...and drawing conclusions from data. Students are exposed to four broad conceptual themes: • Exploring Data: Describing patterns and departures from patterns • Sampling and Experimentation: Planning and conducting a study • Anticipating Patterns: Exploring random phenomena using probability and simulation • Statistical Inference: Estimating population parameters and testing hypotheses Students who successfully complete the course and examination may receive credit and/or advanced placement for a one-semester introductory college statistics course. Textbook: The Practice of Statistics, 3rd ed. (2008) by Yates, Moore and Starnes (Freeman Publishers) Calculator needed: TI-83 Graphing Calculator (Rentals Available) TI-83+, TI-84, TI-84+ are acceptable calculators as well Note: Any other calculator may/may not have statistical capabilities, and the instructor shall assist whenever possible, but in these instances, the student shall have sole responsibility for the calculator’s use and application in this course. AP STATISTICS Textbook: The Practice of Statistics, 3rd edition by Yates, Moore and Starnes Preliminary Chapter – What Is Statistics? (2 Days) A. Where Do Data Come From? 1. Explain why we should not draw conclusions based on personal experiences. 2. Recognize whether a study is an experiment, a survey, or an observational study that is not a survey. 3. Determine the best method for producing data...
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...Chapter 10 Statistical Inference about Means and Proportions with Two Populations Learning Objectives 1. Be able to develop interval estimates and conduct hypothesis tests about the difference between two population means when σ 1 and σ 2 are known. Know the properties of the sampling distribution of x1 − x2 . Be able to use the t distribution to conduct statistical inferences about the difference between two population means when σ 1 and σ 2 are unknown. Learn how to analyze the difference between two population means when the samples are independent and when the samples are matched. Be able to develop interval estimates and conduct hypothesis tests about the difference between two population proportions. Know the properties of the sampling distribution of p1 − p2 . 2. 3. 4. 5. 6. 10 - 1 Chapter 10 Solutions: 1. a. b. x1 − x2 = 13.6 - 11.6 = 2 zα / 2 = z.05 = 1.645 x1 − x2 ± 1.645 σ 12 n1 + 2 σ2 n2 2 ± 1.645 (2.2) 2 (3) 2 + 50 35 2 ± .98 c. (1.02 to 2.98) zα / 2 = z.025 = 1.96 2 ± 1.96 (2.2) 2 (3) 2 + 50 35 2 ± 1.17 2. a. z= (.83 to 3.17) = (25.2 − 22.8) − 0 (5.2) 2 62 + 40 50 = 2.03 ( x1 − x2 ) − D0 σ 2 1 n1 + σ 2 2 n2 b. c. 3. a. p-value = .5000 - .4788 = .0212 p-value ≤ .05, reject H0. z= ( x1 − x2 ) − D0 σ 2 1 n1 + σ 2 2 = (104 − 106) − 0 (8.4) 2 (7.6) 2 + 80 70 = −1.53 n2 b. c. 4. a. p-value = 2(.5000 - .4370) = .1260 p-value > .05, do not reject H0. x1...
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...Acquisitions Editor: Charles McCormick, Jr. Developmental Editor: Elizabeth Lowry Editorial Assistant: Nora Heink Senior Marketing Communications Manager: Libby Shipp Marketing Manager: Adam Marsh Content Project Manager: Jacquelyn K Featherly Media Editor: Chris Valentine Manufacturing Buyer: Miranda Klapper Production House/Compositor: MPS Limited, a Macmillan Company Senior Rights Specialist: John Hill Senior Art Director: Stacy Jenkins Shirley Internal Designer: KeDesign/cmiller design Cover Designer: Cmiller design Cover Images: © iStock Photo © 2012, 2009 South-Western, a part of Cengage Learning ALL RIGHTS RESERVED. No part of this work covered by the copyright herein may be reproduced, transmitted, stored or used in any form or by any means graphic, electronic, or mechanical, including but not limited to photocopying, recording, scanning, digitizing, taping, Web distribution, information networks, or information storage and retrieval systems, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without the prior written permission of the publisher For product information and technology assistance, contact us at Cengage Learning Customer & Sales Support, 1-800-354-9706 For permission to use material from this text or product, submit all requests online at www.cengage.com/permissions Further permissions questions can be emailed to permissionrequest@cengage.com ExamView® and ExamView Pro® are registered trademarks of FSCreations, Inc. Windows is...
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...| |UNIVERSITI TUNKU ABDUL RAHMAN (UTAR) | | | | | |FACULTY OF BUSINESS AND FINANCE (FBF) | Teaching Plan | |Unit Code & |UBEQ1123 QUANTITATIVE TECHNIQUES II | | |Unit Title: | | | |Course of Study: |Bachelor of Commerce (Hons) Accounting | | | |Bachelor of Business Administration (Hons) | | | |Bachelor of Business Administration (Hons) Banking and Finance | | | |Bachelor of Business Administration (Hons) Entrepreneurship ...
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...of 4 is 50%. 3 is 25% of 12. Etc. But do you know enough about percentages? Is a percentage the same thing as a fraction or a proportion? Should we take the difference between two percentages or their ratio? If their ratio, which percentage goes in the numerator and which goes in the denominator? Does it matter? What do we mean by something being statistically significant at the 5% level? What is a 95% confidence interval? Those questions, and much more, are what this book is all about. In his fine article regarding nominal and ordinal bivariate statistics, Buchanan (1974) provided several criteria for a good statistic, and concluded: “The percentage is the most useful statistic ever invented…” (p. 629). I agree, and thus my choice for the title of this book. In the ten chapters that follow, I hope to convince you of the defensibility of that claim. The first chapter is on basic concepts (what a percentage is, how it differs from a fraction and a proportion, what sorts of percentage calculations are useful in statistics, etc.) If you’re pretty sure you already understand such things, you might want to skip that chapter (but be prepared to return to it if you get stuck later on!). In the second chapter I talk about the interpretation of percentages, differences between percentages, and ratios of percentages, including some common mis-interpretations and pitfalls in the use of percentages. Chapter 3...
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...Edition of Statistics: The Art and Science of Learning From Data by Alan Agresti and Christine Franklin Contents CHAPTER 9: HYPOTHESIS TESTS 9.1 Elements of a Hypothesis Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 Normal Hypothesis Test for Population Proportion p . . . . . . . . . . . . . . . . . . 9.3 The t-Test: Hypothesis Testing for Population Mean µ . . . . . . . . . . . . . . . . . 9.4 Possible Errors in Hypothesis Testing . . . . . . . . . . . . . . . . . . . . . . . . . . 9.5 Limitations and Common Misinterpretations of Hypothesis Testing . . . . . . . . . . 1 1 6 10 15 17 Stat 3011 Chapter 9 CHAPTER 9: HYPOTHESIS TESTS Motivating Example A diet pill company advertises that at least 75% of its customers lose 10 pounds or more within 2 weeks. You suspect the company of falsely advertising the benefits of taking their pills. Suppose you take a sample of 100 product users and find that only 5% have lost at least 10 pounds. Is this enough to prove your claim? What about if 72% had lost at least 10 pounds? Goal: 9.1 Elements of a Hypothesis Test 1. Assumptions 2. Hypotheses Each hypothesis test has two hypotheses about the population: Null Hypothesis (H0 ): Alternative Hypothesis (Ha ): 1 Stat 3011 Chapter 9 Diet Pill Example: Let p = true proportion of diet pill customers that lose at least 10 pounds. State the null and alternative hypotheses for the diet pill example. 3. Test Statistic Definition: Test Statistic...
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...Chapter 11: Testing a Claim Objectives: Students will: Explain the logic of significance testing. List and explain the differences between a null hypothesis and an alternative hypothesis. Discuss the meaning of statistical significance. Use the Inference Toolbox to conduct a large sample test for a population mean. Compare two-sided significance tests and confidence intervals when doing inference. Differentiate between statistical and practical “significance.” Explain, and distinguish between, two types of errors in hypothesis testing. Define and discuss the power of a test. AP Outline Fit: IV. Statistical Inference: Estimating population parameters and testing hypotheses (30%–40%) B. Tests of significance 1. Logic of significance testing, null and alternative hypotheses; P-values; one- and two-sided tests; concepts of Type I and Type II errors; concept of power 4. Test for a mean (large sample -- ( known) What you will learn: A. Significance Tests for µ (( known) 1. State the null and alternative hypotheses in a testing situation when the parameter in question is a population mean µ. 2. Explain in nontechnical language the meaning of the P-value when you are given the numerical value of P for a test. 3. Calculate the one-sample z-statistic and the P-value for both one-sided and two-sided tests about the mean µ of a Normal population. 4. Assess statistical significance at standard levels α by comparing...
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...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 of business MIS 301 contributes to these goals through its student learning outcomes…. • Use data from a sample to make inferences about a population. • Apply probability theory in decision making situations. • Formulate hypotheses for decision making and research. • Analyze data using appropriate statistical techniques...
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...Chapter 10: Comparing Two Groups Bivariate Analysis: Methods for comparing two groups are special cases of bivariate statistical methods – Two variables exist: Response variable – outcome variable on which comparisons are made Explanatory variable – binary variable that specifies the groups Statistical methods analyze how the outcome on the response variable depends on or is explained by the value of the explanatory variable Independent Samples: Most comparisons of groups use independent samples from the groups, The observations in one sample are independent of those in the other sample Example: Randomized experiments that randomly allocate subjects to two treatments Example: An observational study that separates subjects into groups according to their value for an explanatory variable Dependent samples: Dependent samples result when the data are matched pairs – each subject in one sample is matched with a subject in the other sample Example: set of married couples, the men being in one sample and the women in the other. Example: Each subject is observed at two times, so the two samples have the same subject Categorical response variable: For a categorical response variable - Inferences compare groups in terms of their population proportions in a particular category - We can compare the groups by the difference in their population proportions: (p1 – p2) Example: Experiment: Subjects were 22,071 male physicians Every other day for five years, study participants...
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...Demystified Chemistry Demystified College Algebra Demystified Earth Science Demystified Everyday Math Demystified Geometry Demystified Physics Demystified Physiology Demystified Pre-Algebra Demystified Project Management Demystified Statistics Demystified Trigonometry Demystified BUSINESS STATISTICS DEMYSTIFIED STEVEN M. KEMP, Ph.D SID KEMP, PMP McGRAW-HILL New York Chicago San Francisco Lisbon London Madrid Mexico City Milan New Delhi San Juan Seoul Singapore Sydney Toronto Copyright © 2004 by The McGraw-Hill Companies, Inc. All rights reserved. Manufactured in the United States of America. Except as permitted under the United States Copyright Act of 1976, no part of this publication may be reproduced or distributed in any form or by any means, or stored in a database or retrieval system, without the prior written permission of the publisher. 0-07-147107-3 The material in this eBook also appears in the print version of this title: 0-07-144024-0. All trademarks are trademarks of their respective owners. Rather than put a trademark symbol after every occurrence of a trademarked name, we use names in an editorial fashion only, and to the benefit of the trademark owner, with no intention of infringement of the trademark. Where such designations appear in this book, they have been printed with initial caps. McGraw-Hill eBooks are available at special quantity discounts to use as premiums and sales promotions, or for use in corporate training programs. For more information, please contact...
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...LOG OM 5300 Statistical Analysis for Business Decisions Syllabus Spring 2013 Section G01 (11477): T 1855-2135, 132 SSB Instructor: Dr. Alan C. Wheeler Office: ESH 230, 516-6136, awheel@umsl.edu Office hours: MW 1230-1330; T 1400-1500, 1745-1845; or by appointment Text: Statistics for Business and Economics, revised 11th edition, by David R. Anderson, Dennis J. Sweeney, and Thomas A. Williams, South-Western Calculator: hand-held with keys for natural logarithm, mean, and standard deviation Course Description: The role of statistical evidence in the formation of inference and in the selection of strategies in solving business problems is developed. Probability and probability distributions are studied as a basis of statistical inference. An introduction to multivariate analysis is provided, which includes analysis of variance and regression methods. Specifically, the course covers in order most of the material in the following chapters of the text: Chapter Topic 1 Data and Statistics 2 Descriptive Statistics: Tabular and Graphical Presentations 3 Descriptive Statistics: Numerical Measures 4 Introduction to Probability 5 Discrete Probability Distributions 6 Continuous Probability Distributions 7 Sampling and Sampling Distributions 8 Interval Estimation 9 Hypothesis Tests 10 Statistical Inference About Means and Proportions With Two Populations 11 Inferences About Population Variances 12 Tests of Goodness of Fit...
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...A SECOND COURSE IN STATISTICS REGRESSION ANALYISIS Seventh Edition William Mendenhall University of Florida Terry Sincich University of South Florida Prentice Hall Boston Columbus Indianapolis New York San Francisco Upper Saddle River Amsterdam Cape Town Dubai London Toronto Madrid Delhi Milan Mexico Munich City Sao Paris Paulo Montreal Sydney Hong Kong Seoul Singapore Taipei Tokyo Editor in Chief: Deirdre Lynch Acquisitions Editor: Marianne Stepanian Associate Content Editor: Dana Jones Bettez Senior Managing Editor: Karen Wernholm Associate Managing Editor: Tamela Ambush Senior Production Project Manager: Peggy McMahon Senior Design Supervisor: Andrea Nix Cover Design: Christina Gleason Interior Design: Tamara Newnam Marketing Manager: Alex Gay Marketing Assistant: Kathleen DeChavez Associate Media Producer: Jean Choe Senior Author Support/Technology Specialist: Joe Vetere Manufacturing Manager: Evelyn Beaton Senior Manufacturing Buyer: Carol Melville Production Coordination, Technical Illustrations, and Composition: Laserwords Maine Cover Photo Credit: Abstract green flow, ©Oriontrail/Shutterstock Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in this book, and Pearson was aware of a trademark claim, the designations have been printed in initial caps or all caps. Library of Congress Cataloging-in-Publication Data Mendenhall, William. A second course in...
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...INTRODUCTION Statistics deals with methods which help in estimating the characteristics of a population or making decisions concerning a population on the basis of the sample results. Sample and population are the two relative terms. A population is treated as universe and a sample is fraction or segment of the universe. Statistics describe the data and consists of the methods and technique used in the collection, organization, presentation and analysis of data in order to describe the various features and characteristics of such data. These can either be graphical or computational. In this, nothing can be inferred from the data nor can decision be made or conclusion drawn. (Akbhanj, 2013) Statistics are aggregate facts, a series relating...
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...Getting Started: The Anatomy and Physiology of Clinical Research Stephen B. Hulley, Thomas B. Newman, and Steven R. Cummings This chapter introduces clinical research from two viewpoints, setting up themes that run together through the book. One theme is the anatomy of research-what it's made of. This includes the tangible elements of the study plan: the research question, design, subjects, measurements, sample size calculation, and so forth. An investigator's goal is to create these elements in a form that will make the project fast, inexpensive, and easy. The other theme is the physiology of research-how it works. Studies are useful to the extent that they yield valid inferences, first about what happened in the study sample and then about generalizing these events to people outside. the study. The goal is to minimize the errors, random and systematic, that threaten conclusions based on these inferences. Separating these two themes is artificial in the same way that the anatomy of the human body does not make much sense without some understanding of its physiology. But the separation has the same advantage: It clarifies our thinking about a complex topic. . THE ANATOMY OF RESEARCH: WHAT IT'S MADE OF The structure of a research project is set out in its protocol, the written plan of the study. Protocols are well kn~wn as devices for seeking grant funds, but they also have a vital scientific function: helping the investigator to organize her research in a logical, focused...
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...Carol Melville Production Coordination: Lifland et al. Bookmakers Composition: Keying Ye Cover photo: Marjory Dressler/Dressler Photo-Graphics Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in this book, and Pearson was aware of a trademark claim, the designations have been printed in initial caps or all caps. Library of Congress Cataloging-in-Publication Data Probability & statistics for engineers & scientists/Ronald E. Walpole . . . [et al.] — 9th ed. p. cm. ISBN 978-0-321-62911-1 1. Engineering—Statistical methods. 2. Probabilities. I. Walpole, Ronald E. TA340.P738 2011 519.02’462–dc22 2010004857 Copyright c 2012, 2007, 2002 Pearson Education, Inc. All rights reserved. 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, or otherwise, without the...
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