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# Quantitative and qualitative data analysis

## Introduction:

This kind of essayconsists of two parts. Part A is an analysis of quantitative data and Portion B is usually an analysis of qualitative data. We will give you the information in both equally cases. Each part should consist of data analysis, commentary and model. You should write well-structured report of among 750 and 1000 words for each part plus any kind of diagrams and charts you produce and a list of references.

## Part A

The report does the data analysis of worker data to reply to some human resources related questions using the SPSS software. The screenshots with the entire data are provided in the Appendix 1 and Appendix installment payments on your The statement provides the results for the HR (human resources) problems after performing the data research of employees data. The outcome of the research reveals that workers among 25 and 55 years old form the major percentage of employees inside the organization where the mean regarding all the complete workforce is 39. 19 years. Nevertheless , workers among 18 and 29 years old consist of 31. 4% with the workforce. However , workers among age of 40 and forty consist of twenty-three. 2% of employees within the organization. Personnel between forty one and 50 years of age contain 23. 2% of the workforce while workers between 51 and 63 of age consist of 21. seven percent.

The studies also uncover the portion of the workers belonging to each ethnic group. The entire staff is 70 in quantity and White colored ethnic group forms fifty-one. 4% (36) of all employees while Asians rank second with twenty-five. 7% (18). West Indians are twenty percent (14) although Africans happen to be 2 . 9% (2). The regular income of all workers is usually \$7, 819. 12. A result of the regression analysis reveals that the range of salary raises with an increase in the number of years performed in the organization because the p-value is almost 8. 6, which is more than 0. 05 the significant level. In addition, the average salary is statistically significant in different skill categories. The p-value is usually. 074 which is more than zero. 05 the numerous level exhibiting that as skills of workers maximize, their income also raises. Moreover, there is a significant difference among males and females who also attended the firms getting together with last month since 58. 3% of guys attended the meeting while 41. seven percent of woman attended the meeting a month ago. The result of the “Pearson Chi-Square” shows that? (1) = zero. 206, l =. 600 revealing that there an important difference between your proportion of males and females who have attended the firms appointment last month.

Following is the evaluation of employee data:

1 . What is age distribution of the workforce? (Use, for example , Histogram)

EQ VARIABLES=age

/STATISTICS=RANGE MEAN

/HISTOGRAM

/ORDER=ANALYSIS.

Frequencies

[DataSet1] C: UsersucerDesktop188375_jss. sav

Statistics

Age

N

Valid

69

Missing

you

Mean

39, 19

Selection

45

Age

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

18

2

2, 9

2, 9

a couple of, 9

nineteen

2

2, 9

a couple of, 9

21

2

a couple of, 9

2, 9

8, 7

23

1

you, 4

one particular, 4

twelve, 1

twenty six

3

5, 3

5, 3

13, 5

twenty seven

2

a couple of, 9

2, 9

18, 4

twenty-eight

3

5, 3

4, 3

21, 7

29

6

almost eight, 6

eight, 7

35, 4

35

1

one particular, 4

one particular, 4

31, 9

thirty-one

4

five, 7

a few, 8

37, 7

thirty-two

2

2, 9

2, 9

forty five, 6

33

1

one particular, 4

you, 4

42, 0

34

1

you, 4

1, 4

43, 5

35

1

you, 4

one particular, 4

44, 9

37

2

two, 9

2, 9

forty seven, 8

38

1

you, 4

you, 4

49, 3

39

1

one particular, 4

one particular, 4

55, 7

40

2

two, 9

a couple of, 9

53, 6

forty two

2

two, 9

2, 9

56, 5

43

3

some, 3

four, 3

70, 9

forty-five

1

you, 4

one particular, 4

sixty two, 3

46

2

a couple of, 9

2, 9

66, 2

47

1

1, 4

1, 4

66, 7

forty-eight

5

several, 1

several, 2

73, 9

50

2

a couple of, 9

2, 9

76, 8

fifty-one

1

1, 4

one particular, 4

78, 3

52

2

two, 9

two, 9

seventy eight, 2

53

4

a few, 7

a few, 8

87, 0

54

2

a couple of, 9

two, 9

fifth 89, 9

fifty five

2

a couple of, 9

two, 9

92, 8

57

1

1, 4

you, 4

94, 2

59

1

you, 4

one particular, 4

96, 7

sixty one

1

you, 4

you, 4

97, 1

62

1

one particular, 4

one particular, 4

98, 6

63

1

1, 4

1, 4

75, 0

Total

69

98, 6

75, 0

Absent

0

one particular

1, 4

Total

70

100, 0

2 . What proportion of employees is owned by each ethnic group? (Use, for example , Club Graph)

FREQUENCIES VARIABLES=ethnicgp

/STATISTICS=RANGE MEAN

/GROUPED=ethnicgp

/BARCHART FREQ

/ORDER=ANALYSIS.

Eq

[DataSet1] C: UsersucerDesktop188375_jss. sav

Stats

Ethnic Group

N

Valid

70

Absent

0

Indicate

1, seventy four

Range

3

Ethnic Group

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

light

36

fifty-one, 4

51, 4

51, 4

oriental

18

25, 7

25, 7

77, 1

west indian

16

20, zero

20, zero

97, you

african

two

2, on the lookout for

2, 9

100, zero

Total

75

100, zero

100, 0

3. What is the average salary? (Use, for example , Descriptive Stats, Descriptives)

DESCRIPTIVES VARIABLES=income

/STATISTICS=MEAN STDDEV MIN MAX.

Descriptives

[DataSet1] C: UsersucerDesktop188375_jss. sav

Descriptive Statistics

N

Minimum

Optimum

Mean

Std. Deviation

Profits

68

5900

10500

7819, 12

997, 947

Valid N (listwise)

68

four. How is definitely number of years proved helpful related to earnings, if at all? (Use, for example , Linear Regression)

REGRESSION

/MISSING LISTWISE

/STATISTICS COEFF OUTS CI(95) R ANOVA

/CRITERIA=PIN(. 05) POUT(. 10)

/NOORIGIN

/DEPENDENT income

/METHOD=ENTER years.

Regression

[DataSet1] C: UsersucerDesktop188375_jss. sav

Variables Entered/Removeda

Model

Variables Moved into

Variables Taken off

Method

one particular

Years Workedb

.

Enter

a. Dependent Varying: Income

n. All requested variables joined.

Model Overview

Model

Ur

R Rectangular

An std. Error of the Estimate

one particular

, 340a

, 116

, 102

945, 711

a. Predictors: (Constant), Years Performed

ANOVAa

Unit

Sum of Squares

df

Mean Sq .

F

Sig.

1

Regression

7696787, 937

1

7696787, 937

almost eight, 606

, 005b

Residual

59028359, 122

sixty six

894369, 078

Total

66725147, 059

67

a. Based mostly Variable: Salary

b. Predictors: (Constant), Years Worked

Coefficientsa

Model

Unstandardized Coefficients

Standardised Coefficients

to

Sig.

ninety five, 0% Confidence Interval pertaining to B

W

Std. Error

Beta

Decrease Bound

Top Bound

you

(Constant)

7410, 810

180, 346

41, 092

, 500

7050, 737

7770, 883

Years Worked

31, 841

10, 854

, 340

a couple of, 934

, 005

10, 169

53, 511

a. Reliant Variable: Salary

5. Just how different would be the average incomes of the different skill classes? (Use, for example , One-way ANOVA)

ONEWAY income BY skill

/POLYNOMIAL=1

/STATISTICS DESCRIPTIVES

/MISSING ANALYSIS

/POSTHOC=TUKEY ALPHA(0. 05).

Oneway

[DataSet1] C: UsersucerDesktop188375_jss. sav

Descriptives

Profits

N

Mean

Std. Deviation

Std. Problem

95% Self confidence Interval intended for Mean

Minimum

Maximum

Lower Bound

Upper Bound

unskilled

14

7628, 57

730, 046

195, 113

7207, 06

8050, 09

6500

8800

semi-skilled

18

7288, 89

741, 135

174, 687

6920, 33

7657, 45

5900

8800

reasonably skilled

twenty

8095, 00

931, 029

208, 185

7659, 21

8530, seventy four

6200

9500

highly skilled

of sixteen

8237, 55

1267, 478

316, 869

7562, eleven

8912, fifth there’s 89

6400

10500

Total

sixty-eight

7819, doze

997, 947

121, 019

7577, 56

8060, 67

5900

10500

ANOVA

Salary

Sum of Squares

df

Mean Sq .

F

Sej.

Between Organizations

(Combined)

9891797, 852

several

3297265, 951

3, 713

, 016

Unweighted

5288030, 013

1

5288030, 013

, 017

Weighted

6062905, 680

1

6062905, 680

6, 827

, 011

Change

3828892, 73

2

1914446, 086

a couple of, 156

, 124

Within Organizations

56833349, 206

64

888021, 081

Total

66725147, 059

67

Post Hoc Checks

Multiple Evaluations

Dependent Variable: Income

Tukey HSD

(I) rated skill

(J) rated skill

Suggest Difference (I-J)

Std. Mistake

Sig.

95% Confidence Time period

Lower Destined

Upper Sure

unskilled

semi-skilled

339, 683

335, 804

, 743

-546, 12

1225, 48

quite skilled

-466, 429

328, 377

, 491

-1332, 63

399, 78

highly skilled

-608, 929

344, 864

, 299

-1518, 62

300, 77

semi-skilled

not skilled

-339, 683

335, 804

, 743

-1225, 48

546, 12

fairly skilled

-806, 111

306, 163

, 051

-1613, seventy two

1, 50

highly skilled

-948, 611*

323, 784

, 024

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Category: Essay,

Topic: Data analysis,

Words: 2711

Published: 03.25.20

Views: 112

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