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data analysis
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Executive Summary Students Performance has been the focus of much recent investigation and discussion. So the principal objective of this study is to isolate factors associated with the student performance. So we identified Gender, Time Spent on Leisure activities, Qualification of Parents, Previous Performance records, Class size, Attendance, Family Income & Infrastructure of College. The results reflect clearly the importance of analyzing all these factors simultaneously in order to understand the variation in student performance. To analyse these factors we gathered primary data by distributing questionnaire and then applying various tests like F- test, T-test, Chi-square test to evaluate the statistically significance of the independent variables we choose and the regression than the whole. We found that all the factors stated above are statistically significant. Descriptive Statistics AM Gen AT (hrs) QF QM FI PR1 CS ATD OI Mean 61.25 0.211538 4.788462 17.19231 16.19231 6.961538 65.59615 32.48077 68.38462 3.903846 Standard Error 1.184641 0.057187 0.205664 0.351723 0.313158 0.134427 1.303279 1.844336 2.206728 0.110735 Median 60 0 4.75 17 15 7 65.5 39 65 4 Mode 60 0 4.5 17 15 7 70 44 65 4 Standard Deviation 8.542569 0.412384 1.483062 2.536312 2.258218 0.969365 9.39808 13.2997 15.91294 0.798519 Sample Variance 72.97549 0.17006 2.199472 6.432881 5.099548 0.939668 88.32391 176.882 253.2217 0.637632 Kurtosis -0.43617 0.120005 -0.67515 -0.09104 2.669976 1.014704 -0.86396 -1.7048 -0.59745 3.582314 Skew ness 0.500642 1.454956 -0.06608 1.125301 1.982545 0.079084 0.077331 -0.52392 0.4126 -1.50483 Range 32 1 5.5 7 7 5 35 31 60 4 Minimum 48 0 2 15 15 5 50 13 40 1 Maximum 80 1 7.5 22 22 10 85 44 100 5 Sum 3185 11 249 894 842 362 3411 1689 3556 203 Count 52 52 52 52 52 52 52 52 52 52 Largest (1) 80 1 7.5 22 22 10 85 44 100 5 Smallest (1) 48 0 2 15 15 5 50 13 40 1 Confidence Level (95.0%) 2.378265 0.114808 0.412887 0.706113 0.628691 0.269873 2.61644 3.702657 4.430188 0.222309 The concept of reengineering has been around for nearly two decades and was implemented in a piecemeal fashion in organizations. Production organizations have been in the vanguard without knowing it. They have undertaken reengineering by implementing concurrent engineering, lean production, cellular manufacturing, group technology, and pull-type production systems. These represent fundamental rethinking of the manufacturing process. Manufacturers generally made significant improvements in their internal operations during the 1980s. But excellence in manufacturing has not always translated to superior sustainable results in the marketplace. More recently, the focus appears to have shifted out of the manufacturing process to other interfunctional and interorganizational and customer-based processes. The 1992 Manufacturing Futures Survey reports similar views held by manufacturing executives. Rapid advances in information technology and its applications have been a major enabler of business process reengineering in services. Rule 1. Organize around outcomes, not tasks Several specialized tasks previously performed by different people should be combined into a single job. This could be performed by an individual "case worker" or by a "case team." The new job created should involve all the steps in a process that creates a well-defined outcome. Organizing around outcomes eliminates the need for hand-offs, resulting in greater speed, productivity, and customer responsiveness. It also provides a single knowledgeable point of contact for the customer. GTE's "front-end technician" position described in the sidebar on the following page illustrates this principle. Rule 2. Have those who use the output of the process perform the process In other words, work should be carried out where it is makes the most sense to do it. This results in people closest to the process actually performing the work, which shifts work across traditional intra- and interorganizational boundaries.
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