a. CONFIDENCE INTERVAL FOR ONE POPULATION MEAN. INTERPRET YOUR RESULT.

Wages is an important value to worker and economic growth. As we know wages are affected

by many type of variable such as occupation, , industry, age, gender, education and

experience. In this paper we want to know whether wages have relationship with occupation.

Before we proceed to this objective, firstly we want to know the range of wages at the period

2012. The way to know the range of wages at the end period 2012 is by conduct a confidence

level method for the mean amount of wages in period 2012. From a one organisation we

randomly select a sample of 80 workers has been selected, with 5% significant level for each

analysis. Based on SPSS output below conduct this analysis and interpret the result.

One-Sample Statistics

N Mean Std. Deviation Std. Error Mean

Wages2012 80 30533.09 16782.760 1876.370

One-Sample Test

Test Value = 30000

t df Sig. (2-tailed) Mean Difference

95% Confidence Interval of the

Difference

Lower Upper

Wages2012 .284 79 .777 533.088 -3201.73 4267.91

b. CONFIDENCE INTERVAL FOR MEAN DIFFERENCE. INTERPRET YOUR RESULT.

Confidence level for wages 2012 has been calculated, now by using SPSS output for independent test. Find confidence level for mean difference in wages 2012 and occupation with 95% confidence interval and interpret the answer.

Group Statistics

occupation N Mean Std. Deviation Std. Error Mean

Wages2012 Clerical 17 27310.94 8056.002 1953.867

Service 17 21107.94 6830.495 1656.638

Independent Samples Test

Levene's Test

for Equality of

Variances t-test for Equality of Means

F Sig. t df

Sig.

(2-

tailed

)

Mean

Differenc

e

Std. Error

Differenc

e

95% Confidence

Interval of the

Difference

Lower Upper

Wages2012 Equal

variances

assumed

1.473 .234 2.42

1

32 .021 6203.000 2561.650 985.09

1

11420.90

9

Equal

variances

not

assumed

2.42

1

31.16

6

.021 6203.000 2561.650 979.61

2

11426.38

8

c. T-TEST FOR ONE SAMPLE

For the further analysis, in 2012 a lot of people say that the salaries among worker are more

than RM30000 per worker. A sample of wages is selected from a one organisation, at the 5%

significant level there is enough evidence to support the claim.

One-Sample Statistics

N Mean Std. Deviation Std. Error Mean

Wages2012 80 30533.09 16782.760 1876.370

One-Sample Test

Test Value = 30000

t df Sig. (2-tailed) Mean Difference

95% Confidence Interval of the

Difference

Lower Upper

Wages2012 .284 79 .777 533.088 -3201.73 4267.91

d. T-TEST FOR INDEPENDENT SAMPLE

The next analysis, in 2012 there a lot of worker say that clerical occupation wages is more

than service occupation wages. A sample of worker from clerical and service occupation has

been selected to conduct this analysis. At the 5% significant level, is there enough evidence

to support the claim.

Group Statistics

occupation N Mean Std. Deviation Std. Error Mean

Wages2012 Clerical 17 27310.94 8056.002 1953.867

Service 17 21107.94 6830.495 1656.638

Independent Samples Test

Levene's Test

for Equality of

Variances t-test for Equality of Means

F Sig. t df

Sig.

(2-

tailed

)

Mean

Differenc

e

Std. Error

Differenc

e

95% Confidence

Interval of the

Difference

Lower Upper

Wages2012 Equal

variances

assumed

1.473 .234 2.42

1

32 .021 6203.000 2561.650 985.09

1

11420.90

9

Equal

variances

not

assumed

2.42

1

31.16

6

.021 6203.000 2561.650 979.61

2

11426.38

8

e. T-TEST FOR PAIRED SAMPLE

Next, in 2007 Malaysian economic was strong enough, to maintain this economic growth government had been implement many policies such as give increasing government expensive, decreasing the tax and so on . The wages in 2007 had been check and compare with wages in 2012 to see whether the policy that government launch is effective in economic growth in 2012. At the 5% significant level, is there enough evidence to support the claim that is a difference between mean in wages 2007 and 2012.

Paired Samples Statistics

Mean N

Std.

Deviation

Std. Error

Mean

Pair

1

Wages2012 30533.09 80 16782.760 1876.370

Wages2007 29228.45 80 15248.712 1704.858

Paired Samples Correlations

N Correlation Sig.

Pair

1

Wages2012 &

Wages2007

80 .990 .000

Paired Samples Test

Paired Differences

t df

Sig. (2-

tailed)Mean

Std.

Deviation

Std.

Error

Mean

95% Confidence

Interval of the

Difference

Lower Upper

Pair

1

Wages2012 -

Wages2007

1304.638 2734.789 305.759 696.040 1913.235 4.267 79 .000

f. ANOVA. CONDUCT THE MULTIPLE COMPARISON TESTS IF APPLICABLE. IF NOT APPLICABLE, STATE YOUR REASON.

a. ONE WAY ANOVA

Before we proceed to the final analysis that is examine the relationship between wages and

occupation. We wish to see whether there is a significant difference among wages (RM) it

takes by six occupations in 2012. In analysis if exist at least one of the mean is significantly

different, we will determine which one is by conduct post-hoc bonferroni analysis.

ANOVA

Wages2012

Sum of

Squares df Mean Square F Sig.

Between Groups 5.231E9 5 1.046E9 4.549 .001

Within Groups 1.702E10 74 2.300E8

Total 2.225E10 79

Multiple Comparisons

Wages2012

Bonferroni

(I) occupation (J) occupation

Mean

Difference (I-J) Std. Error Sig.

95% Confidence Interval

Lower Bound Upper Bound

Others Management -10659.912 6113.463 1.000 -29204.94 7885.11

Sales 732.521 7259.995 1.000 -21290.48 22755.52

Clerical 649.746 5282.422 1.000 -15374.34 16673.83

Service 6852.746 5282.422 1.000 -9171.34 22876.83

Education -16509.313 5550.054 .059 -33345.25 326.63

Management Others 10659.912 6113.463 1.000 -7885.11 29204.94

Sales 11392.433 7831.500 1.000 -12364.21 35149.08

Clerical 11309.659 6043.911 .979 -7024.38 29643.70

Service 17512.659 6043.911 .074 -821.38 35846.70

Education -5849.400 6279.170 1.000 -24897.09 13198.29

Sales Others -732.521 7259.995 1.000 -22755.52 21290.48

Management -11392.433 7831.500 1.000 -35149.08 12364.21

Clerical -82.775 7201.524 1.000 -21928.40 21762.85

Service 6120.225 7201.524 1.000 -15725.40 27965.85

Education -17241.833 7400.072 .338 -39689.75 5206.08

Clerical Others -649.746 5282.422 1.000 -16673.83 15374.34

Management -11309.659 6043.911 .979 -29643.70 7024.38

Sales 82.775 7201.524 1.000 -21762.85 21928.40

Service 6203.000 5201.770 1.000 -9576.43 21982.43

Education -17159.059* 5473.347 .037 -33762.31 -555.81

Service Others -6852.746 5282.422 1.000 -22876.83 9171.34

Management -17512.659 6043.911 .074 -35846.70 821.38

Sales -6120.225 7201.524 1.000 -27965.85 15725.40

Clerical -6203.000 5201.770 1.000 -21982.43 9576.43

Education -23362.059* 5473.347 .001 -39965.31 -6758.81

Education Others 16509.313 5550.054 .059 -326.63 33345.25

Management 5849.400 6279.170 1.000 -13198.29 24897.09

Sales 17241.833 7400.072 .338 -5206.08 39689.75

Clerical 17159.059* 5473.347 .037 555.81 33762.31

Service 23362.059* 5473.347 .001 6758.81 39965.31

b. TWO WAY ANOVA

After done with one way anova and multiple comparison analysis for wages period 2012 and six difference occupation, now can we conclude that there is another factor that affects wages that is industry?. To see which factor effect wages for period 2012, a research has been done with aim to study the effect of occupation in the four difference industry as block.

Between-Subjects Factors

Value Label N

occupation 0 Others 16

1 Management 10

2 Sales 6

3 Clerical 17

4 Service 17

5 Education 14

industry 0 Others 56

1 Manufacturing 13

2 Construction 2

3 Transportation and Telecommunication 9

Tests of Between-Subjects Effects

Dependent Variable:Wages2012

Source Type III Sum of Squares df Mean Square F Sig.

Corrected Model 5.790E9 8 7.238E8 3.122 .004

Intercept 1.977E10 1 1.977E10 85.253 .000

occupation 5.391E9 5 1.078E9 4.651 .001

industry 5.586E8 3 1.862E8 .803 .496

Error 1.646E10 71 2.318E8

Total 9.683E10 80

Corrected Total 2.225E10 79

a. R Squared = .260 (Adjusted R Squared = .177)

g. CHI-SQUARE TEST (INDEPENDENCE TEST)

Now to see whether wages and six type of occupation have a relationship, we will conduct

“chi-square” analysis. A random of sample 80 worker was chosen and classified according to

what occupation they work on. At the 5% significant level test whether there is a relationship

between wages at the end period 2012 and the type of occupation they work on.

Wages2012 * occupation Cross tabulation

Count

occupationTotal

Others Management Sales Clerical Service Education

Wages2012 10997 0 0 0 0 1 0 1

11186 1 0 0 0 0 0 1

11451 1 0 0 0 0 0 1

11702 0 0 1 0 0 0 1

11780 0 0 1 0 0 0 1

12285 0 1 0 0 0 0 1

13162 1 0 0 0 0 0 1

13312 0 0 0 0 1 0 1

13318 1 0 0 0 0 0 1

13787 0 0 0 0 1 0 1

15013 0 0 0 0 1 0 1

15160 0 0 0 0 1 0 1

15234 0 1 0 0 0 0 1

15957 0 0 1 0 0 0 1

16667 0 0 0 1 0 0 1

16789 0 0 0 1 0 0 1

16796 0 0 0 0 1 0 1

16817 0 0 0 1 0 0 1

17694 0 0 0 0 1 0 1

18121 0 0 0 1 0 0 1

18752 0 0 0 0 1 0 1

19227 0 0 0 1 0 0 1

19306 0 0 0 0 0 1 1

19388 1 0 0 0 0 0 1

19452 0 0 0 0 1 0 1

19879 0 0 0 1 0 0 1

19981 0 0 0 0 1 0 1

20852 2 0 0 0 0 0 2

21716 0 0 0 0 0 1 1

21994 0 1 0 0 0 0 1

22133 0 0 0 0 0 1 1

22485 0 0 0 1 0 0 1

23027 0 0 0 0 1 0 1

24509 0 0 0 0 0 1 1

25166 0 0 0 0 1 0 1

26614 1 0 0 0 0 0 1

26795 1 0 0 0 0 0 1

26820 0 0 0 0 0 1 1

28168 1 0 0 0 0 0 1

28219 0 0 0 1 0 0 1

28440 0 0 0 0 1 0 1

29191 1 0 0 0 0 0 1

29390 1 0 0 0 0 0 1

29407 0 0 0 0 1 0 1

29736 0 0 0 0 1 0 1

29977 0 0 0 0 1 0 1

30006 0 0 0 1 0 0 1

30133 1 0 0 0 0 0 1

31304 0 1 0 0 0 0 1

31702 0 0 0 1 0 0 1

31799 0 0 0 1 0 0 1

32094 0 0 0 1 0 0 1

32138 0 0 0 0 1 0 1

33389 0 1 0 0 0 0 1

33411 0 0 1 0 0 0 1

33461 0 1 0 0 0 0 1

33959 0 0 0 0 0 1 1

34484 0 0 0 1 0 0 1

34746 0 0 0 1 0 0 1

35185 0 0 0 1 0 0 1

36178 0 0 0 1 0 0 1

37664 0 0 0 0 0 1 1

37771 0 0 0 0 0 1 1

39888 0 0 0 1 0 0 1

44543 0 0 1 0 0 0 1

45976 0 0 1 0 0 0 1

49898 1 0 0 0 0 0 1

49974 0 1 0 0 0 0 1

50171 0 0 0 0 0 1 1

50235 1 0 0 0 0 0 1

52762 0 0 0 0 0 1 1

55777 0 1 0 0 0 0 1

57623 0 1 0 0 0 0 1

60152 0 0 0 0 0 1 1

66738 1 0 0 0 0 0 1

68573 0 0 0 0 0 1 1

75165 0 1 0 0 0 0 1

83443 0 0 0 0 0 1 1

83601 0 0 0 0 0 1 1

Total 16 10 6 17 17 14 80

Chi-Square Tests

Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 400.000a 390 .352

Likelihood Ratio 278.296 390 1.000

Linear-by-Linear Association .629 1 .428

N of Valid Cases 80

a. 474 cells (100.0%) have expected count less than 5. The minimum expected count

is .08.