Question

Based on this study, what is the final model you would recommend to the Head of the Science Department?

Comment on the overall adequacy of the final model.

DATA 1:

SUMMARY OUTPUT | ||||||||

Multiple R | 0.48301716 | |||||||

R Square | 0.23330558 | |||||||

Adjusted R Square | 0.21210666 | |||||||

1.18199152 | ||||||||

224 | ||||||||

ANOVA | ||||||||

df | SS | MS | F | Significance F | ||||

6 | 92.2552908 | 15.3758818 | 11.0055388 | 1.0596E-10 | ||||

217 | 303.171559 | 1.39710396 | ||||||

223 | 395.42685 | |||||||

Coefficients | t Stat | P-value | Lower 95% | Upper 95% | ||||

Intercept | 1.81105553 | 0.64338389 | 2.81489099 | 0.00532825 | 0.54297399 | 3.07913706 | 0.54297399 | 3.07913706 |

HS_SCI | 0.09600825 | 0.10389727 | 0.92406901 | 0.35647679 | -0.1087687 | 0.30078523 | -0.1087687 | 0.30078523 |

HS_ENG | 0.05585575 | 0.10411875 | 0.53646199 | 0.59218883 | -0.1493577 | 0.26106925 | -0.1493577 | 0.26106925 |

HS_MATH | 0.26024847 | 0.10209913 | 2.54897824 | 0.01149448 | 0.05901554 | 0.46148139 | 0.05901554 | 0.46148139 |

U | -0.3967997 | 0.18116867 | -2.1902223 | 0.02957342 | -0.7538752 | -0.0397241 | -0.7538752 | -0.0397241 |

Gender | -0.0978474 | 0.17959186 | -0.5448323 | 0.58642836 | -0.4518151 | 0.25612026 | -0.4518151 | 0.25612026 |

ATAR | -0.0049033 | 0.02658113 | -0.1844651 | 0.85382087 | -0.0572935 | 0.04748696 | -0.0572935 | 0.04748696 |

Answer #1

From the above result, we can conclude, that as the multiple R-sq is 0.48, the full model can explain the 48% of total variability.

BEST MODEL:

we will use the p-value concepts for selecting the best model. we know that the if the associated p-value is less than 0.05 then the variable is a significant predictor.

Here the variable HS_MATH & U, these two variables are significant. our final model will be:

Predicted= 1.81105553+0.26024847*HS_MATH - 0.3967997*U

Based on this study, what is the final model you would recommend
to the Head of the Science Department? And Comment on the overall
adequacy of the final model.
DATA 1
HS_SCI
HS_ENG
HS_MATH
U
Gender
ATAR
SUMMARY OUTPUT
Multiple R
0.48301716
R Square
0.23330558
Adjusted R Square
0.21210666
1.18199152
224
ANOVA
df
SS
MS
F
Significance F
6
92.2552908
15.3758818
11.0055388
1.0596E-10
217
303.171559
1.39710396
223
395.42685
Coefficients
t Stat
P-value
Lower 95%
Upper 95%
Intercept
1.81105553
0.64338389
2.81489099...

Discuss the model and interpret the results: report overall
model fit (t and significance), report the slope coefficient and
significance, report and interpret r squared.
Regression
Statistics
Multiple R
0.001989374
R Square
3.95761E-06
Adjusted R
Square
-0.005046527
Standard Error
8605.170404
Observations
200
ANOVA
df
SS
MS
F
Significance
F
Regression
1
58025.4985
58025.4985
0.00078361
0.977695901
Residual
198
14661693620
74048957.68
Total
199
14661751645
Coefficients
Standard Error
t
Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
15668.85874
2390.079111
6.555790838...

Solve the missing values in the following regression model.
Write down all solutions along with their key letter.
Regression Statistics
Multiple R
0.489538
R Square
0.239648
Adjusted R Square
0.231889
Standard Error
11.76656
Observations
100
ANOVA
df
SS
MS
F
Significance F
Regression
1
4276.457
30.88765
2.35673E-07
Residual
138.452
Total
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 99.0%
Upper 99.0%
Intercept
-24.1551
12.83013
-1.88268
0.062709
-49.61605579
1.305895
-57.8589
9.548787
Food
3.167042
0.569851
2.36E-07
2.03619109
4.297893
1.670083...

Calculate the following statistics given the existing
information (1 point per calculation):
Regression Statistics
Multiple R
R Square
Adjusted R Square
0.559058
Standard Error
Observations
30
ANOVA
df
SS
MS
F
Significance F
Regression
2
3609132796
19.38411515
6.02827E-06
Residual
27
2513568062
Total
29
6122700857
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept
-15800.8
57294.51554
-0.27578
0.784814722
CARAT
12266.83
1999.250369
6.135715
1.48071E-06
DEPTH
156.686
928.9461882
0.168671
0.867312915
Additionally interpret your results. Be sure to comment on
Accuracy, significance...

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...

In models B through D, what seems to be the relationship between
the burglary rate and the percent of the 18-64 population who are
young adults (18-24)?
Select one:
a. It is difficult to describe the relationship; the young adult
variables were all significant at 5% in models B, C, and D, but the
signs and sizes of the coefficients were very different between
models.
b. Conclusions about the relationship between young adults and
the burglary rate are difficult to...

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...

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...

Regression Statistics
Multiple
R
0.3641
R
Square
0.1325
Adjusted
R Square
0.1176
Standard
Error
0.0834
Observations
60
ANOVA
df
SS
MS
F
Significance F
Regression
1
0.0617
0.0617
8.8622
0.0042
Residual
58
0.4038
0.0070
Total
59
0.4655
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept
-0.0144
0.0110
-1.3062
0.1966
-0.0364
0.0077
X
Variable 1
0.8554
0.2874
2.9769
0.0042
0.2802
1.4307
How do you interpret the above table?

Consider the following output from the model that relates
proportion of household’s budget spent on beverages (WBEV) to total
expenditure (TE), age of head of the household, and number of
children in the household (NK) with a sample size of 144.
Regression Analysis: WBEV versus TE, AGE, NK:
SUMMARY OUTPUT
Regression
statistics
Multiple R
( )
R Square
( )
Adjusted R Square
0.3837
std
( )
Observations
144
variance analysis
df
SS
MS
F
Significance F
regression analysis
(...

ADVERTISEMENT

Get Answers For Free

Most questions answered within 1 hours.

ADVERTISEMENT

asked 2 minutes ago

asked 32 minutes ago

asked 33 minutes ago

asked 54 minutes ago

asked 55 minutes ago

asked 1 hour ago

asked 1 hour ago

asked 1 hour ago

asked 1 hour ago

asked 1 hour ago

asked 1 hour ago

asked 2 hours ago