are well defined but some of the possible factors can cause to the deviations and variances. Those possible factors can be eradicated through extra efforts into the process. However the small chances of variance will remain the same because the real business scenarios may vary sometimes than the forecasted one. This report is an attempt to investigate the operational standards and the possible causes of variance in standards and how does it affect customer satisfaction.
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determined‚ in advance. From the Management’s point of view “What a product should have costed” is more important than “What did it cost?” Standard costs are compared with the actual costs to find out the differences between the two. The differences or variances so obtained are analysed to determine the efficiency of operations‚ so that necessary remedial action may be taken‚ immediately. To plan what should be the cost‚ before production is made‚ is the Underlying idea of Standard Costing. Management
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frontier construction Step 1. Variance/covariance matrix‚ σρσ The expected return and variance for the portfolio are: You can think of the variance as the “weighted average” of all the covariances‚ σiσjρij where the weights are xi and xj. Of course‚ the variance terms are special cases of the covariances when i=j‚ and ρij=1. You can calculate the portfolio variance in the spreadsheet in many different ways. The way I do it is to first calculate the variance/covariance matrix‚ σρσ whose entries
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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 Variance | 15.5623 | 6.281967 | Kurtosis | -0.06933 | 0.58694 | Skewness | -0.22053 | -0.28749 | Range | 19 | 13 | Minimum | 65 | 69 | Maximum | 84 | 82 | Sum | 4579 | 4601 | Count | 61 | 61 | Analysis of descriptive statistics shows that
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Chapter 14 Factor analysis 14.1 INTRODUCTION Factor analysis is a method for investigating whether a number of variables of interest Y1 ‚ Y2 ‚ : : :‚ Yl‚ are linearly related to a smaller number of unobservable factors F1‚ F2‚ : : :‚ Fk . The fact that the factors are not observable disquali¯es regression and other methods previously examined. We shall see‚ however‚ that under certain conditions the hypothesized factor model has certain implications‚ and these implications in turn
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COMPUTER ASSIGNMENT FINS2624 Session 1‚ 2012 Instructions Please read these instructions carefully before you start. Groups You may cooperate on this assignment in groups consisting of up to three students. If you prefer to work alone or with only one other student that is fine‚ too. Either way‚ make sure to enter the student IDs (including the letter) and names of all students in your group in the appropriate cells (B1:B6) on the Answers sheet. There will be draconian punishments for
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to polar coordinate Thus Mean By symmetry if g(x) is odd function g-x=-g(x) then -abgxdx=0 Variance Notation CDF is standard Normal CDF by symmetric ‚CDF ‚ ‚ All the odd moment of standard normal are zero. However‚ even moment is not easy to calculate by integral (Symmetry) Then we say Most of Statistics books will write the pdf then explain the mean and variance but it is not intuitive. Standardization Find PDF of CDF: The PDF is derivative of the CDF
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the resulting variances will be assigned to the cost of goods sold. If the variances are significant‚ they should be prorated to the cost of goods sold and to the inventories. Standard costing and the related variances is a valuable management tool. If a variance arises‚ management becomes aware that manufacturing costs have differed from the standard (planned‚ expected) costs. * If actual costs are greater than standard costs the variance is unfavorable. An unfavorable variance tells management
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Table of Contents Introduction___________________________________________________________3 PART 1: Descriptive Statistics__________________ __________________________5 Defining Important Terms_______________ ___________________________5 Data Analysis of Pay Rate________ _____________________________________6 Data Analysis of Pay Rate vs. Gender¬¬¬¬¬¬¬¬¬¬¬_______________________________________7 Data Analysis of Grade________________ ________________________________9 Data
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FALSE For a random variable X‚ let µ = E (X). The variance of X can be expressed as: V ar(X) = E X 2 − µ2 7. TRUE For random variables Y and X‚ the variance of Y conditional on X = x is given by: V ar(Y |X = x) = E Y 2 |x − [E (Y |x)]2 8. TRUE An estimator‚ W ‚ of θ is an unbiased estimator if E (W ) = θ for all possible values of θ. 9. FALSE The central limit theorem states that the average from a random sample for any population (with finite variance) when it is standardized‚ by subtracting the mean
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