Data Analysis: Types of Questions, Descriptive Analysis, Correct Statistical Tests

This document provides an analysis of data related to the impact of length of service, age, gender, and LDH-5:LDH-1 ratio on health and liver damage. It discusses the types of questions, descriptive analysis, and correct statistical tests used to determine the relationship between variables. The findings suggest that longer length of service and older age have a negative impact on health, while gender plays a role in liver damage. The document also includes frequency tables and information on one-way ANOVA.

Data analysis

Contents
INTRODUCTION ..........................................................................................................................1
TASK ..............................................................................................................................................1
Types of question...................................................................................................................1
Descriptive analysis ...............................................................................................................3
Correct statistical tests............................................................................................................5
CONCLUSION .............................................................................................................................13
REFERENCES .............................................................................................................................14
Appendix .......................................................................................................................................15

INTRODUCTION
The process of recording, evaluating, dissecting information related to a specific topic and them
presenting the main finding into acceptable format which ease the system of decision making is
known as data analysis (Ho, 2013). In order to understand the importance of data analysis sample
of 127 worker working in different production plant are selected that help in extracting valuable
results.
In this report, meaningful evidence of potential health hazard between workers at the
different industries as indicated by the LDH-5: LDH-1 ratio is discussed by using various useful
statistical and descriptive test.
TASK
Types of question
There has been some important question which are formulated to determine the accurate
relationship between the different variable. Such as:
1: The relationship between sector and length of service
Hypothesis: Whether the longer period of service in a specific sector have adverse impact on
health.
Descriptive Statistics
Mean Std.
Deviation
N
sector 2.58 1.087 127
length of service
(years) 8.93 6.452 127
Correlations
1

sector length of
service
(years)
sector
Pearson
Correlation 1 -.008
Sig. (2-tailed) .932
N 127 127
length of service
(years)
Pearson
Correlation -.008 1
Sig. (2-tailed) .932
N 127 127
Nonparametric Correlations
Correlations
sector length of
service
(years)
Spearman's
rho
sector
Correlation
Coefficient 1.000 .036
Sig. (2-tailed) . .692
N 127 127
length of service
(years)
Correlation
Coefficient .036 1.000
Sig. (2-tailed) .692 .
N 127 127
2

From the above calculated correlation table by applying both Spearman's and Pearson it
is determined that significance level of sector and length of service is above the predetermined
significance level. Such as through Spearman's method it is .692 and by Pearson is .932 which is
higher that α = 0.05. Therefore, the above hypothesis is being accepted and it is true in the
scenario that if a worker is worker is working for longer period of time in any of the above
mention industry than there will be dangerous impact over their life.
2. The relationship between LDH-5:LDH-1 and length of service
Hypothesis: Is longer length of service have a negative effect on the liver of worker.
Descriptive Statistics
Mean Std.
Deviation
N
LDH-5:LDH-1 .65866
1 .1474996 127
length of service
(years) 8.93 6.452 127
Correlations
LDH-
5:LDH-1
length of
service
(years)
LDH-5:LDH-1
Pearson
Correlation 1 .377**
Sig. (2-tailed) .000
N 127 127
length of service
(years)
Pearson
Correlation
.377** 1
3

Sig. (2-tailed) .000
N 127 127
**. Correlation is significant at the 0.01 level (2-tailed).
Nonparametric Correlations
Correlations
LDH-
5:LDH-1
length of
service
(years)
Spearman's
rho
LDH-5:LDH-1
Correlation
Coefficient 1.000 .273**
Sig. (2-tailed) . .002
N 127 127
length of service
(years)
Correlation
Coefficient .273** 1.000
Sig. (2-tailed) .002 .
N 127 127
**. Correlation is significant at the 0.01 level (2-tailed).
The above tabular presentation clearly states that the significance level of LDH-5: LDH-1
and length of services is not acceptable. As through Pearson method the significance level is .000
and by Spearman's it is .002 which states that it is lower than the standard p value. Therefore the
above hypothesis has been rejected because the value of p is lower than 0.05 and there is little
impact of length of services on liver damage of worker.
3. The relationship between age and gender
Hypothesis: Does male ages is higher than female age while working in different industry.
Descriptive Statistics
4

Mean Std.
Deviation
N
age
(years) 39.12 11.367 127
gender 1.41 .494 127
Correlations
age
(years)
gender
age
(years)
Pearson
Correlation 1 .056
Sig. (2-tailed) .529
N 127 127
gender
Pearson
Correlation .056 1
Sig. (2-tailed) .529
N 127 127
Nonparametric Correlations
Correlations
age
(years)
gender
Spearman's
rho
age
(years)
Correlation
Coefficient 1.000 .051
Sig. (2-tailed) . .566
N 127 127
5

gender
Correlation
Coefficient .051 1.000
Sig. (2-tailed) .566 .
N 127 127
The above correlation calculation defines that significance level of age with year is .056
through persons method and by applying Spearman's method the correlation is .051. From both
the method the significance level is around 0.529 and 0.566 which is closer to the standard value
of alpha. Thus, from the above discussion it is stated that the above hypothesis is being true
which means that the males age is more than female working in different industry.
4. The relationship between LDH.. and age
Hypothesis: Is more age person have a chance of getting liver damage while working in
hazardous industry.
Descriptive Statistics
Mean Std.
Deviation
N
LDH-
5:LDH-1
.65866
1 .1474996 127
age (years) 39.12 11.367 127
Correlations
LDH-
5:LDH-1
age
(years)
LDH-
5:LDH-1
Pearson
Correlation 1 .158
Sig. (2-tailed) .076
6

N 127 127
age (years)
Pearson
Correlation .158 1
Sig. (2-tailed) .076
N 127 127
Nonparametric Correlations
Correlations
LDH-
5:LDH-1
age
(years)
Spearman's
rho
LDH-
5:LDH-1
Correlation
Coefficient 1.000 .186*
Sig. (2-tailed) . .036
N 127 127
age (years)
Correlation
Coefficient .186* 1.000
Sig. (2-tailed) .036 .
N 127 127
*. Correlation is significant at the 0.05 level (2-tailed).
In the above table, it is clearly define that both method shows the different results
because the standard deviation of LDH-5: LDH-1 is 0.1474996 and age is 11.367. The Pearson
method states that alpha p value of relationship between age and liver damage ratio is .076 which
is higher than 0.05. Due to which above mention hypothesis is being accepted and it is true that
older age worker have a higher chances of getting liver diseases which might affect the working
capacity. While on the other side, through Spearman's corrections method the significance level
is .036 which is lower than standard p value. This state that age does not matter at all for getting
7

liver diseases because if an individual is working in highly dangerous chemical industry they will
be suffering from liver infection.
5. The relationship between LDH.. and gender
Hypothesis: Does depending upon gender an individual have a higher chances of getting liver
infected while working in different sectors.
Descriptive Statistics
Mean Std.
Deviation
N
LDH-
5:LDH-1
.65866
1 .1474996 127
gender 1.41 .494 127
Correlations
LDH-
5:LDH-1
gender
LDH-
5:LDH-1
Pearson
Correlation 1 .017
Sig. (2-tailed) .846
N 127 127
gender
Pearson
Correlation .017 1
Sig. (2-tailed) .846
N 127 127
Correlations
8

LDH-
5:LDH-1
gender
Spearman's
rho
LDH-
5:LDH-1
Correlation
Coefficient 1.000 .002
Sig. (2-tailed) . .984
N 127 127
gender
Correlation
Coefficient .002 1.000
Sig. (2-tailed) .984 .
N 127 127
The above table shows that there have a strong corrections between the gender and
proportionate of Lactate dehydrogenase. It is clearly observed in the above table that significance
level which is 0.846 form Person method of correlation is greater than nominal p value. Similarly
on the other side the level of Alpha from Spearman's method is 0.984 which describe that gender
usually have a greater role in getting liver diseases while working in several chemical sectors.
In order to ascertain the exact connection among the two same variable bivariate
correlation analysis is performed which is helpful in giving understandable results (Demirkan
and Delen, 2013). It is observed that this analysis is supportive in producing the sample
coefficient correlation that benefits to amount the strong point and direction of liner connection
among the sequel of continuous factors.
From the above table it is determined that correlation of length of service with itself is r =1 and
the total count for non-missing observation for the same is n=127. On the other column,
correlation of age and length of service is r =0.523 depending on total observation 127. Thus, it
can be stated that length of service and age have a statistically important linear connection
(p < .001).
To recognize the connection between the two variable i.e. is LDH5LDH1 (which is the
proportionate of Lactate dehydrogenase) and length of actual service in the specific sector
(Sagiroglu and Sinanc, 2013).
9

Descriptive analysis
Descriptive analysis or statistic is mainly used to summarise the collected data by using different
method such as in case of categorical data cross tabs or frequencies approach are applied
(Zsambok, 2014). Where as in case of scale level descriptive or summaries is used and if data
include multiple response to a set of questions than multiple response is used to determine
significant results. Below mention are the frequencies tables that shows the different finding to
the respective case:
Frequency table for sector and length of service
Statistics
sector length of
service
(years)
N Valid 127 127
Missing 0 0
Mean 8.93
Median 8.00
Mode 3a
Std. Deviation 6.452
Variance 41.622
Range 33
Minimum 1
Maximum 34
a. Multiple modes exist. The smallest
value is shown
Frequency table for sector and LDH5: LDH1
Statistics
sector LDH-
5:LDH-1
N Valid 127 127
10

Missing 0 0
Mean .658661
Median .640000
Mode .6400
Std. Deviation .1474996
Variance .022
Range .7600
Minimum .3400
Maximum 1.1000
Frequency table for gender and age
Statistics
gender age (years)
N Valid 127 127
Missing 0 0
Mean 39.12
Median 39.00
Mode 40
Std. Deviation 11.367
Variance 129.200
Range 48
Minimum 17
Maximum 65
Frequency table for gender and LDH-5: LDH-1
Statistics
gender LDH-
5:LDH-1
N Valid 127 127
Missing 0 0
Mean .658661
Median .640000
11

Mode .6400
Std. Deviation .1474996
Variance .022
Range .7600
Minimum .3400
Maximum 1.1000
Correct statistical tests
One way ANOVA
When the data is in the categorical form then independent factor (with two or more
categories) and a naturally dependent variable for a spaced period, a one-way variability study
(ANOVA) has been used (Raiborn and Sivitanides, 2015). It helps to check the variations in the
results of the dependent variable divided by the independent variable measures. Although the
one-way ANOVA is sometimes prefaced with a specious analysis. In the context of above case
the specific method is implemented to determine the key finding such as:
ANOVA (Table 1)
length of service (years)
Sum of
Squares
df Mean Square F Sig.
Between
Groups 2541.261 50 50.825 1.429 .079
Within Groups 2703.101 76 35.567
Total 5244.362 126
12

ANOVA (Table 2)
length of service (years)
Sum of
Squares
df Mean Square F Sig.
Between
Groups 3199.973 44 72.727 2.917 .000
Within Groups 2044.389 82 24.932
Total 5244.362 126
13

ANOVA (Table 3)
age (years)
Sum of
Squares
df Mean Square F Sig.
Between
Groups 6441.125 50 128.822 .995 .500
Within Groups 9838.104 76 129.449
Total 16279.228 126
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From the different one way ANOVA table above it has been determined that the
respective statistical test is helpful in determining the required outcome. This is because the
following results are according to the main assumption of this method (Ratiu, 2015). The main
assumption of ANOVA test is dependent variable should be measured at the interval or ratio
level, Independent variable may compose of several or more different numerical categories.
Therefore, one-way ANOVA is generally required in this case in which more categorical and
independent groups are considered. It is interpreted that only the hypothesis related with mean of
age and LDH-5: LDH-1 because the significance level is p = 0.5. On the other side, different
tables show the ANOVA test result between Mean of length of service and age where p < 0.05 so
that the hypothesis is rejected. Similarly, the ANOVA table of mean of length of service years
and and LDH-5: LDH-1 is also get rejected because the p < 0.079.
Chi square test
In case If a specific test is needed to be performed to know whether there's a connection
between certain categorical variables a chi-square check is used (Ruch and Taylor, 2015). In
SPSS, the chi-sq method is utilized to access the sample statistics or its corresponding p-value
from the crosstabs command's. There is only a specific assumption related with this test it that is
anticipated value for every cell is consider to be 5.
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Descriptive Statistics
N Mean Std.
Deviation
Minimum Maximum
length of service
(years) 127 8.93 6.452 1 34
LDH-5:LDH-1 127 .658661 .1474996 .3400 1.1000
Test Statistics
length of
service
(years)
LDH-5:LDH-1
Chi-Square 76.740a 61.339b
df 24 50
Asymp. Sig. .000 .131
a. 0 cells (0.0%) have expected frequencies less than 5. The
minimum expected cell frequency is 5.1.
b. 51 cells (100.0%) have expected frequencies less than 5.
The minimum expected cell frequency is 2.5.
Descriptive Statistics
N Mean Std.
Deviation
Minimum Maximum
LDH-
5:LDH-1 127 .658661 .1474996 .3400 1.1000
age (years) 127 39.12 11.367 17 65
Test Statistics
LDH-
5:LDH-1
age (years)
Chi-Square 61.339a 44.850b
16

df 50 44
Asymp.
Sig. .131 .436
a. 51 cells (100.0%) have expected frequencies less than 5. The
minimum expected cell frequency is 2.5.
b. 45 cells (100.0%) have expected frequencies less than 5. The
minimum expected cell frequency is 2.8.
Descriptive Statistics
N Mean Std.
Deviation
Minimum Maximum
age (years) 127 39.12 11.367 17 65
length of service
(years) 127 8.93 6.452 1 34
Test Statistics
age (years) length of service (years)
Chi-Square 44.850a 76.740b
df 44 24
Asymp.
Sig. .436 .000
a. 45 cells (100.0%) have expected frequencies less than 5. The minimum
expected cell frequency is 2.8.
b. 0 cells (0.0%) have expected frequencies less than 5. The minimum expected
cell frequency is 5.1.
Regression Test
Variables Entered/Removeda
Model Variables
Entered
Variables
Removed
Method
17

1 LDH-5:LDH-
1b . Enter
a. Dependent Variable: age (years)
b. All requested variables entered.
Model Summaryb
Model R R Square Adjusted R
Square
Std. Error of
the Estimate
1 .158a .025 .017 11.269
a. Predictors: (Constant), LDH-5:LDH-1
b. Dependent Variable: age (years)
ANOVAa
Model Sum of
Squares
df Mean
Square
F Sig.
1
Regression 405.491 1 405.491 3.193 .076b
Residual 15873.738 125 126.990
Total 16279.228 126
a. Dependent Variable: age (years)
b. Predictors: (Constant), LDH-5:LDH-1
Coefficientsa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig. Collinearity
Statistics
B Std. Error Beta Tolerance VIF
1
(Constant) 31.107 4.593 6.772 .000
LDH-
5:LDH-1 12.162 6.806 .158 1.787 .076 1.000 1.000
a. Dependent Variable: age (years)
18

Variables Entered/Removeda
Model Variables
Entered
Variables
Removed
Method
1 LDH-5:LDH-
1b . Enter
a. Dependent Variable: length of service (years)
b. All requested variables entered.
Model Summaryb
Model R R Square Adjusted R
Square
Std. Error of
the Estimate
1 .377a .142 .135 6.000
a. Predictors: (Constant), LDH-5:LDH-1
b. Dependent Variable: length of service (years)
19

ANOVAa
Model Sum of
Squares
df Mean
Square
F Sig.
1
Regression 744.562 1 744.562 20.683 .000b
Residual 4499.800 125 35.998
Total 5244.362 126
a. Dependent Variable: length of service (years)
b. Predictors: (Constant), LDH-5:LDH-1
Coefficientsa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig. Collinearity
Statistics
B Std. Error Beta Toleranc
e
VIF
1
(Constant) -1.926 2.446 -.788 .432
LDH-
5:LDH-1 16.481 3.624 .377 4.548 .000 1.000 1.000
a. Dependent Variable: length of service (years)
20

Key finding
The different statistical test are being performed to determine the whether or not there is
genuine evidence of difference in the potential health hazard between workers at the different
industries. Descriptive analysis and coefficient of correlation analysis helps to describe that there
is a major impact of type of industry a worker is working on its life. Such as the results LDH5:
LDH1 and Age describe the 2 tailed significance value for the respective case is .076 and the
standard value of alpha is around 0.05. it is also determined from the test that in case if a worker
is working in a industry like polyethelyne terephthalate, polystyrene then they have more
chances of getting ill. As these industry use harmful chemicals and solution which produces
harmful gases that directly impact and damage the liver of worker. Therefore it is determined
that there is a positive association between worker length of service in the industry and the
potential damage to their health.
21

CONCLUSION
In the end of this report, it is concluded that data analysis is systematic procedure that
mainly applied by organisation with techniques of logical and statistical to define different
examples and examine data & facts. The main requirement of this component in order to assure
about data that related with the right and authentic analysis as per the research findings. rajeev.s
22

REFERENCES
Books and Journals
Ho, R., 2013. Handbook of univariate and multivariate data analysis with IBM SPSS. Chapman
and Hall/CRC.
Demirkan, H. and Delen, D., 2013. Leveraging the capabilities of service-oriented decision
support systems: Putting analytics and big data in cloud. Decision Support Systems.
55(1). pp.412-421.
Sagiroglu, S. and Sinanc, D., 2013, May. Big data: A review. In 2013 international conference
on collaboration technologies and systems (CTS) (pp. 42-47). IEEE.
Zsambok, C. E., 2014. Naturalistic decision making: where are we now?. In Naturalistic decision
making (pp. 23-36). Psychology Press.
Raiborn, C. and Sivitanides, M., 2015. Accounting issues related to Bitcoins. Journal of
Corporate Accounting & Finance. 26(2). pp.25-34.
Ratiu, R. V., 2015. Financial reporting of European banks during the GFC: a pitch. Accounting
& Finance. 55(2). pp.345-352.
Ruch, G. W. and Taylor, G., 2015. Accounting conservatism: A review of the literature. Journal
of Accounting Literature. 34. pp.17-38.
Scholes, M. S., 2015. Taxes and business strategy. Prentice Hall.
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