QM - Individual Assignment V4

7/31/2019 QM - Individual Assignment V4

1/26

Statistical Analysis of Organisational Human Resource

Figures

ByYasaruwan Yuwanmini Landersz

201207044

PPQM 100 Quantitative Methods


2/26

IMPERIAL

INSTITUTE OF

HIGHER

EDUCATION

Student Name: Yasaruwan Yuwanmini Landersz

Student I.D: 201207044

Report Title: Statistical Analysis of Organisational Human

Resource Figures

Module: PPQM 100 Quantitative Methods

Describe any non-paper attachments: N/A

Submission- Date: Time: .

Plagiarism and Collusion are methods of cheating.

Plagiarism:Plagiarism means to take and use another persons ideas or works and pass these off asones own by failing to give appropriate acknowledgment. This includes material from any source published and unpublished works, staff or students, the Internet. For further information refer theguideline manual.


3/26

IMPERIAL

INSTITUTE OF

HIGHER

EDUCATION

IMPERIAL INSTITUTE OF HIGHER EDUCATION

ASSIGNMENT/COURSEWORK FEEDBACK FORM

Please fill the cages below numbered 1,2, & 3 before you handover the assignment.

1. Name of the student Yasaruwan Yuwanmini Landersz

2. Module PPQM 100 Quantitative Methods

3. Assignment/Course workTitle

Statistical Analysis of Organisational Human ResourceFigures

Please the appropriate columnDescriptor Over

70%

A

60-

69%

B

50-

59%

C

40-

49%

D

30-

39%

E

29-

0%

F

Coverage of relevant Literature

Research & Analysis

Understanding of concept

Application of concepts to understand

the question/issue/problem

Suitability of structure used

Style Grammar/Presentation

Acknowledgement of sources &

reference List


4/26

IMPERIAL

INSTITUTE OF

HIGHER

EDUCATION

Table of Contents

1. Introduction ............................................................................................................ 12. Methodology ........................................................................................................... 23. Data Description ..................................................................................................... 44. Analysis and Interpretation ..................................................................................... 6

Descriptive Analysis .................................................................................................. 6Multiple Correlation and Regression Analysis ........................................................ 10Selective Correlation and Regression Analysis ....................................................... 12Residual Analysis of Observed Correlation ............................................................. 14

5. Conclusion ............................................................................................................ 176. References ............................................................................................................ 18Annexure A: Data Set (Including Residuals)................................................................. I


5/26

IMPERIAL

INSTITUTE OF

HIGHER

EDUCATION

Table of Tables

Table 1: Descriptive Statistics of Numerical Variable .................................................. 6Table 2: Correlations of Multiple Variables against Basic Salary ............................... 10Table 3: Model Summeryb ........................................................................................... 10Table 4: Coefficientsa................................................................................................... 11Table 5: Correlation of Age Vs Basic Salary ............................................................... 12Table 6: Model Summaryb ........................................................................................... 13Table 7: Descriptive Statistics of Residuals................................................................. 15Table 8: Correlations.................................................................................................... 15Table 9: Model Summaryb ........................................................................................... 15Table 10: Coefficientsa................................................................................................. 16


6/26


7/26

IMPERIAL

INSTITUTE OF

HIGHER

EDUCATION

2.Methodology

Keeping in line with the requirement of this assignment, a step by step approach

was adopted in order to analyse the above mentioned data. As of the requirement, a

descriptive analysis and a correlation & regression analysis was carried out, where as

the correlation & regression analysis was the main focus of the assignment. The

following are the sequence of steps carried out during the execution of thisassignment and preparation of this report:

1. Variable Selection

The first step was to derive which parameters to select for this analysis. Since the

database accessed regarding ABC Group of companies included a vast and

comprehensive scope of data related to HR metrics it was a tough choice to selectrelevant information. However since employee salaries were of main concern for

many HR and managerial professionals Basic Salary as well as some other

determinants assumed to have an effect on this was selected, based on general

know how.

2. Sample Selection


8/26

IMPERIAL

INSTITUTE OF

HIGHER

EDUCATION

4. Descriptive Analysis

A descriptive analysis was carried out for a few variables which were assumed to

be critical with respect to their contribution to the variance of Basic Salary, while

Basic Salary that was collected was also subject to this. This was done in order to

determine the validity of the selected sample for the correlation and regression

analysis.

The Mean, Median, Mode and Standard Deviation were observed to determine the

central tendency of the information. Also the Co-efficient of variance was derived

to determine the consistency of each critical variable.

5. Multiple Correlation and Regression Analysis

As mentioned above it was identified that several variables maybe key factors indetermining an employees Basic Salary in ABC Group of companies. Therefore

initially all of these critical variables were put against the Basic Salary for a

multiple correlation (how much these multiple variables were related to the

change in Basic Salary) and regression analysis (Up to which effect sis Basic

Salary change when each of these variables changed). Based on this the

significance of correlation was to be observed against salary and the most

i ifi d i id ifi d b b i f h i li i f h


9/26

IMPERIAL

INSTITUTE OF

HIGHER

EDUCATION

3.Data Description

The data sample that was considered for this analysis was randomly picked with no

special criteria and information with respect to the following variables were extracted

for 30 employees:

1. Employee wise Sector which sector does each employee belong to

(Categorical data in the nominal scale)

2. Employee Designation what is the designation held by each employee


3. Age the ages of all 30 employees

(Numerical data)

4. Gender the gender of each employee


5. Qualification Type whether an academic, professional


10/26


11/26

IMPERIAL

INSTITUTE OF

HIGHER

EDUCATION

4.Analysis and Interpretation

As per the methodology the Variable Selection, Sample Selection and Data Entry

in to SPSS Software are assumed to be completed. This section will highlight the

various methods used during this analysis exercise and how it was executed in line

with the proposed methodology.

Descriptive Analysis

The main focus of this exercise was to determine the factors that would affect

the Salary of an employee. Therefore during this descriptive analysis, instead of going

into detailed descriptive, the focus is to identify the quality of data obtained in order

to proper analyse the correlation in question.

First let us take a look at the numerical variables:

Table 1: Descriptive Statistics of Numerical Variable

Age Basic Salary

Related

Experience

Total

Experience

N Valid 30 30 30 30


12/26


13/26

IMPERIAL

INSTITUTE OF

HIGHER

EDUCATION


14/26

IMPERIAL

INSTITUTE OF

HIGHER

EDUCATION

As you can see above the Gender distribution of the sample has 53.3% Males (16

Employees) and 46.7% females (14 Employees).

Figure 4.4: Qualification Distribution of Frequencies


15/26

IMPERIAL

INSTITUTE OF

HIGHER

EDUCATION

From what is observed in the distribution of employees in each sector we see that

more employees are based at the corporate office rather than specialising in each field.

Form our knowledge of the business we can presume that they may be staff more

related to clerical work rather than specialised jobs, which will also indicate why the

Basic Salary histogram is left skewed.

Multiple Correlation and Regression AnalysisAs an initial step we will blindly compare afore mentioned variables (they will

be assumed to be the independent variables) with respect to the variations of Basic

Salary (this will b the dependant variable) to determine the best correlative variable

i.e. the variable that affects the change of Basic Salary significantly. For this let us

observe the correlations obtained by SPSS.

Table 2: Correlations of Multiple Variables against Basic Salary

AgeQualification

Level

Related

Experience

Total

Experience

Pearson Correlation Basic Salary .742 .153 .156 .103

Sig. (1-tailed) Basic Salary .000 .210 .206 .294

N B i S l 30 30 30 30


16/26

IMPERIAL

INSTITUTE OF

HIGHER

EDUCATION

When considering the above model we can see that our set of variables is

strongly and positively correlated with Basic Salary considering the correlation

coefficient (R) value being close to +1. This means that as our variables will increase

the Basic Salary will also grow. In practical perspective this is also true.

Considering the coefficient of determination (R2) we can also observe that

60% of the variation in Basic Salary can be explained by the variation of thecollective independent variables given. Let us now observe the collective prediction

model or Trend Line as well as the scatter plot for this data set.

Table 4: Coefficientsa

Model

Unstandardized

Coefficients

Standardized

Coefficients

t Sig.

95.0% Confidence

Interval for B

B Std. Error Beta Lower Bound

Upper

Bound

1 (Constant) -245774.936 51103.892 -4.809 .000 -351025.373 -

140524.

500

A 9171 076 1570 647 753 5 839 000 5936 269 12405 8


17/26


18/26


19/26

IMPERIAL

INSTITUTE OF

HIGHER

EDUCATION

Figure 4.7: Scatter Plot of Age vs. Basic Salary

As observed the correlation and regression is clearly visible for the selected sample


20/26

IMPERIAL

INSTITUTE OF

HIGHER

EDUCATION

Table 7: Descriptive Statistics of Residuals

Mean Std. Deviation N

Residual On Age -.0012 37080.26836 30

Age 30.10 4.544 30

As shown above the mean of the residual is roughly 0 and the values for it are

spared across a large area when compared to the mean sine the standard deviation is

37080.26836. This is consistent for all values when the coefficient of variance is also

close to zero (3.236222533099272e-7).

Table 8: Correlations

Age

Pearson Correlation Residual On Age .000

Sig. (1-tailed) Residual On Age .500

N Residual On Age 30

T bl 9 M d l S b


21/26

IMPERIAL

INSTITUTE OF

HIGHER

EDUCATION

Table 10: Coefficientsa

Model

Unstandardized

Coefficients

Standardized

Coefficients

t Sig.

95.0% Confidence

Interval for B

B Std. Error Beta Lower Bound

Upper

Bound

1 (Constant) -.001 46930.578 .000 1.000 -96132.931 96132.9

29

Age .000 1542.262 .000 .000 1.000 -3159.181 3159.18

1

a. Dependent Variable: ResidualOnAge

As observed above the slope and the constant of Age against the Residuals is

almost 0. This implies that values are spread almost equally on both sides of the trend

line when we look at the scatter plot. This further confirms the independence of Age

with respect to the residual variable which determines basic salary.

Figure 4.8: Scatter Plot of Age vs. Residual


22/26

IMPERIAL

INSTITUTE OF

HIGHER

EDUCATION

5.Conclusion

As witnessed during the statistical analysis, several variables were first

statistically analysed to see if they were valid. Once the validity of the sample of data

was confirmed next several variables were put up against a Multiple Regression

analysis against Basic Salary. During this process the variable Age stood out and

therefore Age alone was analysed against Basic Salary for correlation. Once the highcorrelation was also identified it was further analysed and the linearity, independence

and homoscedasticity was established.

This proves that as an employee's age in ABC Group of Companies increases their

Basic Salary should also increase. But in reality this is not true. There are many

determinants of employee basic salary like qualifications, experience and

performance. But as a coincidence we know that employees with more workexperience will be older. Also experience will impact their performance as well. So

the correlation between Age and Basic Salary can only be a coincidental correlation.

However statistically we can arrive at the conclusion that age does have an impact on

an employees basic salary.

Also another fact is that only 55% of the variation of Basic Salary can be


23/26

IMPERIAL

INSTITUTE OF

HIGHER

EDUCATION

6.References

ABC Group of Companies*, 2012.ABC Group of Companies Web Site. [Online]

Available at: *

[Accessed 29 August 2012].

hSenid Business Solutions, 2012.Sample HR Database for Indian Operations,

Chennai, India: s.n.


24/26


25/26

2

007684 EMP14 Corporate Office

27 Male ProfessionalQualification

6 17,000.00 0 0 26,728.10 -9,728.10 25,088.22 -8,088.22

007781 EMP15 Corporat

e Office

26 Female Professional

Qualification

6 40,000.00 1 1 30,639.12 9,360.88 16,055.93 23,944.08

008158 EMP16 FMCG 44 Male ProfessionalQualification

6 250,000.00 0 0 182,636.39 67,363.61 178,637.24 71,362.77

008701 EMP17 TransportationSector


6 100,000.00 0 8 57,692.63 42,307.37 70,249.70 29,750.31

008708 EMP18 FMCG 39 Female Higher Diploma 6 100,000.00 0 0 136,781.01 -36,781.01 133,475.76 -33,475.76

006890 EMP19 FMCG 31 Female Other EducationalQualification(Diploma)

4 55,000.00 0 3 49,533.67 5,466.33 61,217.40 -6,217.40

006689 EMP20 FMCG 30 Male Other EducationalQualification

(Diploma)

3 40,000.00 0 0 41,799.33 -1,799.33 52,185.10 -12,185.10

007019 EMP21 Leisure 28 Male Other EducationalQualification(Diploma)

3 25,000.00 3 3 62,703.48 -37,703.48 34,120.52 -9,120.52

007415 EMP22 TransportationSector

24 Female Primary &SecondaryEducation (SchoolLevel)

3 16,000.00 0 2 -16,949.83 32,949.83 -2,008.66 18,008.67

007694 EMP23 Healthcare Sector

36 Female Primary &SecondaryEducation (SchoolLevel)

1 30,000.00 0 7 75,501.64 -45,501.64 106,378.88 -76,378.88



1 40,000.00 0 0 24,333.60 15,666.40 43,152.81 -3,152.81


25 Female ProfessionalQualification

6 15,000.00 0 0 8,385.94 6,614.06 7,023.63 7,976.37


24 Male Other EducationalQualification(Diploma)

6 14,375.00 0 0 -785.13 15,160.13 -2,008.66 16,383.67


26/26

3

004892 EMP27 Leisure 28 Male ProfessionalQualification

3 25,000.00 0 0 23,457.18 1,542.82 34,120.52 -9,120.52

004893 EMP28 Healthcar

e Sector

31 Male Higher Education

Qualification -Degree

6 30,000.00 0 0 63,412.40 -33,412.40 61,217.40 -

31,217.40

004894 EMP29 FMCG 29 Male ProfessionalQualification

7 28,000.00 0 0 49,217.58 -21,217.58 43,152.81 -15,152.81


33 1 6 6 150,000.00 0 0 81,754.55 68,245.45 79,281.99 70,718.01

QM - Individual Assignment V4

Documents

Transcript of QM - Individual Assignment V4