...Calorie and Alcohol Content Percentage in Large Sample of Premium Beers Due Date: December 8, 2003 Descriptive Statistics for Calories Per Beer: Focusing on the Descriptive Statistics of the number of Calories in the sample of beers it can be determined that, the average calories per beer, 142.3478, is the central point around which the data cluster. The median calories per beer, 148, are the middle value in the data and means that fifty percent of the beers have calories above 148 and fifty percent of the beers have calories below 148. The mode, 148, is the value, or number of calories that occur most frequently in the data set. The standard deviation is the typical spread around the mean number of calories per beer and is +/- 29.90221 calories. The range of calories per beer is 143 and measures the total spread of the data from the minimum value of 58 calories per beer to the maximum value of 201 calories per beer. The shape of the data concerning calories per beer is left or negatively skewed, meaning that there are some extremely low outliers, or beers that are very low in calories, which distort the shape of the data. Looking at the Frequency Distribution of Calories per beer it can be said that, 58% of the beers have 150 calories of less, 92.75% of the beers have 170 calories of less, and 34.78% of the beers fall between 150 and 170 calories per beer. Interval Estimates of Calories Per Beer: Concentrating on the Interval Estimate of Calories of beer it can be understood...
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...Statistical Analysis Results for The Finer Diner Sales Proposal Introduction Cost, time and prospect is the tools used to base effective business decisions. Calculating time cost, money cost, and return on forecast, all are based on data mining, marketing research, information analysis and findings. Statistics is way to finding the answer. Statistics – The Method of Organizing Data Generally, statistics is a set of disciplines to analyze quantitative information. Statistics entails all aspects of information: comprehending, collecting, communicating, organizing, and interpreting. All of these are the key reference for forecasting consequences or decision making. Thus, it permits us to estimate the extent of our errors. Purchasing a Business It is not an easy task or decision to purchase a business. Before the final decision is made there are many things to consider. To start with, what exactly do you want to achieve? For whatever reason, you must be sure that it is something that you are ready to devote a large amount of time and energy too. Otherwise, you might be trapped into doing something that you loathe. You must ask yourself how far you are ready to commit. How much of your own time, energy, and money are you willing to sacrifice? Finer Diner Sales Proposal The owner of the Finer Diner submitted a proposal to you in hopes of selling the business to you. His asking price is $250,000. Your...
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...COURSE NOTES TOPIC 1: Introduction to Statistics (Textbook Chapter 1) Introduction No doubt you have noticed the large number of facts and figures, often referred to as statistics, that appear in the newspapers and magazines you read, websites you visit, television you watch (especially sporting events), and in grocery stores where you shop. A simple figure is called a statistic. A few examples: • Home and condominium sales declined 6.5% in Charleston, South Carolina in April, 2006 compared to sales in April, 2005. • Tuition and fees for resident undergraduate students at public four year institutions averaged $5,491 for 2005-06, a 7.1%increase over 2004-05. • Approximately 24 million medicare beneficiaries were enrolled in the new prescription drug program as of January, 2006. • The government reported that 138,000 jobs were added to the economy in April, 2006. • The Dow Jones Industrial Average was 11,094.04 on May 30, 2006. You may think of statistics simply as a collection of numerical information. However, statistics has a much broader meaning. Learning Objectives After completing this chapter, you will be able to: 1. Understand why we study statistics. 2. Explain what is meant by descriptive statistics and inferential statistics. 3. Distinguish between a qualitative variable and a quantitative variable. 4. Distinguish between a discrete variable and a continuous variable. 5. Distinguish among nominal,...
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...your findings. Review the Terminology video to help you understand the terms. Videos and Instructions are found on the Class Materials page in CANVAS. For Part A, use the same data file you used for Assignment 1 with the corrections made. For Part B, use the one of the Customer Dissatisfaction Groups posted on the Class Materials page. Part A 1. Central Tendencies a. Compare the mean and median for the HCTotal, SETotal, PNKTotal, CRTotal, and PHBTotal variables. Discuss what the results indicate. (This is to be a within variable comparison, i.e., what is the difference between the mean of HCTotal and the median of HCTotal?). • To find the mean and median, in the SPSS data file from assignment 1, click on analyze, descriptive statistics, explore, HCTotal, and then okay. These steps will generate an output file with the needed descriptives (mean and median). The mean is the average and the median is the central point or middle value. For HCTotal, the mean is 3.65 and the median is 3.67. Factors affecting the closeness of the mean and median are the range of 1 to 4 and a skewness of -.520 which produces an imperfect bell curve (curve that resembles the shape of a bell). Another factor affecting the mean and median is that the mean is susceptible to outliers (observation point that is distant from other observations) but, from observing the box and whisker plot (histogram-like method of displaying data) there were no significant outliers, hence the close variation between...
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...MATH 533: Applied Managerial Statistics Course Project –Part A I. Introduction. SALESCALL Inc. is a company with thousands of salespeople. The data provided; SALES (the number of sales made this week), CALLS (the number of sales calls made this week), TIME (the average time per call this week), YEARS (years of experience in the call center) and TYPE (the type of training, either group training, online training of no training). The data is used to determine the most productive sales person. With this information the company can tailor it’s training to achieve the greatest number of sales. II. Individual Variables. 1. Sales Descriptive Statistics: SALES Total Variable Count Mean StDev Variance Minimum Q1 Median Q3 SALES 100 42.340 4.171 17.398 32.000 39.250 42.000 45.000 N for Variable Maximum Range IQR Mode Mode SALES 52.000 20.000 5.750 44 12 Data for sales made in a week for SALESCALL Inc. shows that an average of 42 sales are made. The company can expect to have as few as 32 and up to 52 sales in a week. From the data gathered the company can expect to see the average sales made. Looking at the Histogram above shows sale have a bell shaped curve. 2. Calls Descriptive Statistics: CALLS Total Variable Count Mean StDev Variance Minimum Q1 Median Q3 CALLS 100 162.09 18.01 324.53 ...
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...Bottling Company Case Study Vincent Bacon Dr. Pamela Self MAT 300 Statistics December 14, 2013 Bottling Company Case Study Calculate the mean, median, and standard deviation for ounces in the bottles. To determine the mean, which is the statistical average of all numbers involved, we will add the number of ounces together and divide by the number of bottles, in this case 30. For the mean we get 446.1 / 30, for a mean of 14.9. The median, however, is the ‘middle’ number of the bottles ounces. Since we have an even number, it will be the average of the two middle numbers, which will give us a better perspective of the average than just the mean will. In this case it will be 14.8 + 14.8 / 2, which equals 14.8. The standard deviation is calculated to show how far apart the data can be. In the bottling case, after using the mean to get the variance, we divided by 30-1 to get a standard deviation of 0.55 (rounded down to two decimal places). Construct a 95% Confidence Interval for the ounces in the bottles. In order to construct a confidence interval, we need several statistics. The first is the sample mean, which is 14.9. Since we have selected a confidence interval of 95%, we need to find the margin of error to calculate our findings. Using the t-score model ( compute alpha, find the critical probability [.975], the degrees of freedom [999]) (StatTrek, 2013), we find that the critical value is 1.96. When we multiply this by the confidence interval...
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...Journal of Economic Literature Vol. XXXIV (March 1996), pp. 97-114 The Standard Error of Regressions By D E I R D R E N . M C C L O S K E Y and STEPHEN T. ZILIAK University of Iowa Suggestions by two anonymous and patient referees greatly improved the paper. Our thanks also to seminars at Clark, Iowa State, Harvard, Houston, Indiana, and Kansas State universities, at Williatns College, and at the universities of Virginia and Iowa. A colleague at Iowa, Calvin Siehert, was materially helpful. T cant for science or policy and yet be insignificant statistically, ignored by the less thoughtful researchers. In the 1930s Jerzy Neyman and Egon S. Pearson, and then more explicitly Abraham Wald, argued that actual investigations should depend on substantive not merely statistical significance. In 1933 Neyman and Pearson wrote of type I and type II errors: HE IDEA OF Statistical significance is old, as old as Cicero writing on forecasts (Cicero, De Divinatione, 1. xiii. 23). In 1773 Laplace used it to test whether comets came from outside the solar system (Elizabeth Scott 1953, p. 20). The first use of the very word "significance" in a statistical context seems to be John Venn's, in 1888, speaking of differences expressed in units of probable error; Is it more serious to convict an innocent man or to acquit a guilty? That will depend on the consequences of the error; is the punishment death or fine; what is the danger to the community of released...
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...COURSE INFORMATION Class Days: Friday Class Times: 1:00 to 3:40 PM Class Location: EBA 345 Blackboard: blackboard.sdsu.edu Office Hours Times (and by appointment): TH 3:30 – 5:00 F 3:45 – 5:00 Office Hours Location: EBA 322 Units: 3 Course Overview Statistical methods applied to business decision making. (Formerly numbered Information and Decision Systems 301.) The objective of this course is for students to achieve an understanding of fundamental statistical techniques and how they are applied to decision making and the scientific method. Greater emphasis is placed on the application and interpretation, as opposed to the mathematical derivation, of the techniques covered. The content of this course is essential for any student pursuing an undergraduate business major and any person involved in organizational decision making. This course is intended to help satisfy the Association to Advance Collegiate Schools of Business (AACSB) curriculum criterion for management specific knowledge in the area of “Statistical data analysis and management science as they support decision-making processes throughout an organization.” Student Learning Outcomes BSBA students will graduate being: • Effective Communicators • Critical Thinkers • Able to Analyze Ethical Problems • Global in their perspective • Knowledgeable about the essentials...
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...Position: Professor, School of Mathematics and Statistics, University of Sydney. Degrees: University of Queensland, B.Sc. (1961) University of Queensland, B.Sc. (Hons II, 1, Mathematics)(1963). University of Sydney, Ph.D. (Mathematical Statistics)(1969). Thesis title: ”Mixtures of Distributions”. Honours: 1984: Elected Member of the International Statistical Institute. 1990: Elected Fellow of the Institute of Mathematical Statistics. 2008: Awarded the Pitman Medal of the Statistical Society of Australia. Positions held: Biometrician, Queensland Department of Primary Industry, 1961-1964. Lecturer, Biometry Section, Department of Agriculture, University of Sydney, 1964-1966. Lecturer, Department of Mathematical Statistics, University of Sydney, 19661971. Senior Lecturer, Department of Mathematical Statistics, University of Sydney, 1972-1982. Associate Professor, Department of Mathematical Statistics, University of Sydney, 1983-1991. Professor, School of Mathematics and Statistics, University of Sydney, 1991Visiting Associate Professor, Department of Statistics, University of Connecticut, 1969-1970. Visiting Associate Professor, Department of Statistics, University of Waterloo, Canada, 1975-1976. Visiting Lecturer, Department of Statistics, University of California, Berkeley, 1979-1980. Visiting Associate Professor, Department of Statistics, University of Rochester, NY, 1986, January-July. Administration: Head of Department of Mathematical Statistics, University of Sydney, Jan-June, 1979;...
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...TRIDENT UNIVERSITY INTERNATIONAL Falesha R. Vonner Module 5 Case MAT 201 Dr. Lall 20 OCTOBER 2014 HYPOTHESIS TESTING AND TYPE ERRORS Answer the following problems showing your work and explaining (or analyzing) your results. 1. Explain Type I and Type II errors. Use an example if needed. Type I errors, also known as an error of the first kind involves the rejection of a true null hypothesis that is actually the equivalent to a false positive. If the null hypothesis is rejected, a statement can be made that the control does in fact have some effect on the test. But if the null hypothesis is true, then in reality the control does not fight the test in any way visible. Although, type I errors can be controlled, the value of alpha is related to the level of importance that are selected as a direct bearing on type I errors. Alpha is the maximum probability that there will be a type I error. If the value of alpha is 0.05 this equates to a 95% confidence level. Meaning there is a 5% probability that a true null hypothesis will be excluded. In the long run, one out of every twenty hypothesis tests performed at this level will result in a type I error. (www.statistics.about.com, 2014). Type II error, also known as a "false negative": the error of not rejecting a null hypothesis when the alternative hypothesis is the true state of nature. In other words, this is the error...
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...Statistics and Computing Series Editors: J. Chambers D. Hand W. H¨ rdle a Statistics and Computing Brusco/Stahl: Branch and Bound Applications in Combinatorial Data Analysis Chambers: Software for Data Analysis: Programming with R Dalgaard: Introductory Statistics with R, 2nd ed. Gentle: Elements of Computational Statistics Gentle: Numerical Linear Algebra for Applications in Statistics Gentle: Random Number Generation and Monte Carlo Methods, 2nd ed. H¨ rdle/Klinke/Turlach: XploRe: An Interactive Statistical a Computing Environment H¨ rmann/Leydold/Derflinger: Automatic Nonuniform Random o Variate Generation Krause/Olson: The Basics of S-PLUS, 4th ed. Lange: Numerical Analysis for Statisticians Lemmon/Schafer: Developing Statistical Software in Fortran 95 Loader: Local Regression and Likelihood Marasinghe/Kennedy: SAS for Data Analysis: Intermediate Statistical Methods ´ Ruanaidh/Fitzgerald: Numerical Bayesian Methods Applied to O Signal Processing Pannatier: VARIOWIN: Software for Spatial Data Analysis in 2D Pinheiro/Bates: Mixed-Effects Models in S and S-PLUS Unwin/Theus/Hofmann: Graphics of Large Datasets: Visualizing a Million Venables/Ripley: Modern Applied Statistics with S, 4th ed. Venables/Ripley: S Programming Wilkinson: The Grammar of Graphics, 2nd ed. Peter Dalgaard Introductory Statistics with R Second Edition 123 Peter Dalgaard Department of Biostatistics University of Copenhagen Denmark p.dalgaard@biostat.ku.dk ISSN: 1431-8784 ISBN: 978-0-387-79053-4 DOI:...
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...ANNUAL REPORT 2011-12 Government of India Ministry of Statistics and Programme Implementation Sardar Patel Bhawan New Delhi - 110001 Website: http//mospi.gov.in. CONTENTS Chapters Page Vision Mission Introduction Development and Highlights National Statistical Commission Central Statistical Office National Sample Survey Office Coordination of Statistical Activities Computer Centre Statistical Services Indian Statistical Institute Twenty Point Programme Infrastructure and Projects Monitoring Member of Parliament Local Area Development Scheme Hindi Promotion Other Activities ANNEXES I IA IB IC ID IE IF IG IH II IIIA IIIB IVA IVB IVC V VI VII VIII Organisation Charts Ministry of Statistics & Programme Implementation Administration National Statistical Commission Central Statistical Office National Sample Survey Office Computer Centre Programme Implementation Wing Abbreviations used Allocation of Business to the Ministry Project, Seminar/Conference/Workshop and Travel Grant Assistance sanctioned during 2010-11 Project, Seminar/Conference/Workshop and Travel Grant Assistance sanctioned during 2011-12 (Up to December, 2011) Statement of Budget Estimate (SBE) -2011-12 Total Plan Gross Budgetary Support (GBS) for 2010-11 (BE and RE) for North-Eastern Region. Total Plan Gross Budgetary Support (GBS) for 2011-12 (BE and RE) for North-Eastern Region. Performance of Monthly Monitored Items under TPP-2006 (April, 2010 to March, 2011) Performance of Monthly Monitored Items under TPP-2006...
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...Part 1. Basic Concepts of Statistics Basic Concepts of Statistics • Every four years, we suffer through an affliction, the presidential election. • Months before the election, public media will inform us that a poll conducted by the opinion research shows that a candidate gains support of more than 50 percent of voters. 1 2 Basic Concepts of Statistics • However, the high percent of support will be with a margin of error of plus or minus 3%. • What is meant by the term margin of error? • If you have an ambition to become president, you need to know something about statistics. • If you cannot perform statistics yourself, it would be better to hire a statistician right away. 3 Testing Hypotheses: One-sample tests • One-sample tests • Null hypothesis: – Ho: μ ≧0 • Alternative hypothesis: – Ha: μ ＜0 4 What is a Hypothesis? • A hypothesis is a claim (assumption) about a population parameter: – population mean Example: The mean monthly cell phone bill in this city is μ = $42 The Null Hypothesis, H0 • States the claim or assertion to be tested Example: The average number of TV sets in U.S. Homes is equal to three (H0 : µ = 3 ) • Is always about a population parameter, not about a sample statistic H0 : X = 3 6 – population proportion Example: The proportion of adults in this city with cell phones is π = 0.68 5 H0 : µ = 3 The Null Hypothesis, H0 (continued) The Alternative Hypothesis, H1 • Is the opposite of the null hypothesis ...
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...ia l s Ess Learn: • Exactly what you need to know about statistical ideas and techniques • The “must-know” formulas and calculations • Core topics in quick, focused lessons Deborah Rumsey, PhD Auxiliary Professor and Statistics Education Specialist, The Ohio State University Statistics Essentials FOR DUMmIES ‰ by Deborah Rumsey, PhD Statistics Essentials For Dummies® Published by Wiley Publishing, Inc. 111 River St. Hoboken, NJ 07030-5774 www.wiley.com Copyright © 2010 by Wiley Publishing, Inc., Indianapolis, Indiana Published simultaneously in Canada No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except as permitted under Sections 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 646-8600. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008, or online at http://www.wiley.com/go/permissions. Trademarks: Wiley, the Wiley Publishing logo, For Dummies, the Dummies Man logo, A Reference for the Rest of Us!, The Dummies Way, Dummies Daily, The Fun and Easy...
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...J. Bus. Financ. 01 (03) 2013. 94-104 94 Available Online at ESci Journals Journal of Business and Finance ISSN: 2305-1825 (Online), 2308-7714 (Print) http://www.escijournals.net/JBF LACK OF TIMELINESS AS AN EXPLANATION OF THE LOW CONTEMPORANEOUS RETURNS-EARNINGS ASSOCIATION Jaouida E. Trabelsi Institute of the High Commercial Studies of Sfax (IHEC), University of Sfax, Tunisia. A B S T R A C T This paper empirically tests whether the low contemporaneous returns-earnings association set by previous empirical researches may be explained by lack of timeliness of accounting numbers. It hypothesises that if the criteria for accounting recognition yield a multi-period lag in earnings recognitions of economic events and if these events affect an informed market immediately when they occur, then future periods’ earnings would have explanatory for current returns as well as current earnings. To assess the significance of future earnings as an explanatory variable for stock returns we regress at first step annual returns on current earnings and at second step, annual returns on current earnings and successively next period and next two periods’ earnings. Results show that future earnings continue to explain current returns. The evidence is characteristic of a substantial recognition lag in earnings that extends to the immediate next period. However, over one year, earnings do not seem reflecting any relevant economic event impounded in security prices at previous period. The earnings...
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