Safety-Critical Systems: The Convergence of High Tech and Human Factors - Essay Example

Mintab Assignment Introduction It is recognized that work-related stress can prompt expanded affliction nonappearance, higher work turnover does their work and early retirement. Indeed, somewhere around 2007 and 2008, an expected 13.5 million working days were lost to push related nonattendance. The Management Standards were produced by the UK Health and Safety Executive to help decrease the levels of work-related anxiety retentive working practices to keep away from the convict and to manage ported by British specialists (Redmill, 1996). This principles based methodology highlights six key territories of work outline that, if not HSE makes utilization of money matters in assessing the costs and profits of its mediation and in understanding the most extensive financial connection for well-being and security. The primary point of Health and Safety Executive (HS) is to break down the respects between laborers in the four separate segments furthermore to complete the strength of professionals in relationship between them. The Health and Safety Executive likewise asked to do an analysis in relationship between the length of administration and recorded well-being issues, for example, silicosis. Tile, porcelain and precious stone glass commercial enterprise. This study however, will incur and make use of variance test both H0 and H1. The samples associated with variance H1 will have different variances while H0 will have no difference in the variances between the samples. From this perception we realize that if one rejects the alternative if the p value is greater than 0.05, he or she will be forced to reject the null if the p value is less than 0.05 Hypothesis The main objective of this study is to analyse the deference’s between workers in the four different sectors and also to carry out the health of workers in relationship between them as far as Health and safety Executive (HS) is of concern. However, this study constitutes two hypotheses. The first hypothesis is H0 where there is no links in the recorded health of the workers and there is no variance in the health of the workers. The second hypothesis is H1 where there is a link in the recorded health of the workers and there is a variance in the health of the workers. The study will analyse and give justification on both hypotheses to oversee the null perception as far as p value is taken into consideration. Justification In the light of the HSEs concentrate on empowering associations successfully to handle work-related stress using Variance approach, one would anticipate that the relationship will exist between the change (as measured by the Indicator Tool) and real markers of work-related anxiety. It is maybe astounding that there has been a stand out study to date that has researched the relationship between the fluctuations and anxiety related work results. Utilizing a pilot adaptation of the Indicator Tool, Main discovered a frail relationship between the changes focused around the employment fulfillment, disorder nonappearance and occupation execution among word related wellbeing and human asset workers. Graphical representation The box plot ℅ damaged cells Figure 1: According to the above figure 1, the box plot is ideal when one is doing an analysis of difference and compares more than one data set. The crystal glass sector workers had highest cell damage. In combination with other sector workers, the other sectors workers had also cell damage but in combination with crystal glass worker not a lot. Descriptive Statistics: % damaged cells Variable sector N N* Mean SE Mean StDev Minimum Q1 % damaged cells brick 38 0 1.572 0.185 1.142 0.200 0.536 Crystal glass 30 0 2.108 0.186 1.017 0.300 1.439 Porcelain 32 0 1.814 0.150 0.847 0.489 1.095 Tile 27 0 1.337 0.205 1.065 0.200 0.600 Variable Sector Median Q3 Maximum % damaged cells brick 1.370 2.375 4.300 Crystal glass 2.000 2.650 4.700 porcelain 1.855 2.272 3.676 tile 1.100 1.500 4.500 The normality test Test Analysis The analysis is of more importance keeping in mind that we have to see that our result are normally distributed or not normally distributed. As we said before about the result when our (H0) can be accepted because the p value obtained is greater than 0.05, our p value damaged cells brick is 0.012, therefore we can accept (H0/H1). That means our result is normally distributed. As from the first graph, if our (H0) is greater than 0.05 than we can accept our p value. But our p value in this graph is greater than 0.005 it means our p value is accepted (H0/H1 accepted?). According to our scholarly perception, when our result are both normally distributed then we can do a 2- sample t- test as well as doing the variance and normality tests help one to decide to do a 2 sample t test. We went through all these tests like doing an analysis of difference and how many samples and if it was two than we can do observations independent within samples. If it was yes than we can do observations independent between samples. Than we see again the variance equal is yes or no. if it is yes than we can do both normally distributed test, if it is no than we can do a Mann- Whitney test. If both normally distributed than we can two t sample test. These are the methods that I explain why we are doing normality tests to help me to do a 2 sample t test. One-way ANOVA: % damaged cells versus sector Source DF SS MS F P sector 3 9.59 3.20 3.02 0.032 Error 123 129.98 1.06 Total 126 139.57 S = 1.028 R-Sq = 6.87% R-Sq(adj) = 4.60% Individual 95% CIs For Mean Based on pooled StDev Level N Mean StDev ------+---------+---------+---------+--- Brick 38 1.572 1.142 (-------*--------) Crystal glass 30 2.108 1.017 (---------*--------) Porcelain 32 1.814 0.847 (--------*--------) Tile 27 1.337 1.065 (--------*---------) ------+-----------------+---------+---------+--- 1.20 1.60 2.00 2.40 Pooled StDev = 1.028 Grouping Information Using Tukey Method Sector N Mean Grouping Crystal glass 30 2.108 A Porcelain 32 1.814 A B Brick 38 1.572 A B Tile 27 1.337 B Means that do not share a letter are significantly different. Tukey 95% Simultaneous Confidence Intervals All Pairwise Comparisons among Levels of sector Individual confidence level = 98.96% Sector = brick subtracted from: Sector Lower Center Upper ---------+---------+---------+---------+ Crystal glass -0.117 0.536 1.190 (-------*-------) Porcelain -0.399 0.242 0.884 (-------*-------) Tile -0.908 -0.235 0.438 (-------*-------) ---------+---------+---------+---------+ -0.80 0.00 0.80 1.60 Sector = crystal glass subtracted from: Sector Lower Center Upper ---------+---------+---------+---------+ Porcelain -0.974 -0.294 0.386 (-------*--------) Tile -1.481 -0.771 -0.062 (--------*--------) ---------+---------+---------+---------+ -0.80 0.00 0.80 1.60 Sector = porcelain subtracted from: Sector Lower Center Upper ---------+---------+---------+---------+ Tile -1.176 -0.477 0.222 (--------*--------) ---------+---------+---------+---------+ -0.80 0.00 0.80 1.60 Question 2 Scatter plot Figure 4; scatter plot relation shape between cell damage and length of the service. sectors p- values Pearson correlation and coefficient porcelain 0.004 0.492 brick 0.010 0.413 tile 0.000 0.664 Crystal glass 0.000 0.624 There is an average correlation between length of service and cell damage the Pearson test has reviled that for all sectors. Cell damage represented that all the P- values are below 0.05 for all sectors this means there is a connection between the lengths of service. The P- Value for the tile sector is 0.000, P- value for the porcelain is 0.004, P- value for the crystal glass is 0.000 and the P- value for brick sector is 0.010. In order to choose which hypothesis should be rejected and which should be accepted there is proof of correlation a regression analysis needs to be done. Regression Analysis: % damaged cells versus length of service (years) The regression equation is % damaged cells = 1.04 + 0.0761 length of service (years) Predictor Coef SE Coef T P Constant 1.0406 0.1423 7.31 0.000 length of service (years) 0.07607 0.01312 5.80 0.000 S = 0.938112 R-Sq = 21.2% R-Sq(adj) = 20.6% Analysis of Variance Source DF SS MS F P Regression 1 29.567 29.567 33.60 0.000 Residual Error 125 110.007 0.880 Total 126 139.574 Unusual Observations Length of Service % damaged Obs (years) cells Fit SE Fit Residual St Resid 9 24.0 4.3000 2.8662 0.2162 1.4338 1.57 X 13 19.0 0.2000 2.4858 0.1577 -2.2858 -2.47R 19 23.3 2.5993 2.8146 0.2080 -0.2153 -0.24 X 22 5.0 3.5557 1.4209 0.0970 2.1348 2.29R 37 34.0 3.7000 3.6268 0.3410 0.0732 0.08 X 44 24.0 1.5000 2.8662 0.2162 -1.3662 -1.50 X 62 27.2 4.5000 3.1117 0.2558 1.3883 1.54 X 77 24.0 2.2518 2.8662 0.2162 -0.6144 -0.67 X 85 25.0 2.7000 2.9422 0.2283 -0.2422 -0.27 X 122 16.4 4.7000 2.2880 0.1299 2.4120 2.60R 124 13.0 4.1000 2.0304 0.0999 2.0696 2.22R R denotes an observation with a large standardized residual. X denotes an observation whose X value gives it large leverage. Conclusion All in all, the main objective of this study was to analyse the deference’s between workers in the four different sectors and also to carry out the health of workers in relationship between them as far as Health and safety Executive (HS) is of concern. It is recognized that work-related stress can prompt expanded affliction nonappearance, higher work turnover does their work and early retirement. HSE makes utilization of money matters in evaluating the expenses and benefits of its intercession and in understanding the most far reaching money related association for prosperity and security. The essential purpose of Health and Safety Executive (HS) is to break down the regards between workers in the four different portions to finish the quality of experts in relationship between them. The Health and Safety Executive similarly asked to do a dissection in relationship between the length of the organization and recorded success issues, for instance, silicosis. Tile, porcelain and valuable stone glass business endeavor. The test above indicates that the initiated hypothesis is normally distributed. Several tests have been done such as normality test, and we have plot graph such as Pearson correlation. The test that we have done was very useful. We saw that sector glass worker was highly damage in combination with other sectors. Bibliography Redmill, F. (1996). Safety-Critical Systems: The Convergence of High Tech and Human Factors: Proceedings of the Fourth Safety-critical Systems Symposium Leeds, UK 6-8 February 1996 [Paperback]. New York: Springer; Softcover reprint of the original 1 Read More