Adj. R Square=0.61515856
Standard Error=11.6506589
Observations=10
Regression: df=1 ss=2088.497175 ms=2088.497 F=15.3863
Residual: df=8 ss=1085.902825 ms=135.7379
Total: df=9 ss=3174.4
Intercept: Coefficients=-70.39 Std. Error=30.00
P-value=0.047
X: Coefficients=17.18 Std. Error=4.38 t-stat=3.92 p
value=0.004
Question: For the test of hypothesis regarding
the intercept of the model, compute and report the
calculated value of the test-statistic.
Question: Predict value of Y, when X = 10.

df
SS
MS
F
Significance F
Regression
3
156.4823
52.16077
28.01892
0.000002177
Residual
26
48.4023
1.861627
Total
29
204.8846
Coefficients
P-value
Intercept
23.8163
9.24E-07
Price
-0.3035
0.001925
Price other
-0.342937
0.112442
Income
0.23406
0.033889
A) What is the percent risk of the coefficients really being
zero? In other words, are the individual coefficients statistically
significant using the 95 percent confidence level?
B) Using the regression summery, compute R2
and interpret its meaning.
C) Is the “Price other” coefficient referring to...

A regression analysis relating a company’s sales, their
advertising expenditure, price, and time resulted in the
following.
Regression Statistics
Multiple R
0.8800
R Square
0.7744
Adjusted R Square
0.7560
Standard Error
232.29
Observations
25
ANOVA
df
SS
MS
F
Significance F
Regression
3
53184931.86
17728310.62
328.56
0.0000
Residual
21
1133108.30
53957.54
Total
24
54318040.16
Coefficients
Standard Error
t Stat
P-value
Intercept
927.23
1229.86
0.75
0.4593
Advertising (X1)
1.02
3.09
0.33
0.7450
Price (X2)
15.61
5.62
2.78
0.0112
Time (X3)
170.53...

df
SS
MS
F
Regression
A
8,693.11
4,346.56
375.16
Residual
22
254.89
C
Total
24
B
Coefficients
Standard Error
t-stat
p-value
Intercept
257.74
22.82
11.29
1.27E-10
x1
-2.97
0.47
-6.31
2.37E-06
x1
0.23
0.24
0.96
0.3454
a. What is the sample regression equation?
b. Interpret the slope coefficient for
x1.
c. Find the predicted value for y if
x1 equals 25 and x2 equals
50.
d. Fill in the missing values in the ANOVA table.
e. Calculate the standard error...

Discuss the strength and the significance of your regression
model by using R-square and significance F where α = 0.05.
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.919011822
R
Square
0.844582728
Adjusted R Square
0.834446819
Standard Error
163.953479
Observations
50
ANOVA
df
SS
MS
F
Significance F
Regression
3
6719578.309
2239859.44
83.3257999
1.28754E-18
Residual
46
1236514.191
26880.7433
Total
49
7956092.5
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
21.7335244
114.2095971
0.19029508
0.84991523
-208.158471
251.62552...

Using the following information:
Coefficients
Intercept
-12.8094
Independent variable
2.1794
ANOVA
df
SS
MS
F
Regression
1
12323.56
12323.56
90.0481
Residual
8
1094.842
136.8550
Total
9
13418.4
The regression equation is _______.
Multiple Choice
Ŷ = 2.1794 − 12.8094X
Ŷ = −12.8094 + 2.1794X
12.8094X = 2.1794Ŷ
X = −12.8094 + 2.1794 Ŷ

Use Excel to develop a regression model for the Hospital
Database (using the “Excel Databases.xls” file on Blackboard) to
predict the number of Personnel by the number of Births. Perform a
test of the slope. What is the value of the test statistic? Write
your answer as a number, round your answer to 2 decimal places.
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.697463374
R
Square
0.486455158
Adjusted R Square
0.483861497
Standard Error
590.2581194
Observations
200
ANOVA
df
SS
MS
F...

Compare the two regression models. Does it make sense that
spending and household debt could each be predicted by annual
household income? Why or why not?
1. Predicting spending by household income
Regression
Statistics
Multiple R
0.859343186
R Square
0.738470711
Adjusted R
Square
0.737149856
Standard Error
1602.157625
Observations
200
ANOVA
df
SS
MS
F
Significance
F
Regression
1
1435121315
1435121315
559.085376
1.42115E-59
Residual
198
508247993.2
2566909.056
Total
199
1943369308
Coefficients
Standard Error
t
Stat
P-value
Lower 95%
Upper 95%
Lower...

According to the Data, is the regression a better fit than the
one with the Dummy variable, explain?
Regression Statistics
Multiple R
0.550554268
R Square
0.303110002
Adjusted R Square
0.288887757
Standard Error
2.409611727
Observations
51
ANOVA
df
SS
MS
F
Significance F
Regression
1
123.7445988
123.7445988
21.31238807
2.8414E-05
Residual
49
284.5052051
5.806228676
Total
50
408.2498039
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Intercept
5.649982553
1.521266701
3.713998702
0.000522686
2.592882662
U-rate
1.826625993
0.395670412
4.616534206
2.84144E-05
1.0314965
Multiple R
0.572568188
R Square...

ANOVA for SLR
source
df
ss
ms
F
regression
1
14.4
14.4
27
Error
3
1.6
0.533333
Total
4
16
R^2=0.9. n=5(there are five x and five y in original table)
1.Construct a 95% confidence interval of
B1(beta1),B1(beta1=-1.2)
2.Does the average value of y change with x? Assume
a(alpha)=0.05
question 2 is much important plz tell me, thx

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