purchased to detect any unusual deviations from its benchmarks. Recommendation: Standard costing is a great tool for this company and they must continue to use it. It will aid in helping management analyze actual costs versus standard costs. The variances that can be calculated will help them make future decisions in regard to cost cutting and also many other things such as what quality materials to use in production. Analysis: During May Direct materials per unit = 110‚000 square
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these three possibilities‚ I will address the company’s desire to finish the warehouse expansion in 40 weeks. To do this‚ I used the expected time formula‚ the variance formula‚ standard variance and the “z score.” In the formulas below‚ the optimistic outlook is “a”‚ the most likely is “m”‚ and the pessimistic is “b‚” ℴ is standard variance and the z score is used to find the percentage the warehouse is able to be built in 40 weeks. Additionally‚ the actual expected time is 43 weeks‚ shown below is
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distribution between 0 and 1. Then test to determine if the lag-1 correlation coefficient can be assumed to be zero and report those results below. (Note: before you would actually accept these as random numbers‚ you would also want to check the lag-j correlation coefficients for several more values of j Fill in the blanks below: Estimate for lag-1 correlation coefficient: 0.016457823 Variance of estimator: 0.006502 Test statistic (i.e.‚ ratio of lag-1 estimate to its standard deviation): 0.204107 p-value
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its sales force spends entertaining clients. The following is a random sample of six entertainment expenses (dinner costs for four people) from expense reports submitted by members of the sales force. $157‚ $132‚ $109‚ $145‚ $125‚ $139. Calculate the mean and sample variance(s^2) and standard deviation. Mean = 807/6 = 134.5. Sample Variance = (109925 – (807^2/6)/6-1 = (109925 – 108541)/5 = 1384/5 = 276.8. Standard Deviation = √276.8 = 16.6373. ***the 109925 is all values of x individually
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half fall above). Middle number in a sorted list. If population is an even number‚ divide between neighbors on the midpoint. Mode = most frequent score Deviation = (X - M) Sum of Squares (SS) = ∑ (X - M)^2 Variance = ∑ (X - M)^2 / N (mean squares) Standard Deviation = sqrt of variance Pearson product-moment correlation coefficient (r): degree to which X and Y vary together‚ relative to the degree to which they vary independently Sum of cross products SPxy: Measure of the degree to which
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(BUE under normality). Likewise the usual statistic‚ such as the t-ratios‚ will have the usual distribution‚ such as the t-distribution‚ under the null hypothesis. Under the alternative hypothesis‚ however‚ there will be loss of power since the variance of the estimates on the collinear terms will become highly variable. c. How might you go about detecting the presence and severity of collinearity? If the coefficient estimate of the suspected collinear term is significant‚ then collinearity is not
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Skewness‚ Kurtosis‚ and the Normal Curve Skewness In everyday language‚ the terms “skewed” and “askew” are used to refer to something that is out of line or distorted on one side. When referring to the shape of frequency or probability distributions‚ “skewness” refers to asymmetry of the distribution. A distribution with an asymmetric tail extending out to the right is referred to as “positively skewed” or “skewed to the right‚” while a distribution with an asymmetric tail extending out to
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Think Stats: Probability and Statistics for Programmers Version 1.6.0 Think Stats Probability and Statistics for Programmers Version 1.6.0 Allen B. Downey Green Tea Press Needham‚ Massachusetts Copyright © 2011 Allen B. Downey. Green Tea Press 9 Washburn Ave Needham MA 02492 Permission is granted to copy‚ distribute‚ and/or modify this document under the terms of the Creative Commons Attribution-NonCommercial 3.0 Unported License‚ which is available at http://creativecommons
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Solutions Manual Econometric Analysis Fifth Edition William H. Greene New York University Prentice Hall‚ Upper Saddle River‚ New Jersey 07458 Contents and Notation Chapter 1 Introduction 1 Chapter 2 The Classical Multiple Linear Regression Model 2 Chapter 3 Least Squares 3 Chapter 4 Finite-Sample Properties of the Least Squares Estimator 7 Chapter 5 Large-Sample Properties of the Least Squares and Instrumental Variables Estimators 14 Chapter 6 Inference and Prediction 19 Chapter 7
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Chapter 8: Cost Estimation Strategic Role of Cost Estimation * Cost Estimationthe development of a well-defined relationship b/t a cost object and its cost drivers for the purpose of predicting the cost * Facilitates strategic mgmt is 2 ways * Helps predict future costs * Helps identify key cost drivers for a cost object and which driver is most useful * Using Cost Estimation to Predict future costs * Strategic mgmt requires accurate estimates for the
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