Question

SUMMARY OUTPUT Regression Statistics Multiple R 0.440902923 R Square 0.194395388 Adjusted R Square 0.165100675 Standard Error 0.428710255 Observations 115 ANOVA df SS MS F Significance F Regression 4 4.878479035 1.219619759 6.635852231 8.02761E-05 Residual 110 20.21717314 0.183792483 Total 114 25.09565217 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 0.321875686 0.323939655 0.99362854 0.322584465 -0.320096675 0.963848047 -0.320096675 0.963848047 Gender -0.307211858 0.082630734 -3.717888514 0.000317832 -0.470966578 -0.143457137 -0.470966578 -0.143457137 Age 0.000724105 0.091134233 0.007945479 0.993674883 -0.179882553 0.181330763 -0.179882553 0.181330763 GPA 0.171984622 0.05279787 3.257415886 0.001495536 0.067351635 0.276617608 0.067351635 0.276617608 Total Q 0.000260423 0.003407326 0.076430415 0.939215512 -0.006492097 0.007012944 -0.006492097 0.007012944

The data set is a study of student persistent enrolling in the next semester based on Gender, Age, GPA, a 22 questionnaire on self-efficacy, and student enrollment status.The educational researcher wants to study the relationship between student enrollment status as it relates to gender, age, GPA, and the total response to a 22 questionnaire survey. 2. The estimated multiple regression analysis equation. 3. Does the model work? 4. How well does the model work? 5. Which variables contribute to the model? 6. General interpretation of the data and the data analysis

Answer #1

2) the estimated multiple regression model is, y=0.322-0.307gender+0.000724age+0.172GPA+0.000260423total q

3) there is a linear relation between the regressors to explain the response variable, the model may work.

4) the model won't work well, as the r sq and adjusted r sq is very small, i.e. the total variability of the respornse variable is only 44.09% explained by the explanatory variables.

5) from p values of the independent variable we can say that, gender and GPA are the variables that contributes to the model

6) there exists regressors which are insignificant, so the model is not good. It won't be a good predictive model as r sq is very small

SUMMARY OUTPUT Regression Statistics Multiple R 0.84508179 R
Square 0.714163232 Adjusted R Square 0.704942691 Standard Error
9.187149383 Observations 33 ANOVA df SS MS F Significance F
Regression 1 6537.363661 6537.363661 77.4535073 6.17395E-10
Residual 31 2616.515127 84.40371378 Total 32 9153.878788
Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
Lower 95.0% Upper 95.0% Intercept 61.07492285 3.406335763
17.92980114 6.41286E-18 54.12765526 68.02219044 54.12765526
68.02219044 Time (Y) -0.038369095 0.004359744 -8.800767426
6.17395E-10 -0.047260852 -0.029477338 -0.047260852 -0.029477338
Using your highlighted cells, what is the equation...

SUMMARY OUTPUT
Regression Statistics
Multiple R
0.909785963
R
Square
0.827710499
Adjusted R Square
0.826591736
Standard Error
7.177298036
Observations
156
ANOVA
df
SS
MS
F
Significance F
Regression
1
38112.05194
38112.05194
739.8443652
1.09619E-60
Residual
154
7933.095493
51.5136071
Total
155
46045.14744
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
8.67422449
2.447697434
3.543830365
0.000522385
3.838827439
13.50962154
3.838827439
13.50962154
X
Variable 1
0.801382837
0.029462517
27.20008024
1.09619E-60
0.743179986
0.859585688
0.743179986
0.859585688
(d)
How much of the variation in...

SUMMARY OUTPUT
Regression Statistics
Multiple
R
0.231960777
R
Square
0.053805802
Adjusted
R Square
0.034093423
Standard
Error
5272.980333
Observations
50
ANOVA
df
SS
MS
F
Significance F
Regression
1
75893113.09
75893113.09
2.729543781
0.105035125
Residual
48
1334607437
27804321.59
Total
49
1410500550
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 99.0%
Upper 99.0%
Intercept
6396.894057
3281.342486
1.949474669
0.057094351
-200.6871963
12994.47531
-2404.335972
15198.12409
HSRANK
64.68225855
39.15075519
1.6521331
0.105035125
-14.03561063
143.4001277
-40.32805468
169.6925718
a. According to your estimate, what is the predicted...

SUMMARY OUTPUT
Regression Statistics
Multiple R
0.870402
R
Square
0.7576
Adjusted R Square
0.68488
Standard Error
1816.52
Observations
27
ANOVA
df
SS
MS
F
Significance F
Regression
6
2.06E+08
34376848
10.41804
2.81E-05
Residual
20
65994862
3299743
Total
26
2.72E+08
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
-4695.4
12622.97
-0.37197
0.713825
-31026.5
21635.66
-31026.5
21635.66
AGE
161.7028
126.5655
1.277621
0.216015
-102.308
425.7137
-102.308
425.7137
MILAGE
-0.03441
0.023186
-1.4842
0.153346
-0.08278
0.013953
-0.08278
0.013953...

SUMMARY OUTPUT
Regression Statistics
Multiple R
0.993709623
R Square
0.987458816
Adjusted R Square
0.987378251
Standard Error
514.2440271
Observations
471
ANOVA
df
SS
MS
F
Significance F
Regression
3
9723795745
3241265248
12256.7707
0
Residual
467
123496711.4
264446.9194
Total
470
9847292456
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
-267.1127974
42.01832073
-6.357055513
4.8988E-10
-349.68118
-184.54441
-349.68118
-184.54441
Fuel cost (000,000)
0.449917223
0.098292092
4.577349137
6.0451E-06
0.25676768
0.64306676
0.25676768
0.64306676
Salary (000,000)
-0.327915884
0.188252958
-1.741889678
0.08218614
-0.6978436...

SUMMARY OUTPUT
Regression Statistics
Multiple R
0.884651238
R Square
0.782607814
Adjusted R Square
0.601447658
Standard Error
25.32612538
Observations
12
ANOVA
df
SS
MS
F
Significance F
Regression
5
13854.44091
2770.888181
4.319977601
0.051673038
Residual
6
3848.475761
641.4126268
Total
11
17702.91667
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
-53.17436031
42.95203957
-1.237993838
0.261960445
-158.274215
51.92549434
-158.274215
51.92549434
Advertising ($1000s)
2.050813091
0.763960482
2.684449181
0.036320193
0.181469133
3.92015705
0.181469133
3.92015705
t (quarters)
-4.047065728
2.779316427
-1.456137088
0.19560701
-10.84780803
2.753676575...

SUMMARY OUTPUT
Regression Statistics
Multiple R
0.881644384
R Square
0.77729682
Adjusted R Square
0.767919844
Standard Error
2.046234994
Observations
100
ANOVA
df
SS
MS
F
Significance F
Regression
4
1388.337623
347.0844058
82.89418891
3.94359E-30
Residual
95
397.7723769
4.187077651
Total
99
1786.11
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
30.46621607
3.539611332
8.607220742
1.55786E-13
23.43919912
37.49323302
23.43919912
37.49323302
Engine size
-0.026439837
0.008914999
-2.965769936
0.003818268
-0.044138349
-0.008741326
-0.044138349
-0.008741326
Compression Ratio
0.364901894
0.056081385
6.506649162
3.58903E-09
0.253566269
0.476237519...

Regression Statistics
Multiple
R
0.710723
R
Square
0.505127
Adjusted
R Square
0.450141
Standard
Error
1.216847
Observations
21
ANOVA
df
SS
MS
F
Significance F
Regression
2
27.20518
13.60259
9.186487
0.00178
Residual
18
26.65291
1.480717
Total
20
53.8581
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
58.74307
12.66908
4.636728
0.000205
32.12632
85.35982
32.12632
85.35982
High
School Grad
-0.00133
0.000311
-4.28236
0.000448
-0.00198
-0.00068
-0.00198
-0.00068
Bachelor's
-0.00016
5.46E-05
-3.00144
0.007661
-0.00028
-4.9E-05
-0.00028
-4.9E-05...

Dep.=
Mileage
Indep.=
Cylinders
SUMMARY OUTPUT
Regression Statistics
Multiple R
R Square
Adjusted R Square
Standard Error
Observations
7.0000
ANOVA
Significance
df
SS
MS
F
F
Regression
12.4926
Residual
Total
169.4286
Standard
Coefficients
Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept
38.7857
Cylinders
-2.7500
SE
CI
CI
PI
PI
Predicted
Predicted
Lower
Upper
Lower
Upper
x0
Value
Value
95%
95%
95%
95%
4.0000
1.9507
6.0000
1.1763
Is there a relationship between a car's gas
MILEAGE (in miles/gallon) and its...

Dep.=
Mileage
Indep.=
Length
SUMMARY OUTPUT
Regression Statistics
Multiple R
R Square
Adjusted R Square
Standard Error
Observations
7.0000
ANOVA
Significance
df
SS
MS
F
F
Regression
6.1135
Residual
Total
169.4286
Standard
Coefficients
Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept
80.0094
Length
-0.3047
SE
CI
CI
PI
PI
Predicted
Predicted
Lower
Upper
Lower
Upper
x0
Value
Value
95%
95%
95%
95%
175.0000
2.3108
210.0000
2.9335
Is there a relationship between a car's gas
MILEAGE (in miles/gallon) and its...

ADVERTISEMENT

Get Answers For Free

Most questions answered within 1 hours.

ADVERTISEMENT

asked 4 minutes ago

asked 23 minutes ago

asked 34 minutes ago

asked 34 minutes ago

asked 36 minutes ago

asked 39 minutes ago

asked 43 minutes ago

asked 46 minutes ago

asked 52 minutes ago

asked 58 minutes ago

asked 1 hour ago

asked 1 hour ago