Data Analysis‚ Interpretation‚ and Conclusion‚ Part IV Domingo Melchor‚ Ed Mendoza‚ Allen LaHeist‚ Noel Alba University of Phoenix MU11MBA07 QNT/561 Dr. Jyotirmay Deb‚ Ph.D. Workshop/Week 4 July 21‚ 2012 Abstract Part IV of the business research proposal paper is the analysis and interpretation of the data collected in Part III. This installment of the paper illustrates the hypothesis testing technique of analyzing data. It answers the questions raised in Part I about low attendance
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between hv and lv in data set. | | | | Variance | population (δ2) | =∑(x-x)2N | x – classmarkx –meanN - population | Average squared deviation from the mean. | | | | | sample(s2) | =∑(x-x)2n-1 | x – classmarkx –meann - sample | | | | | Standard Deviation | population (δ) | =∑(x-x)2N | | is square root of the average deviation from the mean‚ or simply the square root of the variance. | Square root lang yung result ng variance | | | | sample(s) | =∑(x-x)2n-1 | |
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AB1202 – Statistics and Quantitative Methods Nanyang Business School AB1202 – STATISTICS AND QUANTITATIVE METHODS Academic Year : 2014– 2015 Course Coordinator : Chin Chee Kai Pre-requisites : - No. of AUs Semester : S1 : 3 Course Description and Scope In almost every form of business‚ we routinely deal with uncertain business outcomes‚ risky decisions‚ unknowable parameters‚ imprecise opinions and measurements‚ and most importantly‚ the elusive element called
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µmedium ≠ µhigh Analysis 1. All Sales | Inside | | Outside | | | | | | | | Mean | 64925 | Mean | 59289 | Mean | 70562 | Standard Error | 989 | Standard Error | 1115 | Standard Error | 1276 | Median | 61490 | Median | 56407 | Median | 69000 | Mode | 53464 | Mode | 53464 | Mode | #N/A | Standard Deviation | 10839 | Standard Deviation | 8639 | Standard Deviation | 9884 | Sample Variance | 117476832 | Sample Variance | 74634297 | Sample Variance | 97692326 | Kurtosis
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the word Statistics? [ 5 marks] 2. a. In a bivariate data on ‘x’ and ‘y’‚ variance of ‘x’ = 49‚ variance of ‘y’ = 9 and covariance (x‚y) = -17.5. Find coefficient of correlation between ‘x’ and ‘y’. [ 5 marks] b. Enumerate the factors which should be kept in mind for proper planning. [ 5 marks] 3. The percentage sugar content of Tobacco in two samples was represented in table 11.11. Test whether their population variances are same. [ 10 marks] Table 1. Percentage sugar content of Tobacco in two
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time-series analysis‚ testing for the presence of autocorrelation Likelihood-ratio tests in general statistical modelling‚ for testing whether there is evidence of the need to move from a simple model to a more complicated one (where the simple model is nested within the complicated one). One case where the distribution of the test statistic is an exact chi-squared distribution is the test that the variance of a normally distributed population has a given value based on a sample variance. Such
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CONFIDENTIAL CS/JAN 2012/QMT500 UNIVERSITI TEKNOLOGI MARA FINAL EXAMINATION COURSE COURSE CODE EXAMINATION TIME STATISTICS FOR ENGINEERING QMT500 JANUARY 2012 3 HOURS INSTRUCTIONS TO CANDIDATES 1. This question paper consists of five (5) questions. 2. Answer ALL questions in the Answer Booklet. Start each answer on a new page. Do not bring any material into the examination room unless permission is given by the invigilator. Please check to make sure that this examination pack
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Module 2 2-2 Case Study: Helping the Moores Moore Housing Contractors QSO600: Operations Management Irfan Sheikh Moore Housing Contractors CPM/PERT network for Moore House Contractors Activity Activity Activity Duration Variance ES LS EF LF Slack a 4.167 .25 0 0.000 4.167 4.167 C r i t i c a l b 3.167 .25 4.167 4.167 7.333 7.333 C r i t i c a l c 3.833 .25 7.333 7.833 11.167 11.667 .500 d 2.167 .25 7.333 33.833 36 36 26.5 e 2 .111 7.333 7.333 9.333 9.333 C r i t i c a l Dumck
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QUESTION 1 The managing partner for Westwood One Investment Managers Inc. gave a public seminar in which she discussed a number of issues‚ including investment risk analysis. In that seminar‚ she reminded people that the coefficient of variation can often be used as a measure of risk of an investment. To demonstrate her point of view‚ she used two hypothetical stocks as examples. She let x equal the change in assets for a $1‚000.00 investment in stock1 and y reflect the change in assets for a
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figure for food was £50 which leads to the variance which was £100 and this is favourable as B.A are saving money and the cost is lower than expected figure which is favourable. On the other hand‚ Adverse or unfavourable variances in B.A are when the actual figures are higher than the budget. One problem in the budget for B.A is fuel shown above in the table‚ as the produced budget for this was £850 but the actual figure was £1000‚ leading to the variance of £150. This was adverse for B.A as the actual
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