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Data Interpretation Practicum

run either correlation, regression, or discriminant analysis on your chosen data. This Application
requires you to engage in data interpretation and to select the appropriate analyses for your
hypotheses and for the data that you have at your disposal. Toward that end, you should consider
which analyses will inform the reader and allow you to pursue your questions.

  1. Your submission to your Instructor should include your SPSS output file of your selected statistical
    analysis in a Word document, along with each of the following elements:
  2. Your SPSS output, including graphical representations;
  3. Your narrative interpretation; the governing assumptions of the analyses you ran;
  4. The viable and nonviable hypotheses (null and alternative);
  5. And finally the relevant values (such as a P value indicating statistical significance or a lack

Be sure to indicate to your Instructor why you selected the analyses that you did. In other words, why
did you select to engage in discriminant analysis, regression, or correlation? How is this analysis
related to the hypothesis?


Data Interpretation Practicum

The primary objective of this research study is to analyze the provided data to draw inference
about the safety of people at different working sites. Thus, the analysis will revolve around
finding evidence ion whether there exist any causation relationship (correlation) between the
rates of injuries in a working site, the gender of a supervisor at the site, the number of
employees at the three different sites and the hours the employees are working. This study is
significance for it can be applied in the practice of human management to assess the risk
factor of employees at different fields
Thus, the fundamental of this analysis will be to find or establish whether causation of these
variables exist and if it exists, to what extent. In light to this, a bivariate correlation analysis
will be carried out and inference/conclusion made about the relationship of this variables. in
particular, the analysis will be done to find out whether individual supervisor’s genders

contributes to the high injury rate in a site, increase the number of employees increases the
injury rate and also if the increased number of working hours is positively correlated to the
injury rate. The analysis of this study is based on the hypothesis that are:
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 1 : 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.
This hypothesis was vital in the formulation of the research question, which can be regarded
as the backbone any successful research (Wilcox, 2012). The research inference about the
population parameter will be performed at α =0.05 significant level. At the end of the data
analysis, a conclusion will be made which sums up all the inferences made.
To investigate the data distribution, a descriptive statistics analysis was carried out and the
results are as illustrated in Table 1.
Table 1:
Descriptive Statistics

Number of

Site Number of
hours at work

Injury rate Supervisors


Valid 51 51 51 51 51
Missing 0 0 0 0 0
Mean 24.0196 2.04 49960.7843 15.1755 .47
Std. Deviation 7.49531 .799 15590.23590 17.47447 .504
Variance 56.180 .638 243055455.373 305.357 .254

Skewness .056 -.072 .056 2.046 .121
Std. Error of

.333 .333 .333 .333 .333

Kurtosis .506 -1.419 .506 4.309 -2.068
Std. Error of Kurtosis .656 .656 .656 .656 .656

The summary table in Figure 1 shows that all the variables had a positive skewness except
the site. This simply means that they are asymmetric and have a long tail to the right (Ho, &
Carol, 2015). On the nature of the curve relative to the standardized normal curve, the
number of employees, the number of hours at work, and injury rates have a positive kurtosis
that indicates that these variables have a more picked plot relative to the normal curve
(Wilcox, 2012)..

To find a model that can be used to determine injury rate at different working sites, a
regression analysis was performed and the results are as summarized in Table 2 and Table 3.

Table 2:
Model Sum of

Df Mean

F Sig.

Regression 6244.698 3 2081.566 10.843 .000 b
Residual 9023.158 47 191.982
Total 15267.856 50
a. Dependent Variable: injury rate

b. Predictors: (Constant), number of hours at work, site, supervisors
The p-value in this case is less than the set level of significance α = 0.05. Thus, this is a clear
indication that there exist a significance evidence to reject the null hypothesis.

Table 3:
Coefficients a
Model Unstandardized


t Sig.

B Std. Error Beta

(Constant) 50.082 7.842 6.387 .000
Site .308 2.481 .014 .124 .902
supervisors gender 2.259 4.013 .065 .563 .576
number of hours at work -.001 .000 -.654 -5.604 .000
a. Dependent Variable: injury rate
A fitted regression model that can be used to estimate the risk rate at different working site,
when different supervisors gender is in charge and different working hours is as given below;
Injury rate = 50.082 + 0.308* (Site) + 2.259* (Supervisors gender) – 0.001*(Number of
hours at work)
Table 4:
Model Summary
Model R R Square Adjusted R

Std. Error of
the Estimate
1 .640 a .409 .371 13.85576

a. Predictors: (Constant), number of hours at work,
site, supervisors gender
From the model summary table, the coefficient of determinant r 2 shows that only 37.1% of
the variation can be explained by the fitted regression model. Nevertheless, a larger portion of
62.1% remain unexplained, hence this model is not best determining or estimating the injury
Table 5:
Paired Samples Correlations

N Correlation Sig.
Pair 1 Injury rate & number of employees 51 -.636 .000
Pair 2 Injury rate & number of hours at work 51 -.636 .000
Pair 3 Injury rate & supervisors gender 51 -.090 .532
Pair 4 Injury rate & site 51 -.074 .606
This result indicates that there exist a strong negative correlation between injury rate & a
number of hours at work, and injury rate & number of employees, which is statistically
significance since p-value < 0.001. Nevertheless, Injury rate & supervisor’s gender and Injury
rate & site have a weak negative correlation which is not statistically significant since p-value
> α = 0.05.
The analysis illustrates clearly that the objectives set at the beginning of the paper have been
achieved and the hypothesis tested and inference made about the sample population. There
results showed that there existed statistically significance difference between the given
variables and this lead to rejection of null hypothesis. Using this token, a conclusion was
made that 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.



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.
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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