Dear Peterson Pottery‚ Here are the variances explaining the Clay and Glaze operations. By knowing the variances‚ we can paint a picture to see what is going on within those operations. Here you will find the picture of Clay Materials and Labor‚ Glaze Material and Labor‚ Variable and Fixed Overhead. By analyzing the data‚ shown by the variances‚ there are some issues that must be brought to attention. By taking time to analyze the data and devising a plan will help your business save on costs
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(a) Suppose all distinct assets in the economy have a correlation of ρ = −.02 with every other asset. Let the variance of each asset be 0.25‚ and the investor holds an equally weighted portfolio of these assets. How many of such assets should an investor hold so that the variance of her portfolio is zero? (b) If the correlation was 0.02 can the investor ever achieve a zero variance? (c) For the case that the correlation is 0.4‚ and the investor holds an equally weighted portfolio of 10 assets
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test this difference‚ we determine the difference between the statistic (the difference between the means)‚ and the hypothesized value for the parameter (0). o if the population variance is known‚ the sampling distribution of differences is normally distributed. o if the population variance is UNKNOWN‚ the sampling distribution of differences is the t distribution‚ for the appropriate degrees of freedom. Assumptions that must be considered: 1. Independence. The samples
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Compagnie du Froid S.A. (#s in thousands) Variance Evaluations The CEO‚ Jacques Trumen‚ of Compagine discusses ambitious growth opportunities and the profit plan for three regions with very competent managers that strive to produce the best results for their division. The Italian and Spanish had favorable product mix variance of 68 and 4‚241‚ respectively. This additional profit both of these regions attained should be looked by Pierre and Andres and determine cost implications on “specialties”
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material price and usage variances for the month. Material Price (Standard) | Material X | Material Y | Total Price | Product A | 218‚400 | 35‚700 | 254‚100 | Product B | 280‚800 | 61‚200 | 342‚000 | Total | 499‚200 | 96‚900 | 596‚100 | Material Price (Actual) Material X = 39‚000 @ 12.40 Material Y = 11‚000 @ 8.70 | Material X | Material Y | Total Price | | 483‚600 | 95‚700 | 579‚300 | Difference | 15‚600 | 1‚200 | 16‚800 | Usage Variance Material X = 15‚600
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Generating a budget is complex undertaking‚ and for a budget to be effective the organization ought to follow it strictly. However‚ no matter how closely a business follows their guidelines there will always be some form of variances. The organization should expect a few variances and be able to work these discrepancies in any budget constraints. Managing the Budget within the Forecast: According to Finkler‚ Kovner and Jones‚ (2007)‚ organizations exercise control over operations through the use
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MFE MATLAB Function Reference Financial Econometrics Kevin Sheppard October 30‚ 2009 2 c 2001-2009 Kevin Sheppard Contents Notes v 1 Included but not documented functions 1 2 Cross Sectional Analysis 5 2.1 Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.1.1 3 Regression: ols Stationary Time Series 3.1 ARMA Simulation . . . . . . . . . . .
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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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concerning variances for test concerning frequency distributions to test the independence of two variables The chi-square variable cannot be negative and the distributions are positively skewed. At about 100 d.f.‚ the distribution becomes symmetrical. The area under each chi-square distribution is equal to 1 or 100%. BUSSTAT prepared by CSANDIEGO A portion of 2 distribution BUSSTAT prepared by CSANDIEGO Test for a Single Variance Used to test a claim about a single variance‚ should
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some (though not necessarily every bit) of work for any substantial calculations. 1. (Each part 5 points) Suppose . That is‚ X has a normal distribution with μ=30 and σ2=144. 1a. Find a transformation of that will give it a mean of zero and a variance of one (ie.‚ standardize ). Let the transformed variable be named Z. We desire μz=0‚ σ2z=1. This means 0=a+b μX and b2 σ2X=1. One solution to this system of equations is b=1/12 and a=-5/2. Of course‚ if you recognized the fact that our standard
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