spss output [5 marks] regression
TRANSCRIPT
Answer to Assignment 5 [90 marks] Q1 [20 marks] Dependent variable: Per Capita Personal Consumption (US$) Independent variables: Paper Consumption (kg per person), Fish consumption (lbs per person), Gasoline Consumption (litres per person) [5 marks] SPSS output [5 marks] Regression
Descriptive Statistics
Mean Std. Deviation N
Per Capita Personal
Consumption (US$)
18265.1818 32633.81943 11
Paper Consumption (kg per
person)
125.1818 107.50704 11
Fish consumption (lbs per
person)
65.3636 43.70188 11
Gasoline Consumption (litres
per person)
419.6364 444.62057 11
Correlations
Per Capita
Personal
Consumption
(US$)
Paper
Consumption (kg
per person)
Fish consumption
(lbs per person)
Gasoline
Consumption
(litres per person)
Pearson Correlation Per Capita Personal
Consumption (US$)
1.000 .785 .018 .902
Paper Consumption (kg per
person)
.785 1.000 .395 .747
Fish consumption (lbs per
person)
.018 .395 1.000 .045
Gasoline Consumption (litres
per person)
.902 .747 .045 1.000
Sig. (1-tailed) Per Capita Personal
Consumption (US$)
. .002 .479 .000
Paper Consumption (kg per
person)
.002 . .114 .004
Fish consumption (lbs per
person)
.479 .114 . .448
Gasoline Consumption (litres
per person)
.000 .004 .448 .
N Per Capita Personal
Consumption (US$)
11 11 11 11
Paper Consumption (kg per
person)
11 11 11 11
Fish consumption (lbs per
person)
11 11 11 11
Gasoline Consumption (litres
per person)
11 11 11 11
Variables Entered/Removeda
Model Variables Entered
Variables
Removed Method
1 Gasoline
Consumption
(litres per person),
Fish consumption
(lbs per person),
Paper
Consumption (kg
per person)b
. Enter
2 . Fish consumption
(lbs per person)
Backward
(criterion:
Probability of F-to-
remove >= .100).
3 . Paper
Consumption (kg
per person)
Backward
(criterion:
Probability of F-to-
remove >= .100).
a. Dependent Variable: Per Capita Personal Consumption (US$)
b. All requested variables entered.
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
Change Statistics
R Square Change F Change df1 df2 Sig. F Change
1 .927a .860 .800 14600.56643 .860 14.319 3 7 .002
2 .917b .842 .802 14514.65715 -.018 .906 1 7 .373
3 .902c .814 .793 14839.47313 -.028 1.407 1 8 .270
a. Predictors: (Constant), Gasoline Consumption (litres per person), Fish consumption (lbs per person), Paper Consumption (kg per person)
b. Predictors: (Constant), Gasoline Consumption (litres per person), Paper Consumption (kg per person)
c. Predictors: (Constant), Gasoline Consumption (litres per person)
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 9157425923.613 3 3052475307.871 14.319 .002b
Residual 1492235780.024 7 213176540.003 Total 10649661703.636 10
2 Regression 8964259525.525 2 4482129762.762 21.275 .001c
Residual 1685402178.112 8 210675272.264 Total 10649661703.636 10
3 Regression 8667772039.054 1 8667772039.054 39.361 .000d
Residual 1981889664.582 9 220209962.731 Total 10649661703.636 10
a. Dependent Variable: Per Capita Personal Consumption (US$)
b. Predictors: (Constant), Gasoline Consumption (litres per person), Fish consumption (lbs per person),
Paper Consumption (kg per person)
c. Predictors: (Constant), Gasoline Consumption (litres per person), Paper Consumption (kg per person)
d. Predictors: (Constant), Gasoline Consumption (litres per person)
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
t Sig. B Std. Error Beta
1 (Constant) -7629.627 9155.404 -.833 .432
Paper Consumption (kg per
person)
116.255 77.111 .383 1.508 .175
Fish consumption (lbs per
person)
-120.090 126.157 -.161 -.952 .373
Gasoline Consumption (litres
per person)
45.733 17.144 .623 2.668 .032
2 (Constant) -13283.458 6926.377 -1.918 .091
Paper Consumption (kg per
person)
76.233 64.261 .251 1.186 .270
Gasoline Consumption (litres
per person)
52.440 15.538 .714 3.375 .010
3 (Constant) -9521.539 6295.618 -1.512 .165
Gasoline Consumption (litres
per person)
66.216 10.554 .902 6.274 .000
a. Dependent Variable: Per Capita Personal Consumption (US$)
Excluded Variablesa
Model Beta In t Sig. Partial Correlation
Collinearity
Statistics
Tolerance
2 Fish consumption (lbs per
person)
-.161b -.952 .373 -.339 .701
3 Fish consumption (lbs per
person)
-.022c -.144 .889 -.051 .998
Paper Consumption (kg per
person)
.251c 1.186 .270 .387 .441
a. Dependent Variable: Per Capita Personal Consumption (US$)
b. Predictors in the Model: (Constant), Gasoline Consumption (litres per person), Paper Consumption (kg per person)
c. Predictors in the Model: (Constant), Gasoline Consumption (litres per person)
Regression equation: Per Capita Personal Consumption (US$)=-9521.539+66.216xGasoline Consumption (litres per person) [5 marks] Except for Gasoline Consumption (p=0.000), the other independent variables are excluded as p>0.05. The r2 is 0.86 meaning 86% of the variation of the Per Capita Personal Consumption (US$) can be explained by Gasoline Consumption. The p-value of the overall model is 0.000<0.05 indicating that this model is statistically significant. This model is good/strong with good predictive value. [5 marks] Q2 [20 marks] Dependent variable: Asking Price ($ thousands) Independent variable: Lot Size, Living Space, Yearly Taxes, Bedrooms, Bathrooms, Ages, Parking Spaces [5 marks] Regression [5 marks]
Descriptive Statistics Mean Std. Deviation N
Asking Price 481.6852 90.14365 61
Lot Size .2772 .15933 61
Living Space 1856.4590 680.60132 61
Yearly Taxes 4485.6393 820.75327 61
Bedrooms 4.0164 .92181 61
Bathrooms 2.7295 .76143 61
Age 54.3279 17.19663 61
Parking Spaces .6721 .74658 61
Correlations Asking Price Lot Size Living Space Yearly Taxes Bedrooms Bathrooms Age Parking Spaces
Pearson Correlation Asking Price 1.000 .426 .631 .990 .386 .402 -.418 .427
Lot Size .426 1.000 .491 .419 .337 .373 -.408 .561
Living Space .631 .491 1.000 .645 .562 .575 -.581 .495
Yearly Taxes .990 .419 .645 1.000 .392 .389 -.371 .417
Bedrooms .386 .337 .562 .392 1.000 .683 -.434 .371
Bathrooms .402 .373 .575 .389 .683 1.000 -.577 .325
Age -.418 -.408 -.581 -.371 -.434 -.577 1.000 -.522
Parking Spaces .427 .561 .495 .417 .371 .325 -.522 1.000
Sig. (1-tailed) Asking Price . .000 .000 .000 .001 .001 .000 .000
Lot Size .000 . .000 .000 .004 .002 .001 .000
Living Space .000 .000 . .000 .000 .000 .000 .000
Yearly Taxes .000 .000 .000 . .001 .001 .002 .000
Bedrooms .001 .004 .000 .001 . .000 .000 .002
Bathrooms .001 .002 .000 .001 .000 . .000 .005
Age .000 .001 .000 .002 .000 .000 . .000
Parking Spaces .000 .000 .000 .000 .002 .005 .000 .
N Asking Price 61 61 61 61 61 61 61 61
Lot Size 61 61 61 61 61 61 61 61
Living Space 61 61 61 61 61 61 61 61
Yearly Taxes 61 61 61 61 61 61 61 61
Bedrooms 61 61 61 61 61 61 61 61
Bathrooms 61 61 61 61 61 61 61 61
Age 61 61 61 61 61 61 61 61
Parking Spaces 61 61 61 61 61 61 61 61
Variables Entered/Removeda
Model Variables Entered
Variables
Removed Method
1 Yearly Taxes . Stepwise (Criteria:
Probability-of-F-
to-enter <= .050,
Probability-of-F-
to-remove
>= .100).
2 Age . Stepwise (Criteria:
Probability-of-F-
to-enter <= .050,
Probability-of-F-
to-remove
>= .100).
3 Living Space . Stepwise (Criteria:
Probability-of-F-
to-enter <= .050,
Probability-of-F-
to-remove
>= .100).
a. Dependent Variable: Asking Price
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .990a .981 .980 12.61773
2 .992b .984 .983 11.68870
3 .993c .986 .985 11.11837
a. Predictors: (Constant), Yearly Taxes
b. Predictors: (Constant), Yearly Taxes, Age
c. Predictors: (Constant), Yearly Taxes, Age, Living Space
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 478159.459 1 478159.459 3003.381 .000b
Residual 9393.218 59 159.207 Total 487552.677 60
2 Regression 479628.381 2 239814.190 1755.263 .000c
Residual 7924.296 58 136.626 Total 487552.677 60
3 Regression 480506.444 3 160168.815 1295.674 .000d
Residual 7046.233 57 123.618 Total 487552.677 60
a. Dependent Variable: Asking Price
b. Predictors: (Constant), Yearly Taxes
c. Predictors: (Constant), Yearly Taxes, Age
d. Predictors: (Constant), Yearly Taxes, Age, Living Space
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
t Sig. B Std. Error Beta
1 (Constant) -6.205 9.048 -.686 .496
Yearly Taxes .109 .002 .990 54.803 .000
2 (Constant) 21.434 11.887 1.803 .077
Yearly Taxes .106 .002 .968 53.718 .000
Age -.310 .094 -.059 -3.279 .002
3 (Constant) 28.602 11.623 2.461 .017
Yearly Taxes .110 .002 1.000 47.976 .000
Age -.441 .103 -.084 -4.304 .000
Living Space -.008 .003 -.063 -2.665 .010
a. Dependent Variable: Asking Price
Excluded Variablesa
Model Beta In t Sig. Partial Correlation
Collinearity
Statistics
Tolerance
1 Lot Size .013b .640 .525 .084 .824
Living Space -.014b -.594 .555 -.078 .584
Bedrooms -.002b -.121 .904 -.016 .847
Bathrooms .019b .959 .341 .125 .848
Age -.059b -3.279 .002 -.395 .862
Parking Spaces .017b .857 .395 .112 .827
2 Lot Size -.006c -.301 .765 -.040 .750
Living Space -.063c -2.665 .010 -.333 .449
Bedrooms -.025c -1.324 .191 -.173 .750
Bathrooms -.015c -.720 .475 -.095 .631
Parking Spaces -.011c -.554 .582 -.073 .670
3 Lot Size .004d .237 .814 .032 .718
Bedrooms -.010d -.511 .612 -.068 .665
Bathrooms .000d .002 .999 .000 .580
Parking Spaces -.004d -.193 .847 -.026 .655
a. Dependent Variable: Asking Price
b. Predictors in the Model: (Constant), Yearly Taxes
c. Predictors in the Model: (Constant), Yearly Taxes, Age
d. Predictors in the Model: (Constant), Yearly Taxes, Age, Living Space
Regression equation: Asking Price ($ thousands) =28.602+0.110xYearly Taxes-0.441xAge-0.008xLiving Space [5 marks] b) adjusted r2 is 0.985 meaning 98.5% of the variation of Asking Price can be explained by the Regression Model which is rather good. The p-value of Yearly taxes, Age & Living Space and the overall model are very small (0.000, 0.000, 0.01 & 0.000 respectively). The model is significant and has a good predictive value (adjusted r2=0.985). [5 marks] Q3 [25 marks] H0: μHS=μB=μM=μPhD
H1: At least one of the population mean is different from the others
H0: μEd=μFin=μMed
H1: At least one of the population mean is different from the others H0: there is no interaction between the Education Level and the Field of Employment H1: there is an interaction between the Education Level and the Field of Employment [5 marks] SPSS output [5 marks] Univariate Analysis of Variance
Between-Subjects Factors N
Education_Level Bachelor 9
HighSchool 9
Master 9
PhD 9
Employment_field Edu 12
Fin 12
Med 12
Descriptive Statistics
Dependent Variable: Annual_Income Education_Level Employment_field Mean Std. Deviation N
Bachelor Edu 33.0000 2.64575 3
Fin 46.0000 2.00000 3
Med 43.3333 1.52753 3
Total 40.7778 6.22049 9
HighSchool Edu 22.3333 2.51661 3
Fin 25.6667 1.15470 3
Med 25.0000 1.00000 3
Total 24.3333 2.12132 9
Master Edu 47.6667 2.08167 3
Fin 54.6667 4.16333 3
Med 59.3333 3.05505 3
Total 53.8889 5.79751 9
PhD Edu 77.0000 2.64575 3
Fin 92.3333 2.51661 3
Med 98.3333 7.63763 3
Total 89.2222 10.42566 9
Total Edu 45.0000 21.56597 12
Fin 54.6667 25.33891 12
Med 56.5000 28.46529 12
Total 52.0556 25.07582 36
Tests of Between-Subjects Effects
Dependent Variable: Annual_Income
Source
Type III Sum of
Squares df Mean Square F Sig.
Partial Eta
Squared
Corrected Model 21758.556a 11 1978.051 190.401 .000 .989
Intercept 97552.111 1 97552.111 9390.043 .000 .997
Education_Level 20523.889 3 6841.296 658.520 .000 .988
Employment_field 916.222 2 458.111 44.096 .000 .786
Education_Level *
Employment_field
318.444 6 53.074 5.109 .002 .561
Error 249.333 24 10.389 Total 119560.000 36
Corrected Total 22007.889 35 a. R Squared = .989 (Adjusted R Squared = .983)
At first, all the main effects (Education Level & Field of Employment) and the interaction are significant with p<0.05 (p=0.000 for Education level & Employment field and p=0.002 for interaction). As the interaction is significant, it is more appropriate to look into the pairwise comparison in different levels of the simple main effect. [5 marks]
Pairwise Comparisons
Dependent Variable: Annual_Income
Employment_field (I) Education_Level (J) Education_Level
Mean Difference
(I-J) Std. Error Sig.b
95% Confidence Interval for
Differenceb
Lower Bound Upper Bound
Edu Bachelor HighSchool 10.667* 2.632 .000 5.235 16.098
Master -14.667* 2.632 .000 -20.098 -9.235
PhD -44.000* 2.632 .000 -49.432 -38.568
HighSchool Bachelor -10.667* 2.632 .000 -16.098 -5.235
Master -25.333* 2.632 .000 -30.765 -19.902
PhD -54.667* 2.632 .000 -60.098 -49.235
Master Bachelor 14.667* 2.632 .000 9.235 20.098
HighSchool 25.333* 2.632 .000 19.902 30.765
PhD -29.333* 2.632 .000 -34.765 -23.902
PhD Bachelor 44.000* 2.632 .000 38.568 49.432
HighSchool 54.667* 2.632 .000 49.235 60.098
Master 29.333* 2.632 .000 23.902 34.765
Fin Bachelor HighSchool 20.333* 2.632 .000 14.902 25.765
Master -8.667* 2.632 .003 -14.098 -3.235
PhD -46.333* 2.632 .000 -51.765 -40.902
HighSchool Bachelor -20.333* 2.632 .000 -25.765 -14.902
Master -29.000* 2.632 .000 -34.432 -23.568
PhD -66.667* 2.632 .000 -72.098 -61.235
Master Bachelor 8.667* 2.632 .003 3.235 14.098
HighSchool 29.000* 2.632 .000 23.568 34.432
PhD -37.667* 2.632 .000 -43.098 -32.235
PhD Bachelor 46.333* 2.632 .000 40.902 51.765
HighSchool 66.667* 2.632 .000 61.235 72.098
Master 37.667* 2.632 .000 32.235 43.098
Med Bachelor HighSchool 18.333* 2.632 .000 12.902 23.765
Master -16.000* 2.632 .000 -21.432 -10.568
PhD -55.000* 2.632 .000 -60.432 -49.568
HighSchool Bachelor -18.333* 2.632 .000 -23.765 -12.902
Master -34.333* 2.632 .000 -39.765 -28.902
PhD -73.333* 2.632 .000 -78.765 -67.902
Master Bachelor 16.000* 2.632 .000 10.568 21.432
HighSchool 34.333* 2.632 .000 28.902 39.765
PhD -39.000* 2.632 .000 -44.432 -33.568
PhD Bachelor 55.000* 2.632 .000 49.568 60.432
HighSchool 73.333* 2.632 .000 67.902 78.765
Master 39.000* 2.632 .000 33.568 44.432
Based on estimated marginal means
*. The mean difference is significant at the .05 level.
b. Adjustment for multiple comparisons: Least Significant Difference (equivalent to no adjustments).
The above pairwise comparison for different fields of employment, there are significant differences between all of the different pairs of Education Level. Within Educational Services, High School has the lowest annual income (mean=22.3), then Bachelor has the second-lowest income (mean=33), then Master is next (mean=47.7) and PhD has the highest annual income (mean=77). Within Financial Services, High School has the lowest annual income (mean=25.7), then Bachelor has the second-lowest income (mean=46), then Master is next (mean=54.7) and PhD has the highest annual income (mean=92.3). Within Medical Services, High School has the lowest annual income (mean=25), then Bachelor has the second-lowest income (mean=43.3), then Master is next (mean=59.3) and PhD has the highest annual income (mean=98.3). All these are pointing out that the higher the educational level, the annual income would be higher in these three employment fields (Educational Services, Final Services & Medical Services) [5 marks]
Pairwise Comparisons
Dependent Variable: Annual_Income
Education_Level (I) Employment_field (J) Employment_field
Mean Difference
(I-J) Std. Error Sig.b
95% Confidence Interval for
Differenceb
Lower Bound Upper Bound
Bachelor Edu Fin -13.000* 2.632 .000 -18.432 -7.568
Med -10.333* 2.632 .001 -15.765 -4.902
Fin Edu 13.000* 2.632 .000 7.568 18.432
Med 2.667 2.632 .321 -2.765 8.098
Med Edu 10.333* 2.632 .001 4.902 15.765
Fin -2.667 2.632 .321 -8.098 2.765
HighSchool Edu Fin -3.333 2.632 .217 -8.765 2.098
Med -2.667 2.632 .321 -8.098 2.765
Fin Edu 3.333 2.632 .217 -2.098 8.765
Med .667 2.632 .802 -4.765 6.098
Med Edu 2.667 2.632 .321 -2.765 8.098
Fin -.667 2.632 .802 -6.098 4.765
Master Edu Fin -7.000* 2.632 .014 -12.432 -1.568
Med -11.667* 2.632 .000 -17.098 -6.235
Fin Edu 7.000* 2.632 .014 1.568 12.432
Med -4.667 2.632 .089 -10.098 .765
Med Edu 11.667* 2.632 .000 6.235 17.098
Fin 4.667 2.632 .089 -.765 10.098
PhD Edu Fin -15.333* 2.632 .000 -20.765 -9.902
Med -21.333* 2.632 .000 -26.765 -15.902
Fin Edu 15.333* 2.632 .000 9.902 20.765
Med -6.000* 2.632 .032 -11.432 -.568
Med Edu 21.333* 2.632 .000 15.902 26.765
Fin 6.000* 2.632 .032 .568 11.432
Based on estimated marginal means
*. The mean difference is significant at the .05 level.
b. Adjustment for multiple comparisons: Least Significant Difference (equivalent to no adjustments).
In the pairwise comparisons above, within High School, all three fields of employment are not significant (p>0.05). For both Bachelor’s and
Master’s levels, Educational Services is significantly different from both Financial Services and Medical Services while the difference between
Financial Services & Medical Services is not significant. At the PhD level, there are significant differences between each pair of the three fields
of employment. Within the High School level, the annual income has no significant differences between each pair of the three employment fields (Educational
Services, Final Services & Medical Services). It seems that High School level qualification does not impact the annual income in these three
professions.
Within the Bachelor level, the annual income in Educational Services (mean=33) is significantly lower than those of Financial Services
(mean=46) and Medical Services (mean=43.3). No significant difference between Financial Services & Medical Services.
At the Master level, the situation is the same with Educational Services (mean=47.7) is significantly lower than those of Financial Services
(mean=54.7) and Medical Services (mean=59.3). It seems that in these two educational levels (Bachelor & Master), the Educational Services
has lower annual income than the other two Employment fields (Financial & Medical Services).
Within the PhD level, all of the annual incomes are significantly different between all pairs of Employment fields with Educational Services
being the lowest (mean=77), Financial Services being second (mean=92.3) and Medical Services having the highest annual income
(mean=89.2). These have indicated that at the PhD level, the different Employment fields are different from each other.
It seems that the Financial Services and Medical Services do require a higher level of professionalism especially at a higher educational level,
they are being rewarded by higher annual income.
[5 marks]
Q4 [25 marks] H0: μHighGPA=μBMedGPA=μM=μLowGPA
H1: At least one of the population mean is different from the others
H0: μBusiness=μEngeering=μArt
H1: At least one of the population mean is different from the others H0: there is no interaction between the GPA Level and the majors H1: there is an interaction between the GPA Level and the majors [5 marks] SPSS output [5 marks] Univariate Analysis of Variance
Between-Subjects Factors
N
GPA High 15
Low 15
Med 15
Faulty ART 15
BUS 15
ENG 15
Descriptive Statistics
Dependent Variable: Starting_salary
GPA Faulty Mean Std. Deviation N
High ART 55.2000 13.31165 5
BUS 67.6000 11.61034 5
ENG 65.2000 6.41872 5
Total 62.6667 11.48083 15
Low ART 45.2000 5.76194 5
BUS 57.2000 9.95992 5
ENG 54.8000 9.47101 5
Total 52.4000 9.60506 15
Med ART 49.6000 7.92465 5
BUS 59.2000 11.79830 5
ENG 61.2000 7.82304 5
Total 56.6667 10.13246 15
Total ART 50.0000 9.79796 15
BUS 61.3333 11.33053 15
ENG 60.4000 8.63382 15
Total 57.2444 11.04980 45
Tests of Between-Subjects Effects
Dependent Variable: Starting_salary
Source
Type III Sum of
Squares df Mean Square F Sig.
Partial Eta
Squared
Corrected Model 2018.311a 8 252.289 2.708 .019 .376
Intercept 147461.689 1 147461.689 1582.773 .000 .978
GPA 798.044 2 399.022 4.283 .021 .192
Faulty 1187.378 2 593.689 6.372 .004 .261
GPA * Faulty 32.889 4 8.222 .088 .986 .010
Error 3354.000 36 93.167 Total 152834.000 45 Corrected Total 5372.311 44 a. R Squared = .376 (Adjusted R Squared = .237)
The interaction is not statistically significant (p>0.05) but both the GPA and major are significant (p=0.021 and 0.004 respectively). Post Hoc test using HST & Scheffe is appropriate to aid interpretation. See below. [5 marks]
Post Hoc Tests GPA
Multiple Comparisons Dependent Variable: Starting_salary
(I) GPA (J) GPA
Mean Difference
(I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Tukey HSD High Low 10.2667* 3.52452 .016 1.6517 18.8816
Med 6.0000 3.52452 .218 -2.6150 14.6150
Low High -10.2667* 3.52452 .016 -18.8816 -1.6517
Med -4.2667 3.52452 .455 -12.8816 4.3483
Med High -6.0000 3.52452 .218 -14.6150 2.6150
Low 4.2667 3.52452 .455 -4.3483 12.8816
Scheffe High Low 10.2667* 3.52452 .022 1.2678 19.2655
Med 6.0000 3.52452 .248 -2.9988 14.9988
Low High -10.2667* 3.52452 .022 -19.2655 -1.2678
Med -4.2667 3.52452 .488 -13.2655 4.7322
Med High -6.0000 3.52452 .248 -14.9988 2.9988
Low 4.2667 3.52452 .488 -4.7322 13.2655
Based on observed means.
The error term is Mean Square(Error) = 93.167.
*. The mean difference is significant at the .05 level.
Homogeneous Subsets
Starting_salary
GPA N
Subset
1 2
Tukey HSDa,b Low 15 52.4000 Med 15 56.6667 56.6667
High 15 62.6667
Sig. .455 .218
Scheffea,b Low 15 52.4000 Med 15 56.6667 56.6667
High 15 62.6667
Sig. .488 .248
Means for groups in homogeneous subsets are displayed.
Based on observed means.
The error term is Mean Square(Error) = 93.167.
a. Uses Harmonic Mean Sample Size = 15.000.
b. Alpha = .05.
For GPA, it is noted that there is a significant difference (p<0.05) between High GPA (mean=62.7) and Low GPA (mean=52.4) in starting salary
but not between High GPA (mean=62.7) and Medium GPA (mean=56.7) and between Low GPA (mean=52.4) and Medium GPA (mean=56.7). Looking at the sample means, it is clear that the higher the GPA, the higher the starting salary. [5 marks]
Faulty
Multiple Comparisons Dependent Variable: Starting_salary
(I) Faulty (J) Faulty
Mean Difference
(I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Tukey HSD ART BUS -11.3333* 3.52452 .008 -19.9483 -2.7184
ENG -10.4000* 3.52452 .015 -19.0150 -1.7850
BUS ART 11.3333* 3.52452 .008 2.7184 19.9483
ENG .9333 3.52452 .962 -7.6816 9.5483
ENG ART 10.4000* 3.52452 .015 1.7850 19.0150
BUS -.9333 3.52452 .962 -9.5483 7.6816
Scheffe ART BUS -11.3333* 3.52452 .011 -20.3322 -2.3345
ENG -10.4000* 3.52452 .020 -19.3988 -1.4012
BUS ART 11.3333* 3.52452 .011 2.3345 20.3322
ENG .9333 3.52452 .966 -8.0655 9.9322
ENG ART 10.4000* 3.52452 .020 1.4012 19.3988
BUS -.9333 3.52452 .966 -9.9322 8.0655
Based on observed means.
The error term is Mean Square(Error) = 93.167.
*. The mean difference is significant at the .05 level.
Homogeneous Subsets
Starting_salary
Faulty N
Subset
1 2
Tukey HSDa,b ART 15 50.0000
ENG 15 60.4000
BUS 15 61.3333
Sig. 1.000 .962
Scheffea,b ART 15 50.0000 ENG 15 60.4000
BUS 15 61.3333
Sig. 1.000 .966
Means for groups in homogeneous subsets are displayed.
Based on observed means.
The error term is Mean Square(Error) = 93.167.
a. Uses Harmonic Mean Sample Size = 15.000.
b. Alpha = .05.
Again, there is a significant difference in starting salary between Business major (mean=61.3) and Art major (mean=50) and also for
Engineering major (mean=60.4) and art major (mean=50). The difference in starting salary between a Business major & an engineering major is not significant. Therefore there is evidence to support that a practical major is having a higher starting salary. [5 marks]