File > Open Then, select file from directory dialog box.
Uncheck ¡°column names¡±. Select ¡°Field delimiter¡± from list.
Stat > Regression > Regression > Fit Regression Model…
Regression Analysis: y versus x1, x2 Analysis of Variance
MTB > Regress;
SUBC> Response ‘y’;
SUBC> Nodefault;
SUBC> Continuous ‘x1’ – ‘x2’;
SUBC> Terms x1 x2;
SUBC> Constant;
SUBC> Unstandardized;
SUBC> Tmethod;
SUBC> Tanova;
SUBC> Tsummary;
SUBC> Tcoefficients;
SUBC> Tequation;
Source DF Regression 2 x1 1 x2 1
Adj SS 967.284 237.366 860.003
Adj MS 483.642 237.366 860.003
F-Value P-Value 5411.22 0.000 2655.76 0.000 9622.14 0.000
Error 6 0.536 Total 8 967.820
0.089
Model Summary
S R-sq 0.298961 99.94%
R-sq(adj) 99.93%
R-sq(pred) 99.87%
Coefficients
Term Coef
Constant 11.4333 0.0997 x1 2.0102 0.0390 x2 -6.9473 0.0708
T-Value P-Value 114.73 0.000
VIF
Regression Equation
y = 11.4333 + 2.0102 x1 – 6.9473 x2
SE Coef
51.53 -98.09
0.000 1.03 0.000 1.03
SUBC> MTB >
TDiagnostics 0.
Stat > Regression > Regression > Predict…
Prediction for y Regression Equation
MTB > Predict ‘y’;
SUBC> Nodefault;
SUBC> KPredictors 3 -0.3;
SUBC> TEquation;
SUBC> TPrediction.
MTB >
y =
11.4333 + 2.0102 x1 – 6.9473 x2
Settings
Variable Setting x1 3 x2 -0.3
Prediction
Fit SE Fit 19.5480 0.157988
95% CI (19.1615, 19.9346)
95% PI (18.7206, 20.3754)
Data Display Matrix M1
MTB > regr;
SUBC> resp ‘y’;
SUBC> cont ‘x1’ ‘x2’;
SUBC> term ‘x1’ ‘x2’;
SUBC> xpxi m1.
MTB > print m1
MTB >
0.111111 -0.0000000 -0.0000000
-0.000000 -0.000000
0.0170235 -0.0054956
-0.0054956 0.0561220
File > Save Project
Then, specify filename in directory dialog box.
MTB > Save “F:\My Documents\ise525\Blackboard\regr_eg1_17Ja n2018.MPJ”;
SUBC> Project;
SUBC> Replace.
Saving file as: ¡®F:\My Documents\ise525\Blackboard\regr_eg1_17Ja n2018.MPJ¡¯
MTB >