Kolmogorov-Smirnov Test PAGEREF _Toc20661623 \h 15
Measurement Scale PAGEREF _Toc20661624 \h 15
Measure of Central Tendency PAGEREF _Toc20661625 \h 15
Evaluation PAGEREF _Toc20661626 \h 15
References PAGEREF _Toc20661627 \h 16
Descriptive Data and Assumptions: Correlation
Frequency Distribution Table
PM size
Frequency
0-1
8
2-4
24
5-7
37
8-10
34
Sick Days
Frequency
0-2
1
4-7
61
8-9
30
10-12
11
365371926726500Histogram
Descriptive Statistics Table
Microns
Sick day
Mean
5.65728155
Mean
7.126214
Standard Error
0.25560014
Standard Error
0.186484
Median
6
Median
7
Mode
8
Mode
7
Standard Deviation
2.59405814
Standard Deviation
1.892605
Sample Variance
6.72913764
Sample Variance
3.581953
Kurtosis
-0.8521619
Kurtosis
0.124923
Skewness
-0.37325713
Skewness
0.14225
Range
9.8
Range
10
Minimum
0.2
Minimum
2
Maximum
10
Maximum
12
Sum
582.7
Sum
734
Count
103
Count
103
Largest(1)
10
Largest(1)
12
Smallest(1)
0.2
Smallest(1)
2
Confidence Level(95.0%)
0.50698167
Confidence Level(95.0%)
0.36989
Kolmogorov-Smirnov Test
H01: There is no statistically significant relationship between particulate matter size, and employee annual sick days.
HA1: There is a statistically significant relationship between particulate matter size, and employee annual sick days.
Measurement Scale
Ordinal
Measure of Central Tendency
Mean
Evaluation
An alpha of 0.05 is an indication that the p-values are <0.05 alpha; thus, the null hypothesis (H01) is rejected, and the (HA1) hypothesis is accepted that there is a statistically significant relationship between particulate matter size, and employee annual sick days. This correlation analysis shows that the authors are comfortable with making a Type I error (Creswell & Creswell, 2018). They indicate the p-value 1.89059E-17 (1.89059*10-17) < 0.05.
The size of the particulate matter is strongly correlated with and negatively related to the number of annual employees' sick days according to Pearson's correlation coefficient, with r = 0.715 and R2 = 51. This illustrates that the variability in employee sick days is 51%, which will be explained by the size of the particulate matter.
Descriptive Data and Assumptions: Simple Regression
Frequency Distribution Table
Expenditure
Frequency
20-500
108
501-1000
76
1001-1500
27
1501-2000
11
2001-2500
1
Time
Frequency
0-50
6
51-100
26
101-200
98
201-300
85
301-400
8
Histogram
Descriptive Statistics Table
safety training expenditure
lost time hours
Mean
595.9843812
Mean
188.0045
Standard Error
31.4770075
Standard Error
4.803089
Median
507.772
Median
190
Mode
234
Mode
190
Standard Deviation
470.0519613
Standard Deviation
71.72542
Sample Variance
220948.8463
Sample Variance
5144.536
Kurtosis
0.444080195
Kurtosis
-0.50122
Skewness
0.951331922
Skewness
-0.08198
Range
2251.404
Range
350
Minimum
20.456
Minimum
10
Maximum
2271.86
Maximum
360
Sum
132904.517
Sum
41925
Count
223
Count
223
Largest(1)
2271.86
Largest(1)
360
Smallest(1)
20.456
Smallest(1)
10
Confidence Level(95.0%)
62.03197147
Confidence Level(95.0%)
9.465484
Kolmogorov-Smirnov Test
H02: There is no statistically significant relationship between safety training programs, expenditure, and lost-time hours.
HA2: There is a statistically significant relationship between safety training programs, expenditure, and lost-time hours.
Measurement Scale
Nominal
Measure of Central Tendency
Median
Evaluation
When observing the above data, the simple regression analysis uses an alpha 0.05. It also states a p-value of 7.7E-105 (7.6586* 10-105) < 0.05. The null hypothesis (H02) is rejected, and the alternative hypothesis (HA2) is accepted; there is a statistically significant relationship between safety training programs, expenditure, and lost hours.
The correlation coefficient is r = 0.94, and a very strong negative relationship was found between safety training programs, expenditure, and a decrease in lost hours. This correlates to an R2 of 0.884, which explains the 88.4 percent of the variance between safety training programs, expenditure, and reducing lost hours.
The lost time hours equations are performed by a linear formula: Y = m + bX, which is equivalent to coefficients 1753.60 + (-6.158) (safety training programs, expenditure, and reducing lost hours).
Descriptive Data and Assumptions: Multiple Regression
Frequency Distribution Table
Decibel
Frequency
100-106
4
107-111
51
112-116
126
117-121
249
122-131
786
132-141
287
Histogram
Descriptive Statistics Table
Decibel
Mean
124.8359
Standard Error
0.177945
Median
125.721
Mode
127.315
Standard Deviation
6.898657
Sample Variance
47.59146
Kurtosis
-0.31419
Skewness
-0.41895
Range
37.607
Minimum
103.38
Maximum
140.987
Sum
187628.4
Count
1503
Kolmogorov-Smirnov Test
H03: There is no statistically significant relationship between the primary variable (frequency, angle in degrees, cord length, velocity, and displacement), and decibel level.
HA3: There is a statistically significant relationship between the primary variable (frequency, angle in degrees, cord length, velocity, and displacement), and the decibel level.
Measurement Scale
Internal
Measure of Central Tendency
Mean
Evaluation
The multiple regression analysis uses an alpha of 0.05; the results of the Frequency (Hz), Velocity (as measured meters per second), and displacement show the p-value of 4.10E-104 (4.10*10-104), 1.02E-18 (1.02*10-18), and 5.21E-45 (5.21*10-45) respectively. These have listed p-values < 0.05 alpha. The null hypothesis (H03) is rejected, and (HA3) is accepted, i.e., there is a statistically significant relationship between the primary variable (frequency, angle in degrees, cord length, velocity, and displacement), and decibel level.
The results of the multiple regression state that the angle in degrees and chord shows the p-values of 0.205 and 0.061, respectively. These p-values > 0.05. The null hypothesis (H03) is accepted, and the (HA3) is rejected, i.e., there is no statistically significant relationship between the primary variable (frequency, angle, cord, velocity, and displacement), and level of dB not increasing after the employees are placed on the site for future use.
The correlation coefficient of r = 0.31 states a positive correlation among the other groups. This equates to an R2 of 0.9 and states that 9 percent of the variability in decibel levels is explained by the above-listed groups.
Decibel level equations are performed by a linear formula:
Descriptive Data and Assumptions: Independent Samples t Test
Frequency Distribution Table
Training
Frequency
49-60
12
61-70
20
71-80
21
81-90
8
91-100
1
Training
Frequency
74-80
14
81-85
21
86-90
19
91-95
6
96-100
2
Histogram
Descriptive Statistics Table
Prior Training
Revised Training
Mean
69.79032
Mean
84.77419
Standard Error
1.402788
Standard Error
0.659479
Median
70
Median
85
Mode
80
Mode
85
Standard Deviation
11.04556
Standard Deviation
5.192742
Sample Variance
122.0045
Sample Variance
26.96457
Kurtosis
-0.77668
Kurtosis
-0.35254
Skewness
-0.0868
Skewness
0.144085
Range
41
Range
22
Minimum
50
Minimum
75
Maximum
91
Maximum
97
Sum
4327
Sum
5256
Count
62
Count
62
Largest(1)
91
Largest(1)
97
Smallest(1)
50
Smallest(1)
75
Confidence Level(95.0%)
2.805048
Confidence Level(95.0%)
1.31871
Kolmogorov-Smirnov Test
H04: There is no statistically significant difference in means scores between the prior and revised training programs.
HA4: There is a statistically significant difference in means scores between the prior and revised training programs.
Measurement Scale
Internal
Measure of Central Tendency
Mean
Evaluation
The results indicate that Group A (variable) has a lower mean. With the help of the alpha of 0.05, the p-values of t Stat is 1.94E-15 (1.93993* 10-15) < 0.05. It shows that alternative hypotheses (H04) will be rejected, while the null hypothesis (HA4) will be accepted as it assumed a statistically significant difference in mean scores between the prior and revised training programs.
The mean scores of the Group B (variable) revised training had significantly improved. The mean score of Group A [(variable) prior training] was 69.7903, and Group B (revised training) was 84.7742. Therefore, the mean difference is not zero.
Descriptive Data and Assumptions: Dependent Samples t Test
Frequency Distribution Table
Exposure
Frequency
5-15
5
16-25
8
26-35
12
36-45
16
46-56
8
Exposure
Frequency
5-15
5
16-25
8
26-35
11
36-45
17
46-56
8
Histogram
Descriptive Statistics Table
Pre-Exposure μg/dL
Post-Exposure μg/dL
Mean
32.8571429
Mean
33.28571
Standard Error
1.75230655
Standard Error
1.781423
Median
35
Median
36
Mode
36
Mode
38
Standard Deviation
12.2661458
Standard Deviation
12.46996
Sample Variance
150.458333
Sample Variance
155.5
Kurtosis
-0.57603713
Kurtosis
-0.65421
Skewness
-0.42510965
Skewness
-0.48363
Range
50
Range
50
Minimum
6
Minimum
6
Maximum
56
Maximum
56
Sum
1610
Sum
1631
Count
49
Count
49
Largest(1)
56
Largest(1)
56
Smallest(1)
6
Smallest(1)
6
Confidence Level(95.0%)
3.52324845
Confidence Level(95.0%)
3.581792
Kolmogorov-Smirnov Test
H05: There is no statistically significant relationship differences between the blood lead level of the employees and pre and post-exposure to an unhealthy workplace condition.
HA5: There is a statistically significant relationship differences between the blood lead level of the employees and pre and post-exposure to an unhealthy workplace condition.
Measurement Scale
Interval
Measure of Central Tendency
Mean
Evaluation
By using an alpha of 0.05, the p-value of the t Stat is 0.0596>0.05 of alpha. The alternative hypothesis (H05) will be highly accepted and no statistically significant difference, while the null hypothesis (HA5) is rejected. In this case, it means that the pre-exposure and post-exposure were the same. As long as the instances regarding lead blood levels are contained and results proven via post-exposure results, it is clear that the mean has increased slightly (pre-exposure = 32.8571, while post-exposure =33.2857).
Descriptive Data and Assumptions: ANOVA
Frequency Distribution Table
Air
Frequency
1-3
1
4-6
4
7-9
6
10-12
7
12-15
2
Soil
Frequency
5-7
3
8-10
13
10-13
4
Water
Frequency
1-3
1
4-6
10
7-9
5
10-12
4
Training
Frequency
1-3
1
4-6
16
7-9
3
Histogram
Descriptive Statistics Table
A = Air
B = Soil
Mean
8.9
Mean
9.1
Standard Error
0.684028
Standard Error
0.390007
Median
9
Median
9
Mode
11
Mode
8
Standard Deviation
3.059068
Standard Deviation
1.744163
Sample Variance
9.357895
Sample Variance
3.042105
Kurtosis
-0.6283
Kurtosis
0.11923
Skewness
-0.36085
Skewness
0.492002
Range
11
Range
7
Minimum
3
Minimum
6
Maximum
14
Maximum
13
Sum
178
Sum
182
Count
20
Count
20
Largest(1)
14
Largest(1)
13
Smallest(1)
3
Smallest(1)
6
Confidence Level(95.0%)
1.431688
Confidence Level(95.0%)
0.816294
C = Water
D = Training
Mean
7
Mean
5.4
Standard Error
0.575829
Standard Error
0.265568
Median
6
Median
5
Mode
6
Mode
5
Standard Deviation
2.575185
Standard Deviation
1.187656
Sample Variance
6.631579
Sample Variance
1.410526
Kurtosis
-0.23752
Kurtosis
0.253747
Skewness
0.760206
Skewness
0.159183
Range
9
Range
5
Minimum
3
Minimum
3
Maximum
12
Maximum
8
Sum
140
Sum
108
Count
20
Count
20
Largest(1)
12
Largest(1)
8
Smallest(1)
3
Smallest(1)
3
Confidence Level(95.0%)
1.205224
Confidence Level(95.0%)
0.55584
Kolmogorov-Smirnov Test
H06: There are no statistically significant differences relationship between return on investment and air, soil, water, and safety training.
HA6: There is a statistically significant difference in return on investment between air, soil, water, and safety training.
Measurement Scale
Ratio
Measure of Central Tendency
Mean
Evaluation
According to the results demonstrated above, while using an alpha of 0.05, the p-value of the ANOVA analysis is 1.76E-06 < 0.05. Evidently, F = 11.9232, while F crit = 2.724944. Therefore, F > F crit. This outcome debunks the null hypothesis. (H06) is rejected, and (HA6) is accepted. There are significant differences, and this confirms that they are not equal regarding the return of their investments among the four groups.
References
BIBLIOGRAPHY Assaad, H. I. (2014). Rapid publication-ready MS-Word tables for one-way ANOVA. SpringerPlus.
Creswell, J. W. (2018). Research design: Qualitative, quantitative, and mixed methods approaches. Thousand Oaks, CA: Sage.
