1) Is/are there Independent Variable/s that can predict the Dependent Variable? Explain the result of the Regression Analysis (refer th the attach photos) 2) Where there IVs found that are not predictors of the DV? What could be the reason? Base your answer on the p-value and Adjusted R Square.
1) Is/are there Independent Variable/s that can predict the Dependent Variable? Explain the result of the Regression Analysis (refer th the attach photos) 2) Where there IVs found that are not predictors of the DV? What could be the reason? Base your answer on the p-value and Adjusted R Square.
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question
100%
1) Is/are there Independent Variable/s that can predict the Dependent Variable? Explain the result of the
2) Where there IVs found that are not predictors of the DV? What could be the reason? Base your answer on the p-value and Adjusted R Square.

Transcribed Image Text:Predicted Value
Std. Predicted Value
Standard Error of Predicted Value
Adjusted Predicted Value
Residual
Residuals Statisticsª
Minimum Maximum Mean Std. Deviation N
114.8627 249.3678 1.8923E2
33.26412 26
1.000 26
3.653 26
Regression Standardized Predicted
Value
-2.236
1.808
.000
7.238
19.886 11.474
114.8193 260.1165 1.8851E2
-7.92405E1 61.01555
Std. Residual
Stud. Residual
Deleted Residual
Stud. Deleted Residual
Mahal. Distance
Cook's Distance
Centered Leverage Value
a. Dependent Variable: Production Output
1.724
2.002
-8.27001E1 82.29171
2.155
6.933
466
277
-2.239
-2.288
-2.546
084
.000
003
Scatterplot
O
Dependent Variable: Production Output
O
00
.00000
.000
010
@
.72087
015
Regression Standardized Residual
1.923
055
077
O
34.23423 26
33.94235 26
959 26
1.029 26
39.25609 26
1.083 26
1.879 26
105 26
075 26

Transcribed Image Text:Model Variables Entered Variables Removed
Number of Staff
1
2
Idle Hours
a. Dependent Variable: Production Output
Model
1
2
a. Predictors: (Constant), Number of Staff
b. Predictors: (Constant), Number of Staff, Idle Hours
c. Dependent Variable: Production Output
Model
1
ANOVAC
Sum of Squares df Mean Square F Sig.
20667.398 1
20667.398 13.856 | .001 a
1491.551
35797.217 24
56464.615 25
27662.543 2
2 Regression
Residual
28802.073 23
Total
56464.615 25
a. Predictors: (Constant), Number of Staff
b. Predictors: (Constant), Number of Staff, Idle Hours
c. Dependent Variable: Production Output
Model
1 (Constant)
R R Square
.605a
.366
.700⁰
.490
Regression
Residual
Total
1
-272.382
1.424
-330.675
1.765
Idle Hours
-.139
a. Dependent Variable: Production Output
2 (Constant)
Number of Staff
Model
1
Number of Staff
2
Model Summary
Adjusted R Square Std. Error of the Estimate Durbin-Watson
38.62060
.340
.446
35.38734
1
Beta In
123
Variables Entered/Removeda
Method
Stepwise (Criteria: Probability-of-F-to-enter <= .050, Probability-of-F-to-remove >= .100).
Stepwise (Criteria: Probability-of-F-to-enter <= .050, Probability-of-F-to-remove >= .100).
124.240
.383
116.480
.379
.059
Coefficientsa
Unstandardized Coefficients Standardized Coefficients
B
Std. Error
1.998
.002
2.932
.066
.002
a. Dependent Variable: Production Output
Idle Hours
Machine Hours
Labor Hours
-.381ª
-.290a
.116a
-.221b
-2.363 .027
-1.873 .074
.588
.562
-1.481 .153
2 Machine Hours
Labor Hours
.167b
.930 .363
Number of Staff
a. Predictors in the Model: (Constant),
b. Predictors in the Model: (Constant), Number of Staff, Idle Hours
c. Dependent Variable: Production Output
Collinearity Diagnosticsa
Model Dimension Eigenvalue Condition Index
13831.271
1252.264
1.000
32.776
1.000
6.640
41.668
11.045.000b
Beta
Excluded Variables
Sig. Partial Correlation
.00
1.00
.00
.01
.99
Correlations Collinearity Statistics
t Sig. Zero-order Partial Part Tolerance VIF
-2.192.038|
.605 3.722.001
-2.839.009
.750 4.656.000
-.381 -2.363.027
-.442
-.364
.122
1.787
-.301
.194
Variance Proportions
(Constant) Number of Staff Idle Hours
.00
1.00
.00
.01
.99
.605 .605 .605
Collinearity Statistics
Tolerance VIF Minimum Tolerance
.855 1.169
.995 1.005
.698 1.433
.943 1.060
.688 1.453
.01
.90
.09
.605 .697 .693
-.095 -.442-.352
.855
.995
.698
.811
.647
1.000 1.000
.855 1.169
.855 1.169
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