Statistical Analysis on Industrial Employees Data - Statistics Coursework Example

We begin the analysis by conducting techniques that gives the general insights of the data and the most common characteristics in a given dataset. Progressive more complicated and vital statistical techniques are employed and results interpreted appropriately. The most important variables and factors are considered for the analysis hence enhancing deeper understanding of the underlying variables data. This data corresponds to information of employees working in industries in different geographical locations in USA.

The following tables show the general characteristics or the descriptive statistics of the data:

Descriptive Statistics

N Minimum Maximum Mean Std. Deviation SkewnessKurtosis

Statistic Statistic Statistic Statistic Statistic Statistic Std. Error Statistic Std. Error

NumEmps51 5 45 24.02 7.495 .056 .333 .506 .656

Hours Worked 51 10400 93600 49960.78 15590.236 .056 .333 .506 .656

PerSafeBeh51 .42 1.00 .8658 .13895 -1.388 .333 1.164 .656

InjuryRate51 .000000000000 76.923076923077 15.17569605792568 17.474677348005120 2.046 .333 4.309 .656

SafetyClimate51 2.5 6.8 4.697 1.0350 .101 .333 -.697 .656

V11 0 Risk 51 1 7 4.59 2.012 -.234 .333 -1.342 .656

Valid N (listwise) 0 The above are the simple characteristics of the depicted variables. In the cases above the mean does not tend to the standard deviation and equality in the two variables is not observed. This implies that the underlying data corresponding to the variables do not follow the normal distribution. The minimum and maximum values are vital for noticing the outliers in a given dataset hence promoting accuracy. The Skewness and kurtosis values indicated above clearly shows that the distribution is symmetric which is appropriate.

Further statistical technique referred to as the t-test is conducted on the dataset. The null hypothesis in this case is that there exist no significance differences in means of the variables. The variables will be analyzed in pairs with the assumption given.

The following are the results of the set of variables:

The first pair is the number of employees and the injury rate.

Paired Samples Statistics

Mean N Std. Deviation Std. Error Mean

Pair 1 NumEmps24.02 51 7.495 1.050

InjuryRate15.17569605792568 51 17.474677348005120 2.446944266522697

Paired Samples Correlations

N Correlation Sig.

Pair 1 NumEmps & InjuryRate51 -.636 .000

Paired Samples Test

Paired Differences t dfSig. (2-tailed)

Mean Std. Deviation Std. Error Mean 95% Confidence Interval of the Difference Lower Upper Pair 1 NumEmps - InjuryRate8.843911785211576 22.983254473100660 3.218299350483053 2.379767299331983 15.308056271091170 2.748 50 .008

The P value corresponding to this pair is less than the significance level therefore we reject the null hypothesis and conclude that there exists a significant difference in means of the number employees and the injury rate.

Paired Samples Statistics

Mean N Std. Deviation Std. Error Mean

Pair 1 Hours Worked 49960.78 51 15590.236 2183.070

InjuryRate15.17569605792568 51 17.474677348005120 2.446944266522697

Pair 2 SafetyClimate4.697 51 1.0350 .1449

PerSafeBeh.8658 51 .13895 .01946

Paired Samples Correlations

N Correlation Sig.

Pair 1 Hours Worked & InjuryRate51 -.636 .000

Pair 2 SafetyClimate & PerSafeBeh51 .370 .008

Paired Samples Test

Paired Differences t dfSig. (2-tailed)

Mean Std. Deviation Std. Error Mean 95% Confidence Interval of the Difference Lower Upper Pair 1 Hours Worked - InjuryRate49945.608617667570000 15601.361084184971000 2184.627520991385000 45557.655103834970000 54333.562131500160000 22.862 50 .000

Pair 2 SafetyClimate - PerSafeBeh3.83124 .99199 .13891 3.55224 4.11024 27.581 50 .000

The above corresponding p-values are less than the significance level 0.05. Therefore we conclude that there exist differences in means of the difference pairs shown above.

References

Kapadia, A. S., Chan, W., & Moye, L. A. (2017). Mathematical statistics with applications. CRC Press.Robb, A., Fairlie, R.W & Robinson, D.T. (2013). Patterns of Financing: A Comparison Between series of reports from Kauffman Firm Survey. http://dx.doi.org/10.1039/rmh0000008