projection pursuit directions. The statistical properties of the estimators based on such contrast functions are analyzed under the assumption of the linear mixture model‚ and it is shown how to choose contrast functions that are robust and/or of minimum variance. Finally‚ we introduce simple fixed-point algorithms for practical optimization of the contrast functions. These algorithms optimize the contrast functions very fast and reliably. 1 Introduction A central problem in neural network research
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Statistics: • Science of gathering‚ analyzing‚ interpreting‚ and presenting data • Measurement taken on a sample • Type of distribution being used to analyze data Descriptive statistics: Using data gathered on a group to describe or reach conclusions about that same group only. Descriptive statistics are the tabular‚ graphical‚ and numerical methods used to summarize data. Collect‚ organize‚ summarize‚ display‚ analyze Eg: According to Consumer Reports‚ General Electric washing machine
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Operations Management Listen-Up.com Case introduction Mai Chen‚ fresh from business school‚ has been hired by Listen-Up.com‚ a small‚ start-up manufacturer of hearing aids‚ to resolve the difficulties within its customer service group. The company’s products are sold over the Internet or phoned in using the company’s toll-free telephone lines‚ but telephone orders is the main and growing sales channel. During its three years of existence the company has experienced rapid growth with the
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then selecting all the returns of all assets. Question d The average weight= 1/12 Sum of weight =1 Solver portfolio Return=MMULT(whole covariance matrix‚ TRANSPOSE(whole correlation matrix)) Variance ==MMULT(average weight‚MMULT(whole covariance matrix‚ TRANSPOSE(average weight))) Risk =SQRT(Variance) Mvp First calculate sigma and return Sigma =SQRT(MMULT(MMULT(weight‚whole covariance matrix)‚TRANSPOSE(weight))) Return=MMULT(weight‚TRANSPOSE(annual return)) Then using solver set sigma in
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In the given function‚ respond is a binary function‚ which is dependant on the other explanatory variable which aligns with the requirement of the linear probability model (LPM). This is an extention to the zero conditional mean condition which assumes that E(ul(resplast)‚(avggift)‚(propresp)‚(mailsyear))=0 and hence allow for E(ylx) to omit the error term producing P(respond = 1lx)= β(0)+ β(1) resplast+ β(2)avggift+ β(3)propresp+ β(4)mailyear. This allows the interpretation of β(1) to be the degree
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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 of hypothesis that lag-1 correlation coefficient is zero: 0.83827 1 Note that with eCampus‚ you must click
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Remington’s has large portions‚” on the Remington Data worksheet of the Remington’s Data Set workbook: Mean -3.26 Standard deviation-0.911 Range -3 4 Mean 3.261306533 Standard Error 0.064596309 Median 4 Mode 4 Standard Deviation 0.911243075 Sample Variance 0.830363941 Kurtosis -1.16899198 Skewness -0.663704706 Range 3 Minimum 1 Maximum 4 Sum 649 Count 199 Largest(1) 4 Smallest(1) 1 Confidence Level(95.0%) 0.12738505 Include your calculations in a 1- to 2-page document in which you also do the following:
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1. How you would use the concept of probabilities to apply to profiles for hiring more satisfied individuals and 2. Other ways that probability is used in business (Use the Business Source Elite Database in the Cybrary‚ research how probability is used in business). Begin your email to AIU by first providing an overview of the database‚ i.e.‚ a story. Include the following pieces of information. Part I a) What is the gender distribution (%females and %males)? Solution. From the database
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model. Since absolute value of beta 1 is bigger than 0.1. (iv) Command: su Course_Eval R-square is this estimated model is 0.0357. R-square measures the fraction of variance of Y that is explained by X‚ ranges between zero (no fit) and one (perfect fit). 0.0357 is small‚ thus beauty does not explain a large fraction of the variance in evaluations across courses. (V) No‚ P value is associated with a test statistic. Course_ Eval = 4.00+0.133×Beauty. (0.025) (0.032) so the t-statistic
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www.ignousolvedassignments.com ASSIGNMENT Course Code : MS - 8 Course Title : Quantitative Analysis for Managerial Applications Assignment Code : MS-8/SEM - II /2013 Coverage : All Blocks Note: Attempt all the questions and submit this assignment on or before 31st October‚ 2013 to the coordinator of your study centre. “Statistical unit is necessary not only for the collection of data‚ but also for the interpretation and presentation”. Explain the statement
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