Ejercicios en Minitab
EJERCICIO 52:
Regression Analysis: Price versus Size
The regression equation is
Price = 64,8 + 0,0703 Size
Predictor Coef SE Coef T P
Constant 64,79 38,78 1,67 0,098
Size 0,07029 0,01733 4,06 0,000
S = 43,9547 R-Sq = 13,8% R-Sq(adj) = 12,9%
Analysis of Variance
Source DF SS MS F P
Regression 1 31770 31770 16,44 0,000
Residual Error 103 198997 1932
Total 104 230768
Unusual Observations
Obs Size Price Fit SE Fit Residual St Resid
24 2600 345,30 247,54 7,81 97,76 2,26R
25 2100 326,30 212,40 4,80 113,90 2,61R
59 1600 166,50 177,26 11,63 -10,76 -0,25 X
80 2400 125,90 233,49 5,27 -107,59 -2,47R
96 2900 227,10 268,63 12,48 -41,53 -0,99 X
99 2900 310,80 268,63 12,48 42,17 1,00 X
103 2900 227,10 268,63 12,48 -41,53 -0,99 X
R denotes an observation with a large standardized residual.
X denotes an observation whose X value gives it large leverage.
Regression Analysis: Price versus Distance
The regression equation is
Price = 270 - 3,35 Distance
Predictor Coef SE Coef T P
Constant 270,17 13,76 19,63 0,000
Distance -3,3540 0,8931 -3,76 0,000
S = 44,3919 R-Sq = 12,0% R-Sq(adj) = 11,2%
Analysis of Variance
Source DF SS MS F P
Regression 1 27791 27791 14,10 0,000
Residual Error 103 202976 1971
Total 104 230768
Unusual Observations
Obs Distance Price Fit SE Fit Residual St Resid
5 28,0 139,90 176,26 12,70 -36,36 -0,85 X
7 15,0 327,20 219,86 4,34 107,34 2,43R
24 9,0 345,30 239,98 6,64 105,32 2,40R
25 11,0 326,30 233,27 5,41 93,03 2,11R
27 26,0 187,00 182,96 11,04 4,04 0,09 X
46 21,0 307,80 199,73 7,15 108,07 2,47R
62 21,0 289,80 199,73 7,15 90,07 2,06R
80 28,0 125,90 176,26 12,70 -50,36 -1,18 X
R denotes an observation with a large standardized residual.
X denotes an observation whose X value gives it large leverage.
Regression Analysis: Size versus Price
The regression equation is
Size = 1791 + 1,96 Price
Predictor Coef SE Coef T P
Constant 1790,7 109,2 16,40 0,000
Price 1,9586 0,4830 4,06 0,000
S = 232,027 R-Sq = 13,8% R-Sq(adj) = 12,9%
Analysis of Variance
Source DF SS MS F P
Regression 1 885296 885296 16,44 0,000
Residual Error 103 5545181 53837
Total 104 6430476
Unusual Observations
Obs Price Size Fit SE Fit Residual St Resid
7 327 2500,0 2431,6 56,0 68,4 0,30 X
12 209 1700,0 2200,1 23,4 -500,1 -2,17R
24 345 2600,0 2467,1 64,1 132,9 0,60 X
25 326 2100,0 2429,9 55,6 -329,9 -1,46 X
31 234 1700,0 2249,1 23,5 -549,1 -2,38R
59 167 1600,0 2116,9 34,8 -516,9 -2,25R
96 227 2900,0 2235,6 22,8 664,4 2,88R
99 311 2900,0 2399,5 48,9 500,5 2,21R
103 227 2900,0 2235,6 22,8 664,4 2,88R
R denotes an observation with a large standardized residual.
X denotes an observation whose X value gives it large leverage.
EJERCICIO 53:
Welcome to Minitab, press F1 for help.
Results for: BASEBALL-2000.MTW
Correlations: Wins. Salary
Pearson correlation of Wins and Salary = 0,498
P-Value = 0,005
Correlations: Wins. ERA
Pearson correlation of Wins and ERA = -0,660P-Value = 0,000
P-Value = 0,000
Results for: BASEBALL-2000.MTW
Correlations: Wins; Attendance
Pearson correlation of Wins and Attendance = 0,519
P-Value = 0,003
EJERCICIO 54:
Welcome to Minitab, press F1 for help.
Results for: OECD.MTW
Regression Analysis: Population versus Employemnt
The regression equation is
Population = 2831 + 1,99 Employemnt
Predictor Coef SE Coef T P
Constant 2831 1507 1,88 0,071
Employemnt 1,98538 0,04717 42,09 0,000
S = 6782,68 R-Sq = 98,5% R-Sq(adj) = 98,4%
Analysis of Variance
Source DF SS MS F P
Regression 1 81510280887 81510280887 1771,78 0,000
Residual Error 27 1242127635 46004727
Total 28 82752408523
Unusual Observations
Obs Employemnt Population Fit SE Fit Residual St Resid
18 34325 96582 70980 1488 25602 3,87R
27 22736 62695 47971 1283 14724 2,21R
29 135231 265557 271317 5692 -5760 -1,56 X
R denotes an observation with a large standardized residual.
X denotes an observation whose X value gives it large leverage.
Predicted Values for New Observations
New
Obs Fit SE Fit 95% CI 95% PI
1 194584 3935 (186510. 202658) (178494. 210673)X
X denotes a point that is an outlier in the predictors.
Values of Predictors for New Observations
New
Obs Employemnt
1 96582
Correlations: Area. Domestic
Pearson correlation of Area and Domestic = 0,482
P-Value = 0,008
Correlations: Manufacturing; Energy
Pearson correlation of Manufacturing and Energy = -0,031
P-Value = 0,882
EJERCICIO 55:
Welcome to Minitab, press F1 for help.
Results for: OECD.MTW
Regression Analysis: Population versus Employemnt
The regression equation is
Population = 2831 + 1,99 Employemnt
Predictor Coef SE Coef T P
Constant 2831 1507 1,88 0,071
Employemnt 1,98538 0,04717 42,09 0,000
S = 6782,68 R-Sq = 98,5% R-Sq(adj) = 98,4%
Analysis of Variance
Source DF SS MS F P
Regression 1 81510280887 81510280887 1771,78 0,000
Residual Error 27 1242127635 46004727
Total 28 82752408523
Unusual Observations
Obs Employemnt Population Fit SE Fit Residual St Resid
18 34325 96582 70980 1488 25602 3,87R
27 22736 62695 47971 1283 14724 2,21R
29 135231 265557 271317 5692 -5760 -1,56 X
R denotes an observation with a large standardized residual.
X denotes an observation whose X value gives it large leverage.
Predicted Values for New Observations
New
Obs Fit SE Fit 95% CI 95% PI
1 194584 3935 (186510. 202658) (178494. 210673)X
X denotes a point that is an outlier in the predictors.
Values of Predictors for New Observations
New
Obs Employemnt
1 96582
Correlations: Area. Domestic
Pearson correlation of Area and Domestic = 0,482
P-Value = 0,008
Correlations: Manufacturing; Energy
Pearson correlation of Manufacturing and Energy = -0,031
P-Value = 0,882