hundred) companies are going to help us to save time and money to actually use it as an estimate for the entire companies (population). This is the estimate of a regression model to examine the factors that influence employee absenteeism. The data was collected from 100 randomly selected companies. The key definitions are as follows. Y = Average number of days absent per employee‚ X2 = Average employee wage‚ X3 = percentage of part time employees in a company‚ X4 = percentage of unionized employees
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6.2.5 Statistical analysis The values are expressed as mean ± SEM. The statistical analysis was carried out by one-way analysis of variance using SPSS (version 17) statistical analysis program. Duncan’s post hoc multiple comparison tests were used to determine significant differences among groups. P < 0.05 was considered to be significant. 6.3 Results 6.3.1 Liver function tests Liver toxicity markers were assayed to assess hepatic injury. The activities of alkaline phosphatase (ALP)‚ acid phosphatase
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Statistical Analysis and Application of Charts Presented To: Mam Ayesha IftikharPresented By: Hassan Bashir Roll Number: bba02141016 Program : BBA Semester : 2nd Date: 19-Oct-2014 Research Questionnaire/ Objective: Analysis of quantitative and qualitative data Uses of appropriate charts under the specific/general scenario. To ensure that statistical tools are the important for decision making. Type of Data: Quantitative Data Qualitative data Quantitative Data: Quantitative data is data expressing
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appropriate descriptive statistics to summarize the training time data for each method. What similarities or differences do you observe from the sample data? Descriptive analysis in excel has been used to come up with relevant figures of the given data samples which is tabulated below: Descriptive Statistics | Current | Proposed | Mean | 75.06557 | 75.42623 | Standard Error | 0.505094 | 0.32091 | Median | 76 | 76 | Mode | 76 | 76 | Standard Deviation | 3.944907 | 2.506385 | Sample
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write down how many CDs they owned. The student with the least number of CDs had 14 and all but one of the others owned 60 or fewer. The remaining student owned 65. The quartiles for the class were 30‚ 34 and 42 respectively. Outliers are defined to be any values outside the limits of 1.5(Q3 – Q1) below the lower quartile or above the upper quartile. On graph paper draw a box plot to represent these data‚ indicating clearly any outliers.
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Statistical Arbitrage in the U.S. Equities Market Marco Avellaneda∗† and Jeong-Hyun Lee∗ First draft: July 11‚ 2008 This version: June 15‚ 2009 Abstract We study model-driven statistical arbitrage in U.S. equities. The trading signals are generated in two ways: using Principal Component Analysis and using sector ETFs. In both cases‚ we consider the residuals‚ or idiosyncratic components of stock returns‚ and model them as mean-reverting processes. This leads naturally to “contrarian” trading
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ASSIGNMENT 1- BU1007 Question 1 (i) The following data represent the cost of electricity during July 2006 for a random sample of 50 one-bedroom apartment in a large city Electricity Charge ($) | 96 | 157 | 141 | 95 | 108 | 171 | 185 | 149 | 163 | 119 | 202 | 90 | 206 | 150 | 183 | 178 | 116 | 175 | 154 | 151 | 147 | 172 | 123 | 130 | 114 | 102 | 111 | 128 | 143 | 135 | 153 | 148 | 144 | 187 | 191 | 197 | 213 | 168 | 166 | 137 | 127 | 130 | 109 | 139 | 129 | 82 | 165
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Statistical quality control (SQC) The application of statistical techniques to measure and evaluate the quality of a product‚ service‚ or process. Two basic categories: I. Statistical process control (SPC): - the application of statistical techniques to determine whether a process is functioning as desired II. Acceptance Sampling: - the application of statistical techniques to determine whether a population of items should be accepted or rejected based on inspection of a sample of those
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Statistical Significance Eric G Peppers HCS/438 Statistical Applications October 8‚ 2012 Gerald Rintala Statistical Significance Identification of a statistic as being significant is more difficult than the novice statistician may at first understand. At the most rudimentary definition‚ a significant finding simply means the statistic is reliable. This term states how convinced you are that a relationship or difference may exist. Bennett‚ et al states‚ “we determine statistical
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Components of DSS (Decision Support System) Data Store – The DSS Database Data Extraction and Filtering End-User Query Tool End User Presentation Tools Operational Stored in Normalized Relational Database Support transactions that represent daily operations (Not Query Friendly) Differences with DSS 3 Main Differences Time Span Granularity Dimensionality Operational DSS Time span Real time Historic Current transaction Short time frame Long time frame Specific Data facts Patterns Granularity Specific
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