Regarding the pvalue of multiple linear regression analysis, the introduction from Minitab’s website is shown below.
The pvalue for each term tests the null hypothesis that the coefficient is equal to zero (no effect). A low pvalue (< 0.05) indicates that you can reject the null hypothesis. In other words, a predictor that has a low pvalue is likely to be a meaningful addition to your model because changes in the predictor’s value are related to changes in the response variable.
For example, I have a resultant MLR model as
$
y=0.46753{{X}_{1}}0.2668{{X}_{2}}+1.6193{{X}_{3}}+4.5424{{X}_{4}}+14.48
$. and the out put is shown below. Then a $y$ can be calculated using this equation.Estimate SE tStat pValue ________ ______ _________ _________ (Intercept) 14.48 5.0127 2.8886 0.0097836 x1 0.46753 1.2824 0.36458 0.71967 x2 0.2668 3.3352 0.079995 0.93712 x3 1.6193 9.0581 0.17877 0.86011 x4 4.5424 2.8565 1.5902 0.1292
Based on the introduction above, the null hypothesis is that the coefficient equals 0. My understanding is that the coefficient, for example the coefficient of $X_{4}$, will be set as 0 and another y will be calculated as $y_{2}=0.46753{{X}_{1}}0.2668{{X}_{2}}+1.6193{{X}_{3}}+0{{X}_{4}}+14.48$.
Then a paired ttest is conducted for $y$ and $y_{2}$, but the pvalue of this ttest is 6.9e12 which does not equal to 0.1292 (pvalue of coefficient of $X_{4}$.Can anyone help on the correct understanding? Many thanks!
Answer
This is incorrect for a couple reasons:

The model “without” X4 will not necessarily have the same coefficient estimates for the other values. Fit the reduced model and see for yourself.

The statistical test for the coefficient does not concern the “mean” values of Y obtained from 2 predictions. The predicted $Y$ will always have the same grand mean, thus have a pvalue from the ttest equal to 0.5. The same holds for the residuals. Your ttest had the wrong value per the point above.

The statistical test which is conducted for the statistical significance of the coefficient is a one sample ttest. This is confusing since we do not have a “sample” of multiple coefficients for X4, but we have an estimate of the distributional properties of such a sample using the central limit theorem. The mean and standard error describe the location and shape of such a limiting distribution. If you take the column “Est” and divide by “SE” and compare to a standard normal distribution, this gives you the pvalues in the 4th column.

A fourth point: a criticism of minitab’s help page. Such a help file could not, in a paragraph, summarize years of statistical training, so I need not contend with the whole thing. But, to say that a “predictor” is “an important contribution” is vague and probably incorrect. The rationale for choosing which variables to include in a multivariate model is subtle and relies on scientific reasoning and not statistical inference.
Attribution
Source : Link , Question Author : user2230101 , Answer Author : AdamO