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Data interpretation practicum

Your Data Interpretation Practicum

It is important for the writer to note that this paper is a combination of order #113631, 113672,
113741, 113768. The writer will have to combine the solutions that where giving in the above orders
and will also include what is added here below in a well-structured APA paper including all what is
mention here below. The previous analysis mentioned here below is contain in the solutions of the
above mention order.

In order to widely disseminate or publish your research findings, they must be presented in a manner
that facilitates comprehension by the educated reader. This week, you will present the findings from
the analyses you conducted on your chosen data from Week 3.

Your submission should include your previous analyses and interpretation, along with appropriate
sections to address the limitations of your work and opportunities for further inquiry. Follow APA
format to present your findings as a paper, including a title page, abstract, introduction, sections in the
main body as needed, conclusions, references, and appendices as needed
I will email the dataset that has to be use for this paper as it has also been use for the past 7 weeks
for previouse assignment.


Data interpretation practicum

This paper seeks to compile all the analysis that was performed to determine the safety of
people at different working stations. In particular, the paper will bring together all the
analytical techniques employed on the data and make the inference about the population
parameters. The fundamental of this paper thus is to answer the question whether there exists
a difference in injury rate in a different working site, when different genders supervise
employees, when workers have worked on a different number of hours, and when a site has a
different number of workers (O’Leary, 2013). The analysis will give an insight of the safety
of workers, which is vital for firms like the insurance company, and also for policy planning
of the company.
The research formatting will utilize the APA writing style, and the analysis will be performed
using SPSS for Windows. Nevertheless, the research will seek to infer about the following
H 0 : There is no significance difference in injury rate at a working site and supervisor’s
gender, number of employees and the number of hours at work.
H 1a : There is a significance difference in injury rate at a working site and supervisor’s
gender, number of employees and the number of hours at work.
It is important to notice that the research hypothesis and research question are intertwined
(Creswell, 2013).
Data distribution was evaluated using descriptive measures, which are tabulated in Table 1.


Table 1:
Descriptive Statistics

number of

number of
hours at


injury rate safety
N Valid 51 51 51 51 51
Missing 0 0 0 0 0
Mean 24.02 49960.78 .47 15.1755 4.6971
Std. Error of Mean 1.050 2183.070 .071 2.44692 .14493
Median 23.00 47840.00 .00 9.1600 4.7600
Std. Deviation 7.495 15590.236 .504 17.47447 1.03497
Variance 56.180 243055455.3

73 .254 305.357 1.071
Skewness .056 .056 .121 2.046 .101
Std. Error of
Skewness .333 .333 .333 .333 .333
Kurtosis .506 .506 -2.068 4.309 -.697
Std. Error of Kurtosis .656 .656 .656 .656 .656
Minimum 5 10400 0 .00 2.50
Maximum 45 93600 1 76.92 6.80
The descriptive statistics, results show that on average there are 24 workers at each site with a
minimum of five workers and a maximum of 45. Notably, the skewedness of the number of
workers is close to zero, thus, the normal plot of this parameter will be almost asymmetrical.
On average the workers work for 49960.78 hours, with a minimum of 10400 hours and a
maximum of 93600 hours. Similarly, the distribution of the number of working hours in
almost asymmetric, which is deduced from a low skewness coefficient (Ho, & Carol, 2015).
On average, the injury rate of all the working site is 15.1755, which the minimum of zero
injury rate and a maximum of 76.92. Further, the injury rate has a positive skewedness, which
means that its standard normal curve will have a long tail to the left (on the higher values of
the injury rate) (Ho, & Carol, 2015). Furthermore, these four working sites have on average
4.6971 safety climate, with the safest site having 2.50 safety climate and not safest site have a
6.8 safety climate.

To compare the sample mean, an ANOVA technique was applied, and the results were as

Table 2:

Sum of

df Mean

F Sig.

safety climate

Between Groups 34.210 33 1.037 .911 .605
Within Groups 19.348 17 1.138
Total 53.558 50

supervisors gender

Between Groups 7.973 33 .242 .868 .648
Within Groups 4.733 17 .278
Total 12.706 50

number of hours at work

Between Groups 9791279435.

294 33 296705437.4

33 2.136 .050

Within Groups 2361493333.

333 17 138911372.5

Total 12152772768
.627 50

number of employees

Between Groups 2263.147 33 68.580 2.136 .050
Within Groups 545.833 17 32.108
Total 2808.980 50


Between Groups 18.655 33 .565 .724 .792
Within Groups 13.267 17 .780
Total 31.922 50

In this case, the injury rate was used as a factor. The decision rule is to reject the null
hypothesis when the P-value < level of significance. The p-values show that we will fail to
reject the null hypothesis, following the critical rule. Thus, we infer that there is no
significance difference in injury rate at a working site and supervisor’s gender, a number of
employees and the number of hours at work (Murphy, et al. 2014).
A paired t-test was also performed in an attempt to evaluate the difference in the variables
mean. The results were as illustrated in Table 3. Notably, the assumption (null hypothesis) is
that the mean of the paired variable is equal, versus, the alternative that the mean of paired
variables is not equal (Murphy, et al. 2014).

Table 3:
Paired Samples Test

Paired Differences t df Sig. (2-tailed)

Mean Std.

Std. Error

95% Confidence Interval of
the Difference
Lower Upper

Pair 1
injury rate –
number of

8.84412 22.98329 3.21830 -15.30827 -2.37996 -2.748 50 .008

Pair 2
injury rate –

14.70490 17.52681 2.45424 9.77541 19.63440 5.992 50 .000

Pair 3 injury rate –
safety climate 10.47843 17.51818 2.45304 5.55136 15.40550 4.272 50 .000

Pair 4
injury rate –
number of hours
at work

15601.36168 2184.62760 -54333.56251 -45557.65514 -22.862 50 .000

In this case, the rejection rule is: reject null hypothesis if |t calculated | > t tabulated = 1.684. In that
light, all the t calculated values are greater than 1.684, and thus, conclusively we say that the paired
variables means are not equal.
To find a linear model that can be used to predict injury rate using safety climate, number of
hours at work, supervisors’ gender as the predictors in the model
Table 4:
Coefficients a
Model Unstandardized


t Sig.

B Std. Error Beta

(Constant) 42.248 10.767 3.924 .000
number of
hours at work -.001 .000 -.678 -5.774 .000
gender 3.564 4.194 .103 .850 .400
safety climate 1.958 2.007 .116 .976 .334
a. Dependent Variable: injury rate
The model is:
Injury rate = 42.248 – 0.001* (number of hours at work) + 3.564(supervisors gender) + 1.958(safety climate)
The regression model summary is as given in 5.

Table 5:
Model Summary
Model R R Square Adjusted R

Std. Error of
the Estimate
1 .649 a .421 .384 13.71976
a. Predictors: (Constant), safety climate, number of
hours at work, supervisors gender

The coefficient of determination shows that the fitted model can explain 38.4% of the
variation (Lowry, 2014).


number of

r of
at work
Site Pearson Correlation 1        

Sig. (2-tailed)
N 51

number of

Pearson Correlation .130 1
Sig. (2-tailed) .363
N 51 51
injury rate Pearson Correlation -.074 -.636 ** 1
Sig. (2-tailed) .606 .000
N 51 51 51


Pearson Correlation .331 * .147 -.013 1

Sig. (2-tailed) .018 .303 .930
N 51 51 51 51

number of
hours at

Pearson Correlation .130 1.000 ** -.636 ** .147 1

Sig. (2-tailed) .363 0.000 .000 .303
N 51 51 51 51 51

*. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).


Notably, there exists a perfect correlation between the number of hours at work and the
number of employees, this implies that is the number of workers in a site changes that will be
the same proportional change in the number of working hours (Wilcox, 2012). Also, there is a
significant negative correlation between injury rate and a number of working hours, and the
number of workers in a site at the 95% level of significance. This means that when the
number of working hours or the number of employees increases, there will be a decline in
injury rate (Wilcox, 2012).
The paper has compiled all the analysis performed previously, discussion of the results were
given and inference made. Thus, the primary objective was achieved, on the other hand, the
results indicated that there was no adequate evidence to reject the null hypothesis. For this
reason, a conclusion was made that there is no significance difference in injury rate at a
working site and supervisor’s gender, number of employees and the number of hours at work.



Creswell, J. W. (2013). Research design: Qualitative, quantitative, and mixed methods
approach. Sage publications.
Ho, A. D., & Carol, C. Y. (2015). Descriptive Statistics for Modern Test Score Distributions
Skewness, Kurtosis, Discreteness, and Ceiling Effects. Educational and
Psychological Measurement, 75(3), 365-388.
Lowry, R. (2014). Concepts and applications of inferential statistics.
Murphy, K. R., Myors, B., & Wolach, A. (2014). Statistical power analysis: A simple and
general model for traditional and modern hypothesis tests. Routledge.
O’Leary, Z. (2013). The essential guide to doing your research project. Sage.
Wilcox, R. R. (2012). Introduction to robust estimation and hypothesis testing. Academic

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