spss output [5 marks] regression

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


person)

.785 1.000 .395 .747


person)

.018 .395 1.000 .045


per person)

.902 .747 .045 1.000

Sig. (1-tailed) Per Capita Personal

Consumption (US$)

. .002 .479 .000


person)

.002 . .114 .004


person)

.479 .114 . .448


per person)

.000 .004 .448 .

N Per Capita Personal

Consumption (US$)

11 11 11 11


person)

11 11 11 11


person)

11 11 11 11


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


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


person)

116.255 77.111 .383 1.508 .175


person)

-120.090 126.157 -.161 -.952 .373


per person)

45.733 17.144 .623 2.668 .032

2 (Constant) -13283.458 6926.377 -1.918 .091


person)

76.233 64.261 .251 1.186 .270


per person)

52.440 15.538 .714 3.375 .010

3 (Constant) -9521.539 6295.618 -1.512 .165


per person)

66.216 10.554 .902 6.274 .000


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


person)

.251c 1.186 .270 .387 .441


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


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


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


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


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


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


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


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


Education_Level (I) Employment_field (J) Employment_field

Mean Difference

(I-J) Std. Error Sig.b

95% Confidence Interval for

Differenceb


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


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


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


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.


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.



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


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




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.



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]

spss output [5 marks] regression

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