CS代考计算机代写 Data Display Data

Data Display Data
MTB > WOPEN “E:KurtDocumentsise525Blackboardmm2- 5_2018.DAT”;
SUBC> FTYPE;
SUBC> TEXT;
SUBC> FIELD;
SUBC> COMMA;
SUBC> TDELIMITER;
SUBC> DOUBLEQUOTE;
SUBC> DECSEP;
SUBC> PERIOD;
SUBC> DATA;
SUBC> IGNOREBLANKROWS;
SUBC> EQUALCOLUMNS;
Row y x1 x2 x3 x4
1 240 25 24 91 100
2 236 31 21 90 95
3 290 45 24 88 110
4 274 60 25 87 88
5 301 65 25 91 94
6 316 72 26 94 99
7 300 80 25 87 97
8 296 84 25 86 96
9 267 75 24 88 110
SUBC>
SUBC>
SUBC>
SUBC>
Retrieving worksheet from file: ¡®E:KurtDocumentsise525Blackboardmm2- 5_2018.DAT¡¯
10 276 60 25 91 105
11 288 50 25 90 100
12 261 38 23 89 98
Worksheet was saved on Mon Jan 22 2018
SHEET 1;
VNAMES -1;
FIRST 1;
NROWS 12.
Results for: mm2-5_2018.DAT
MTB > print c1-c5
MTB >

Regression Analysis: y versus x1, x2, x3, x4 Analysis of Variance
MTB > print c1-c5
MTB > Regress;
SUBC> Response ‘y’;
SUBC> Nodefault;
SUBC> Continuous ‘x1’ – ‘x4’;
SUBC> Terms x1 x2 x3 x4;
SUBC> Constant;
Source DF Regression 4 x1 1 x2 1 x3 1 x4 1 Error 7 Total 11
Adj SS 4957.24 653.63 687.93 87.68 0.08 1699.01 6656.25
Adj MS 1239.31 653.63 687.93 87.68 0.08 242.72
F-Value P-Value 5.11 0.030 2.69 0.145 2.83 0.136 0.36 0.567 0.00 0.986
SUBC> Unstandardized;
SUBC> Tmethod;
SUBC> Tanova;
SUBC> Tsummary;
SUBC> Tcoefficients;
SUBC> Tequation;
Model Summary
S R-sq 15.5793 74.47%
R-sq(adj) 59.89%
R-sq(pred) 7.75%
Coefficients
Term Coef Constant -103 x1 0.605 x2 8.92 x3 1.44 x4 0.014
SE Coef T-Value 208 -0.49 0.369 1.64 5.30 1.68 2.39 0.60 0.734 0.02
P-Value VIF 0.636
Regression Equation
y = -103+0.605×1+8.92×2+1.44×3+0.014×4
0.145 2.32 0.136 2.16 0.567 1.34 0.986 1.01
SUBC> MTB >
TDiagnostics 0.

Fits and Diagnostics for Unusual Observations
Std Obs y Fit Resid Resid
3 290.0 266.7 23.3 2.09 R R Large residual
Prediction for y Regression Equation
MTB > Predict ‘y’;
SUBC> Nodefault;
SUBC> KPredictors 75 24 90 98;
SUBC> TEquation;
SUBC> TPrediction.
MTB >
y = -103+0.605×1+8.92×2+1.44×3+0.014×4
Settings
Variable Setting
x1 x2 x3 x4
75 24 90 98
Prediction
Fit
287.562 10.0540
95% CI (263.788, 311.336)
95% PI (243.717, 331.406)
SE Fit

Regression Analysis: y versus x1, x2, x3 Analysis of Variance
MTB > Regress;
SUBC> Response ‘y’;
SUBC> Nodefault;
SUBC> Continuous ‘x1’ – ‘x4’;
SUBC> Terms x1 x2 x3;
SUBC> Constant;
SUBC> Unstandardized;
SUBC> Tmethod;
SUBC> Tanova;
SUBC> Tsummary;
SUBC> Tcoefficients;
SUBC> Tequation;
Source DF Regression 3 x1 1 x2 1 x3 1 Error 8 Total 11
Adj SS 4957.16 654.97 688.87 88.87 1699.09 6656.25
Adj MS 1652.39 654.97 688.87 88.87 212.39
F-Value P-Value 7.78 0.009 3.08 0.117 3.24 0.109 0.42 0.536
Model Summary
S R-sq 14.5735 74.47%
R-sq(adj) 64.90%
R-sq(pred) 43.55%
Coefficients
Term Coef Constant -102 x1 0.606 x2 8.92 x3 1.44
SE Coef 186 0.345 4.95 2.23
T-Value -0.55 1.76 1.80 0.65
P-Value VIF 0.600
Regression Equation
y = -102+0.606×1+8.92×2+1.44×3
0.117 2.32 0.109 2.16 0.536 1.32
SUBC> MTB >
TDiagnostics 0.

Regression Analysis: y versus x1, x2 Analysis of Variance
MTB > Regress;
SUBC> Response ‘y’;
SUBC> Nodefault;
SUBC> Continuous ‘x1’ – ‘x4’;
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 Error 9 Total 11
Adj SS 4868.3 577.8 1109.4 1788.0 6656.2
Adj MS 2434.1 577.8 1109.4 198.7
F-Value P-Value 12.25 0.003 2.91 0.122 5.58 0.042
Model Summary
S R-sq 14.0948 73.14%
R-sq(adj) 67.17%
R-sq(pred) 47.33%
Coefficients
Term Constant x1
x2
Coef SE Coef 0.5 95.6 0.497 0.292 10.27 4.34
T-Value P-Value 0.01 0.996 1.71 0.122 2.36 0.042
VIF
Regression Equation
y = 0.5+0.497×1+10.27×2
1.77 1.77
SUBC> MTB >
TDiagnostics 0.

Regression Analysis: y versus x2 Analysis of Variance
MTB > Name C6 “RESI”.
MTB > Regress;
SUBC> Response ‘y’;
SUBC> Nodefault;
Source DF Regression 1 x21 Error 10 Total 11
Adj SS 4290 4290 2366 6656
Adj MS
4290.5 18.14 4290.5 18.14
P-Value
SUBC>
Continuous ‘x1’ – ‘x4’;
Terms x2;
Constant;
Model Summary
SUBC>
SUBC>
MTB >
TDiagnostics 0;
Residuals ‘RESI’.
S R-sq R-sq(adj) 15.3811 64.46% 60.90%
R-sq(pred) 47.83%
Coefficients
Term Constant x2
Coef -90.2 15.16
SE Coef 86.7 3.56
T-Value -1.04 4.26
P-Value 0.323 0.002
VIF 1.00
Regression Equation
y = -90.2 + 15.16 x2
236.6
F-Value
SUBC> 0.002 SUBC>
0.002
SUBC> Unstandardized;
SUBC> Tmethod;
SUBC> Tanova;
SUBC> Tsummary;
SUBC> Tcoefficients;
SUBC> Tequation;

Fits and Diagnostics for Unusual Observations
Obs y Fit Resid StdResid
1 240.00 273.70 -33.70
-2.30 R 0.89 X
2 236.00 228.21 R Large residual
X Unusual X
7.79
Prediction for y Regression Equation
MTB > Predict ‘y’;
SUBC> Nodefault;
SUBC> KPredictors 24;
SUBC> TEquation;
SUBC> TPrediction.
MTB >
y = -90.2 + 15.16 x2
Settings
Variable Setting x2 24
Prediction
Fit SE Fit 273.696 4.59597
95% CI (263.456, 283.937)
95% PI (237.928, 309.465)

2
1
0
-1
20
10
-10
-20
-30
-40
0
Scatterplot of zscore vs RESI
MTB > name c7 ‘zscore’
MTB > Let ‘zscore’ = NSCOR(‘RESI’)
MTB > Plot ‘zscore’*’RESI’ ‘RESI’*’x2’; SUBC> Symbol.
MTB >
-2
-40 -30 -20 -10 0 10 20
RESI Scatterplot of RESI vs x2
21 22 23 24 25 26 x2
RESI zscore