0

S; R-sq ; R-sq(adj) ;R-sq(pred)

* ; 100.00% ; * ; *

Coefficients

Term ; Coef; Coef ; T-Value; P-Value ; VIF

Constant ; 0.07526 ; * ; *; *;

Hardware EV ; 0.3593 ; * ; * ; *; 230.84

Mechanical EV ; 0.2933 ; * ; *; * ; 75.04

Production EV ; 0.1455 ; * ; * ; * ; 252.27

Firmware EV ; -0.3805 ; * ; * ; * ; 38.53

Note> i need the values in the place of *.

Bhaskar
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  • Cleaning/formatting your question will maximize your chance to get an answer. – keepAlive Jul 27 '17 at 10:55
  • You should say what these partial results came from. It looks like a linear regression. If so, the large VIF values suggest colinearity among the predictors which may make the solution ill-defined, inhibiting calculation of standard errors and consequently t- and p- values. We're there any warning messages? – user20637 Aug 08 '17 at 18:47

1 Answers1

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There are insufficient degrees of freedom for the calculations (including the standard deviation and the model term p-values). If this is from a DOE, you might need to augment your design with some additional runs. See Minitab support note http://support.minitab.com/en-us/minitab/17/topic-library/modeling-statistics/doe/basics/f--and-p-values-that-are-shown-as-asterisks/

Typical successful Minitab regression output will show the P-values and standard deviation (as well as other statistics) in the ANOVA table and model summary as shown below:

Analysis of Variance

Source         DF   Adj SS    Adj MS  F-Value  P-Value
Regression      1  0.03728  0.037275   100.74    0.000
  Temperature   1  0.03728  0.037275   100.74    0.000
Error          98  0.03626  0.000370
  Lack-of-Fit  47  0.01698  0.000361     0.96    0.561
  Pure Error   51  0.01928  0.000378
Total          99  0.07354


Model Summary

        S    R-sq  R-sq(adj)  R-sq(pred)
0.0192354  50.69%     50.19%      48.55%


Coefficients

Term             Coef  SE Coef  T-Value  P-Value   VIF
Constant      100.234    0.022  4475.56    0.000
Temperature  -0.01073  0.00107   -10.04    0.000  1.00

Hope this is useful to you!

dmb
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