How do I validate my multiple linear regression model? I have split my data into two parts. I have used my 80% data to build a multiple regression linear model. Now I want to test it using my rest 20% data. What tools on Minitab do I have to make this checking process?
Edit: I think I can use PRESS statistic. My PRESS for 80% data was 6000 and now based on this model I have calculated the PRESS for 20% data, it is 1000. Now, should I compare 6000 with 1000  or 6000 with 5 times 1000, ie 5000?
 A: Note that the predicted residual sum of squares, PRESS, is got by jack-knifing the sample: there's no sense in calculating it for training & test sets. Calculate it for a model fitted to the whole sample (& compare it to the RSS to assess the amount of over-fitting). For ordinary least-squares regression there's an analytic solution:
$$\sum_i \left(\frac{e_i}{1-h_{ii}}\right)^2$$
where $e_i$ is the $i$th residual & $h_{ii}$ its leverage—from the diagonal of the hat matrix
$$H=X(X^\mathrm{T}X)^{-1}X^\mathrm{T}$$
(where $X$ is the design matrix).
In general cross-validation & bootstrap validation are preferable to splitting a sample into training & test sets: you don't lose precision in the estimates as when fitting on a smaller training set, & the performance measure on the test set will be less variable. How preferable depends on sample size.
A: You may use Root Mean Squared Error (RMSE) which is a measurement of accuracy between two set of values.
Use your model of type $Y = \beta_0 + \beta_1X_1 + \beta_2X_2 + \dots + \beta_nX_n$ calibrated from your 80% dataset, on the independent variables (IV) of your another 20% dataset (validation dataset).
In R use rmse function from hydroGOF package.
Example:
# create an object with dependent variable (DV) values from the validation dataset.
dv_observed = c(1,2,3,4,5,6,7,8,9,10)

# use the multiple linear regression model (derived from the calibration dataset) to predict DV values as from validation dataset IV values. Then, create another object.
dv_predicted = c(1,3,3,4,5,6,6,8,9,10)

require(hydroGOF)
rmse(dv_observed,dv_predicted)
[1] 0.4472136

RMSE output measurement unit is the same of your data (e.g. if DV is weight in pounds, RMSE will be pounds too).
