...NOMOR 1 Buatlah ringkasan statistik deskriptif (max, min, rata-rata, median, deviasi standar, kuartil) dari variabel III, IV dan V. Anda akan lihat output komputer di Windows Session. TUJUAN Untuk melihat persebaran nilai tertinggi, rata-rata, nilai tengah standar deviasi dan nilai di setiap quartil di dalam ‘Y_total”/ pendapatan total di seluaruh kecamatan yang ada. Teori Pendugaan Parameter Untuk mengetahui mengetahui hasil dari seluruh nilai yang meliputi nilai variable Y_total : Pendapatan total dari setiap kecamatan N : jumlah populasi yang di analisa Mean : rata rata pendapatan dari seluruh kecamatan yang ada SE mean : StDev : besarnya nilai penyimpangan yang mungkin terjadi di nilai rata rata populasi yang ada Minimum : pendapatan terkecil dari seluruh kecamatan Q1 : Median : nilai tengah dari data populasi yang ada Q3 : Maximum : pendapatan terkecil dari seluruh kecamatan HASIL DATA MINITAB Variabel Kategorik Variabek Kuantitatif Kelompok I II III IV V 3 Monitoring Evaluasi Y_Total ExpEduc ExpHealth Descriptive Statistics: Y_Total Variable N N* Mean SE Mean StDev Minimum Q1 Median Y_Total 151 0 22765636 2170459 26671048 200000 7200000 13200000 Variable Q3 Maximum Y_Total 30000000 180000000 Untuk Pendapatan Total Rata-rata pendapatan total di pedesaan adalah IDR 22,765,636,-, dengan ukuran yang menggambarkan tingkat penyebaran data dari nilai rata-rata...
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...Math 221 Quiz Review for Weeks 5 and 6 1. Find the area under the standard normal curve between z = 1.6 and z = 2.6. 2. A business wants to estimate the true mean annual income of its customers. It randomly samples 220 of its customers. The mean annual income was $61,400 with a standard deviation of $2,200. Find a 95% confidence interval for the true mean annual income of the business’ customers. 3. IQ test scores are normally distributed with a mean of 100 and a standard deviation of 15. An individual's IQ score is found to be 120. Find the z-score corresponding to this value. 4. Two high school students took equivalent language tests, one in German and one in French. The student taking the German test, for which the mean was 66 and the standard deviation was 8, scored an 82, while the student taking the French test, for which the mean was 27 and the standard deviation was 5, scored a 35. Compare the scores. 5. A business wants to estimate the true mean annual income of its customers. The business needs to be within $250 of the true mean. The business estimates the true population standard deviation is around $2,400. If the confidence level is 90%, find the required sample size in order to meet the desired accuracy. 6. The distribution of cholesterol levels in teenage boys is approximately normal with mean = 170 and standard deviation = 30 (Source: U.S. National Center for Health Statistics). Levels above 200 warrant attention. Find the probability that...
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...Skewness Skewness From Wikipedia, the free encyclopedia Example distribution with non-zero (positive) skewness. These data are from experiments on wheat grass growth. In probability theory and statistics, skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean. The skewness value can be positive or negative, or even undefined. The qualitative interpretation of the skew is complicated. For a unimodal distribution, negative skew indicates that the tail on the left side of the probability density function is longer or fatter than the right side – it does not distinguish these shapes. Conversely, positive skew indicates that the tail on the right side is longer or fatter than the left side. In cases where one tail is long but the other tail is fat, skewness does not obey a simple rule. For example, a zero value indicates that the tails on both sides of the mean balance out, which is the case for a symmetric distribution, but is also true for an asymmetric distribution where the asymmetries even out, such as one tail being long but thin, and the other being short but fat. Further, in multimodal distributions and discrete distributions, skewness is also difficult to interpret. Importantly, the skewness does not determine the relationship of mean and median. Contents [hide] 1 Introduction 2 Relationship of mean and median 3 Definition 3.1 Pearson's moment coefficient of skewness 3.2 Properties 3.3 Sample...
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...STOCHASTIC FRONTIER ANALYSIS 1 MOTIVATION • Usual textbook presentations treat producers as successful optimizers. They maximize production, minimize cost, and maximize profits. • Conventional econometric techniques build on this paradigm to estimate production/cost/profit function parameters using regression techniques where deviations of observed choices from optimal ones are modeled as statistical noise. • However though every producer may attempt to optimize, not all of them may succeed in their efforts. For example, given the same inputs, and the same technology, some will produce more output than others, i.e., some producers will be more efficient than others. • Econometric estimation techniques should allow for the fact that deviations of observed choices from optimal ones are due to two factors: failure to optimize i.e., inefficiency due to random shocks • Stochastic Frontier Analysis or SFA is one such technique to model producer behavior. 2 USEFULNESS OF SFA • SFA produces efficiency estimates or efficiency scores of individual producers. Thus one can identify those who need intervention and corrective measures. Since efficiency scores vary across producers, they can be related to producer characteristics like size, ownership, location, etc. Thus one can identify source of inefficiency. SFA provides a powerful tool for examining effects of intervention. For example, has efficiency of the banks changed after deregulation? Has this change varied across...
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...Stata Guide (Version 10) Sunaina Dhingra This guide introduces the basic commands of Stata. 1. Starting Stata Start Stata by using shortcut on the desktop that you can double-click Start Stata using the Windows menu, click the Start > All Programs > Stata 10 Locate a Stata data file, with *.dta extension, and double-click 2. Stata environment Command—this is where Stata command are typed Results—output from commands, and error messages, appear here Review—a listing of commands recently executed Variables—names of variables in data and labels (if created) 3. Recording Output a. Log file Log file keeps track of everything that happens in the result window. In short, it keeps a record of the commands you have issued and their results during your session. In STATA log files have the extension .smcl or .log Go to File > Log > Begin to open a log file. A screen will pop up, asking for where you want the log file to be saved. Use command: log using filename [, append replace text] where filename is any name you wish to give the file. The append option simply adds more information to an existing file, whereas the replace option erases anything that was already in the file. Full logs are recorded in one of two formats: SMCL (Stata Markup and Control Language) or text (meaning ASCII). The default is SMCL, but the option text can change that. If you want to temporarily turn off the log session, type log off in the command window. The session will resume...
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...University of Phoenix Material Statistical Symbols and Definitions Matching Assignment Match the letter of the definition on the right to the appropriate symbol on the left. |Symbols |Definitions | |( (Uppercase Sigma) B.__ |Null hypothesis | |( (Mu) H.___ |Summation | |( (Lowercase Sigma) E.___ |Factorial | |( (Pi) I.___ |Nonparametric hypothesis test | |( (Epsilon) G.__ |Population standard deviation | |(2 (Chi Square) D.___ |Alternate hypothesis | |! C.___ |Maximum allowable error | |H0 A.__ |Population mean | |H1 F.__ |i. Probability of success in a binomial...
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...Definitions Matching Assignment Match the letter of the definition on the right to the appropriate symbol on the left. Symbols Definitions 1. S (Uppercase Sigma) B a. Null hypothesis 2. m (Mu) H b. Summation 3. s (Lowercase Sigma) E c. Factorial 4. p (Pi) I d. Nonparametric hypothesis test 5. e (Epsilon) G e. Population standard deviation 6. c2 (Chi Square) D f. Alternate hypothesis 7. ! C g. Maximum allowable error 8. H0 A h. Population mean 9. H1 F i. Probability of success in a binomial trial Match the letter of the term on the right to the definition of that term on the left. Definitions Terms 1. The average of the squared deviation scores from a distribution mean. D a. Reliability 2. Midpoint in the distribution of numbers. E b. Mode 3. It has to do with the accuracy and precision of a measurement procedure. A c. Generalization 4. Examines if an observed causal relationship generalizes across persons, settings, and times. F d. Variance 5. The difference between the largest and smallest score in a distribution. I e. Median 6. The arithmetic average. G f. External validity 7. Refers to the extent to which a test measures what we actually wish to measure. K g. Mean 8. The most frequently occurring value in a set of numbers. B h. Internal validity 9. The conclusion...
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...[pic] Retail Loss Prevention: Doing more with Analytics February 2009 Abstract T he retail industry is in the middle of an unprecedented economic crisis. All retailers are trying to figure out how to cut costs, retain customers, conserve cash and more importantly stay in business. Recently, the National Retail Federation (NRF) polled readers of its SmartBrief asking them what was on top of their mind. Loss Prevention (LP) came in second only to the overall economy! It is no surprise given that every dollar saved from retail shrink is a dollar added directly to the bottom-line. Looking back in history, we have seen tough times like these are conducive for higher shrink numbers. This is mainly due to retailers cutting down loss prevention staffing and store personnel, slowdown in technology investments, and increase in theft owing from people who cannot handle the economic pressure. LP organizations are at different stages of evolution when we look at their capability to harness the power of analytics – From basic reporting on shrink to understanding the key drivers with high correlation to shrink and managing by exception with the help of predictive models. There is a need to utilize available data assets effectively by building capabilities to report, analyze and predict shrink accurately. This article reviews the trends in retail shrink, its sources and how analytical techniques can help attack shrink in a cost effective manner. Retail Shrink Trends ...
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...Final Exam Review Questions You should work each of the following on your own, then review the solutions guide. DO NOT look at the solutions guide first. 1. Determine whether the following are nominal, ordinal, interval, and ratio. a. Daily temperatures in Ripon, WI b. Test scores in statistics class Solution: (a) would be interval as there is no zero while (b) would be ratio as there is a zero. 2. The following numbers represent the weights in pounds of six 7-year old children in Mrs. Jones' 2nd grade class. {25, 60, 51, 47, 49, 45} Find the mean; median; mode; variance; standard deviation. Solution: This would be a sample from the class mean = 46.166 (=AVERAGE) median = 48 (=MEDIAN) mode does not exist (looking at the data) variance = 134.5667 (=VARIANCE.S) standard deviation =11.60029 (=STDEV.S) 3. If the variance is 846, what is the standard deviation? Solution: standard deviation = square root of variance = sqrt(846) = 29.086 4. If we have the following data: 34, 38, 22, 21, 29, 37, 40, 41, 22, 20, 49, 47, 20, 31, 34, 66. Draw a stem and leaf. Discuss the shape of the distribution. Solution: 2 | 2 1 9 2 0 0 3 | 4 8 7 1 4 4 | 0 1 9 7 5 | 6 | 6 This distribution is right skewed (positively skewed) because the “tail” extends to the right. 5. Find the regression equation of the following data. X | 6 | 5 | 7 | 6 | 5 | 6 | 8 | 9 | 4 | y | 14 | 33 | 43 | 54 | 21 | 33 | 43 | 24 | 28 | ...
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...THE POETICS OF ARISTOTLE A TRANSLATION BY S. H. BUTCHER A Penn State Electronic Classics Series Publication THE POETICS OF ARISTOTLE trans. S. H. Butcher is a publication of the Pennsylvania State Univer- sity. This Portable Document file is furnished free and without any charge of any kind. Any person using this document file, for any purpose, and in any way does so at his or her own risk. Neither the Pennsylvania State University nor Jim Manis, Faculty Editor, nor anyone associated with the Pennsylvania State University assumes any responsibility for the material contained within the document or for the file as an electronic transmission, in any way. THE POETICS OF ARISTOTLE trans. S. H. Butcher, the Pennsylvania State University, Electronic Classics Series, Jim Manis, Faculty Editor, Hazleton, PA 18201-1291 is a Portable Document File produced as part of an ongoing student publication project to bring classical works of literature, in English, to free and easy access of those wishing to make use of them. Cover Design: Jim Manis Copyright © 2000 The Pennsylvania State University The Pennsylvania State University is an equal opportunity university. THE POETICS OF ARISTOTLE THE POETICS OF ARISTOTLE Analysis of Contents A TRANSLATION BY S. H. BUTCHER I ‘Imitation’ the common principle of the Arts of Poetry. II The Objects of Imitation. III The Manner of Imitation. IV The Origin and Development of Poetry. V Definition of the Ludicrous, and a brief sketch of the rise...
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...comparison distribution where the null hypothesis should be rejected”. This means determining a target against the results that will be compared or to what extreme the sample score would have to be for it to be too doubtful that they could get such an extreme score if the null hypothesis were true. Here the null hypothesis would be rejected if the point of the cutoff sample scores reaches or exceeds the sample score. If the null hypothesis is true the Z score is established at a score which would be improbable. 4. This step is to get the actual results for the sample. “Determine your sample’s score on the comparison distribution” . After one gets the test’s sample outcome, one moves on to step 5. 5. Here it is either declared the test is invalid or one rejects the null hypothesis by noting the similarity of the cutoff Z score to the samples Based on the information given for the following studies, decide whether to reject the null hypothesis. Assume that all populations are normally distributed. For each, give: (A) (a) 1.645...
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...Stochastic frontier analysis of the efficiency of Nigerian banks Abstract Using the Stochastic Frontier Analysis (SFA) the efficiency of Nigerian banks was analysed. The result of the study proved that there is inefficiency in the Nigerian banking system and that the level of inefficiency ranged from 0 to 19 per cent of total cost. The study was able to derive the individual bank's level of inefficiency. Put differently, the study was able to derive the individual bank's level of efficiency. I. INTRODUCTION In the last three decades, as bank regulators open their financial Industries for competition and liberalisation, many banks operated at a level that is less efficient and profitable leading to unsoundness or distress in the industry; thus generating concerns and worries among the bank stakeholders. There are a large number of studies which employ models to explain inter-bank differences in earnings, bank efficiency and continuous existence (failure) in the United States of America and other developed countries of the world. Similar studies have not been carried out using data from emerging markets like Nigeria especially when viewed against the background of the statement of Barltrop and McNaughton (1992) that financial analysis should be done within the context of the particular country and economic environment as each country has a different economic environment, different regulatory and legal environment, different commercial practices, different accounting...
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...SIX SIGMA exam preparation Here you are a pool of 200 questionS . For the exam there will be 50 questions from this pool (different wording, numbers etc. could be used in exam) to create the written or the oral sessions of the exam (words and numbers could be different from the questions presented here.) The Written Exam will start at 9:00 AM and will finish at 12:00 AM You will be allowed to bring in the classroom only a pen and a pencil / rubber and nothing else (no phone, NO pc Nor any other electronic device) the oral exam will start at 12:30 Up to 3 points will be given at the oral exam where we will review the answer given at the written exam and if needed we will choose other questions taken from the pool in order to probe the knowledge gained. The oral exam usually last 15 minutes per candidate. up to 2 points will be assigned by the teacher based on the presence and participation during the entire course. Slides and PDF Books (for your personal reference and interest only) are available on iCorsi. Lean Six Sigma Paolo Rossetti – 2014 – Page 1 of 12 POOLs of QUESTIONS 01 - Six Sigma in a Nutshell 1. 2. 3. 4. 5. 6. 7. 8. 9. What is quality? How Gavin defines quality using different “dimensions”? State and comment the 8 dimensions of quality. Which is the Six Sigma definition of quality? What does CTQ stands for? What types of CTQ exists? Which are the sources of variability in a product? How can variability be expressed in statistical terms? How Edward Deming...
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...= = = = = = Research and Evaluation I - 341 Syllabus –Rev 6 Note: Please note each week’s individual assignments from this syllabus, find the corresponding chapter (s) and read them along with chapters assigned for reading in the e-text. As far as the assignments to be submitted are concerned, you are only, and only, responsible for the requirements which appear in my syllabus as presented below. |Values for the Course Assignments |Percent | |Individual | |Participation (All Weeks - 2% each) |10 | |DQ’s (All Weeks – 2% each) |10 | |Weekly Summaries (All Weeks - 1% each) |5 | |Business Research Paper (week One) |4 | |Survey (Week Two) |6 | |Assignments from the Text - – Section Exercises (Week Two) ...
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...GARCH Model Fitting For IBM Introduction: Nowadays, many econometric studies have documented that financial return time series tend to be highly heteroskedastic, with varying variability or volatility and thick-tailed marginal distribution. Volatility is the conditional variance, which is unobservable and tends to be clustered together. Clustering is referred to as the phenomenon that large price changes of either signs tend to be followed by large changes, and similarly small changes follow small changes. Sudden bursts of volatility in financial returns exhibit strong dependence on the return time series, and a period of tranquility alternates with a period of volatility bursts. A popular ARCH, which is called Autoregressive Conditional Heteroscedasticity, modeling was introduced to capture the predictability of volatility ad the volatility clustering as well as the thick-tailed marginal distribution of the return time series. The tremendous success of GARCH modeling in empirical work makes may econometricians to treat it as the benchmark model for financial time series. Data Description: In this GARCH model fitting report, I use the history of stock price of IBM from January 2nd, 2013, to December 31th, 2013, collecting the data of “High”, “Low”, “Close”, and so on. At the beginning, I run the ACF for the log return and squared log return of IBM to get the autocorrelation for them. Then, by assuming that the model is normal distribution, we use the R to fit a GARCH model...
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