Free Essay

Basic Statistics

In: Business and Management

Submitted By feyza
Words 1299
Pages 6
law of large numbers 2.slide * Statistical concept that larger the sample population (or the number of observations) used in a test, the more accurate the predictions of the behavior of that sample, and smaller the expected deviation in comparisons of outcomes. * As a general principle it means that, in the long run, the average (mean) of a long series of observations may be taken as the best estimate of the 'true value' of a variable.
3.slide
* In other words, what is unpredictable and chancy in case of an individual is predictable and uniform in the case of a large group. * This law forms the basis for the expectation of probable-loss upon which insurance premium rates are computed. Also called law of averages.
Law of Large Numbers
Observe a random variable X very many times. In the long run, the proportion of outcomes taking any value gets close to the probability of that value. The Law of Large Numbers says that the average of the observed values gets close to the mean μ X of X.
4.slide ; Law of Large Numbers for Discrete Random Variables * The Law of Large Numbers, which is a theorem proved about the mathematical model of probability, shows that this model is consistent with the frequency interpretation of probability.

5.slide ; Chebyshev Inequality * To discuss the Law of Large Numbers, we first need an important inequality called the Chebyshev Inequality. * Chebyshev’s Inequality is a formula in probability theory that relates to the distribution of numbers in a set. * This formula is able to prove with little provided information the probability of outliers existing at a certain interval.

6.slide * Given X is a random variable, A stands for the mean of the set, K is the number of standard deviations, and Y is the value of the standard deviation, the formula reads as follows: * Pr(|X-A|=>KY)<=1/K2, Theabsolute value of the difference of X minus A is greater than or equal to the K times Y has the probability of less than or equal to one divided by K squared.
7.slide Statement of Chebyshev’s Inequality * Chebyshev’s inequality says that at least 1-1/K2 of data from a sample must fall within K standard deviations from the mean, where K is any positivereal number greater than one. We can also state the inequality above by replacing the phrase “data from a sample” with probability distribution. This is because Chebyshev’s inequality is a result from probability, which can then be applied to statistics.
8.slide Illustration of the Inequality
To illustrate the inequality, we will look at it for a few values of K: * For K = 2 we have 1 – 1/K2 = 1 - 1/4 = 3/4 = 75%. So Chebyshev’s inequality says that at least 75% of the data values of any distribution must be within two standard deviations of the mean. * For K = 3 we have 1 – 1/K2 = 1 - 1/9 = 8/9 = 89%. So Chebyshev’s inequality says that at least 89% of the data values of any distribution must be within three standard deviations of the mean.
9.slide The Long Run and the Expected Value
Random experiments and random variables have long-term regularities. The Law of Large Numbers says that in repeated independent trials, the relative frequency of each outcome of a random experiment tends to approach the probability of that outcome. That implies that the long-term average value of a discrete random variable in repeated experiments tends to approach a limit, called the expected value of the random variable.
10.slide
The expected value of a random variable depends only on the probability distribution of the random variable. The expected value has properties that can be exploited to find the expected value of some complicated random variables in terms of simpler ones. These properties allow us to find the expected value of the sample sum and sample mean of random draws with and without replacement from a box of numbered tickets.
11.slide :The Law of Large Numbers says that in repeated independent trials with probability p of success in each trial, the chance that the fraction of successes is close to p grows as the number of trials grows. More precisely, for any tolerance e>0,
P(| (fraction of successes in n trials) - p | < e) approaches 100% as the number n of trials grows. This expresses a long-term regularity of repeated independent trials with a shared probability of success.
12.slide: The Expected Value of the Sample Sum of n random Draws from a Box
If a box contains tickets labeled with numbers, the expected value of the sample sum of the labels on n tickets drawn at random with or without replacement from the box is n×(average of the labels on the tickets in the box), where (average of the labels on the tickets in the box) means the average of the list of numbers on all the labels, including repeated values as many times as they occur on different tickets.
(If the draws are without replacement, the number of draws cannot exceed the number of tickets in the box.)
13.slide Exercise A box contains 12 tickets labeled with numbers. The number on the tickets are
-9, -7, -6, -5, -4, -4, -3, -1, -1, 0, 5, 10 .
The expected value of the sample sum of the ticket labels in 11 independent random draws with replacement from the box is ??
[-Solution]
The expected value of the sum of the numbers on the tickets in n draws with replacement from a box of numbered tickets is n×(average of the numbers on the tickets). The average of the numbers on the tickets in this box is -2.08 and the number n of draws is 11, so the expected value is
(11)×(-2.08) = -22.92.
14.slide:Expected Value of the Sample Mean and Sample Percentage
The sample mean is the sample sum divided by the sample size, n. Dividing by the sample size is the same as multiplying by its reciprocal, 1/n, so we can use the properties of expectation to find the expected value of the sample mean from the expected value of the sample sum: The expected value of the sample mean is the expected value of the sample sum, times 1/n. Because the expected value of the sample sum is n×(average of the numbers on all the tickets in box), whether the sampling is with or without replacement, the expected value of the sample mean is the average of the numbers on all the tickets in the box, for random sampling with or without replacement.
15.slide Summary * In repeated independent trials with the same probability of success, as the number of trials increases, the fraction of successes is increasingly likely to be close to the probability of success in each trial. This is the Law of Large Numbers. * As a consequence of the Law of Large Numbers, if a discrete random variable is observed repeatedly in independent experiments, the fraction of experiments in which the random variable equals any of its possible values is increasingly likely to be close to the probability that the random variable equals that value. * The mean of the observed values of the random variable in repeated independent experiments is thus increasingly likely to be close to a weighted average of the possible values, where the weights are the probabilities of the values * That weighted average is called the expected value of the random variable. The expected value of a random variable depends only on the probability distribution of the random variable, so we can speak interchangeably of the expected value of a random variable or of its probability distribution.…...

Similar Documents

Free Essay

Statistic

...which will occur. Then, the possible outcomes of a random experiment are called the basic outcomes, and the set of all basic outcomes is called the sample space, S. Examples of random experiment (i) Tossing a coin Basic outcomes: {H}, {T } Sample space, S = {H, T } (ii) Rolling a dice Basic outcomes: {1}, {2}, {3}, {4}, {5}, {6} Sample space, S = {1, 2, 3, 4, 5, 6} (iii) Daily changes in Hang Sang Index Basic outcomes: {up}, {down}, {no change} Sample space, S = {up, down, no change} Definition. An event is a set of basic outcomes, or a collection of some basic outcomes from the sample space, and it is said to occur if the random experiment gives rise to one of its constituent basic outcomes. Example 2.1. In a random experiment of throwing two dices, we are interested in the sum of the numbers turned up by the two dices. Basic outcomes: {(1, 1)}, {(1, 2)}, . . ., {(6, 5)}, {(6, 6)} HKU STAT1603 (2012-13, Semester 1) 1 STAT1603 Introductory Statistics Sample space, S = {(1, 1), (1, 2), . . . , (6, 5), (6, 6)} Suppose that we are interested in 2. Probability (i) A = Event in which the sum is even. Then, A contains the combinations of numbers whose sums equal 2, 4, 6, 8, 10 or 12. (ii) B = Event in which the sum is odd. Then, B contains the combinations of numbers whose sums equal 3, 5, 7, 9 or 11. (iii) C = Event in which the sum is 8. Then, C = {(2, 6), (3, 5), (4, 4), (5, 3), (6, 2)}. Basic Set Operations (i) Belongs to x 2 A means that object (element) x is a member......

Words: 1155 - Pages: 5

Premium Essay

Statistics

...integrates applied business research and descriptive statistics. Students will learn to apply business research and descriptive statistics in making better business decisions. Other topics include examination of the role of statistics in research, statistical terminology, the appropriate use of statistical techniques, and interpretation of statistical findings in business and research. Policies Faculty and students will be held responsible for understanding and adhering to all policies contained within the following two documents: • University policies: You must be logged into the student website to view this document. • Instructor policies: This document is posted in the Course Materials forum. University policies are subject to change. Be sure to read the policies at the beginning of each class. Policies may be slightly different depending on the modality in which you attend class. If you have recently changed modalities, read the policies governing your current class modality. Course Materials Lind, D. A., Marchal, W. G., & Wathen, S. A. (2011). Basic statistics for business and economics (7th ed.). New York, NY: McGraw-Hill/Irwin. McClave, J. T., Benson, P. G., & Sincich, T. (2011). Statistics for business and economics (11th ed.). Boston, MA: Pearson-Prentice Hall. All electronic materials are available on the student website. |Week One: Understanding the Meaning of Statistics ......

Words: 3466 - Pages: 14

Free Essay

The Basic Counting Principle-Statistic for Management Notes

...The Basic Counting Principle When there are m ways to do one thing,  and n ways to do another,  then there are m×n ways of doing both. 123 132 213 231 321 312 These are the possible three digit no.s 3 2 3 1 1 2 2 3 1 3 2 1 In how many ways can three digit number be formed from the numbers 1,2,3 without repeating the digits within the numbers. 1 2 3 3 options * 2 options * 1option=6 ways Find out the possible number of arrangements below: S1 T1 S2 T2 S3 Here n is 3, r is 2 For principle of counting we must have same number of options. In above if S3 and T2 is not allowed, principle of counting doesn’t work If n=5 and r=3 {A,B,C,D,E} How many different ways can we arrange of taking 3 letters at a time? 5 *4*3= 60 ways This is permutation of n different thing taken r at a time 60=(5*4*3*2*1)/(2*1) = 5!/2!=5!(5-3)!=n!/(n-r)! We are talking about linear arrangement not the circular one here nPr= filling r places by n different thing n=5 {A,B,C,D,E} r=3 {A,B,C}, {A,B,D}, {A,C,D}, {A,C,E}………….. [Note: Arrangement is related to permutation. If we are considered about place or position it is permutation question. Selecting is related to permutation. If we are not considered about place or position it is combination question.] nCr,......

Words: 399 - Pages: 2

Premium Essay

Statistics

...Series—08-02 | January 2008 Pin Ng, Ph.D. Associate Professor James Pinto, Ph.D. Professor Susan K. Williams, Ph.D. Associate Professor All professors at: Northern Arizona University The W. A. Franke College of Business PO Box 15066 Flagstaff, AZ 86011.5066   The Effect of Learning Styles on Course Performance: A Quantile Regression Analysis Introduction Students have different learning styles. “They preferentially focus on different types of information, tend to operate on perceived information in different ways, and achieve understanding at different rates” (Felder, 1993, p. 286). In this study, we investigated the relationship between student performance and learning styles for students enrolled in a basic business statistics course. This course was recently redesigned in order to facilitate learning for students of all learning styles. A learner-centered approach that incorporated multiple teaching styles such as student responsibility via mastery and co-operative learning that used teams in several course components was adopted. In addition, the focus of the redesigned course was on interpretation and implications of statistical results instead of the mechanics of computation (Lockwood, Ng, & Pinto, 2007). To assess our success in designing a course that facilitates learning for all learning styles, we evaluated the impact of students’ learning style on their performance in the course. “Students whose learning styles are compatible with the......

Words: 5123 - Pages: 21

Free Essay

Basic Statistics

...Module 1 - SLP Introduction to Probability MAT201 - Basic Statistics Quantitative data gathered on the amount of time spent using the computer on a daily basis. The procedure and data was collected for ten (10) days: 1. Time logged onto the computer. 2. Time logged off the computer. 3. Total number of hours logged onto the computer per use 4. Total use time to get a daily grand total. 5. Grand total for each day. RESULTS: DAY ONE: TOTAL HOURS = 2 HRS 9:00 AM – 11:00 AM DAY TWO: TOTAL HOURS = 1HR 10:00 AM – 11:00 AM DAY THREE: TOTAL HOURS = 1 HR 11:00 AM – 12:00 PM 1HR DAY FOUR: TOTAL HOURS = 4 HR 9:30 AM – 11:30 AM 2:00 PM – 4:00 PM DAY FIVE: TOTAL HOURS = 1 HR 1:00 PM – 2:00 PM DAY SIX: TOTAL HOURS = 3HR 10:00 AM – 1:00 PM DAY SEVEN: TOTAL HOURS = 2HR 1:00 PM – 3:00 PM DAY EIGHT: TOTAL HOURS = 3HR 11:00 AM – 12:00 PM 8:00 PM – 10:00 PM DAY NINE: TOTAL HOURS = 1HR 10:00 AM – 11:00 AM DAY TEN: TOTAL HOURS = 2HRS 9:30 AM – 11:30AM The data shows that I averaged ( 2 ) hours on the computer per day, 20.00 hrs for ten days. Probability is a measure of how likely an event will occur. The proposition of interest is usually of the form "Will a specific event occur?" The attitude of mind is of the form "How certain are we that the event will occur?" The certainty we adopt can be described in terms of a numerical measure and this number, between 0 and 1 (where 0 indicates......

Words: 320 - Pages: 2

Premium Essay

Statistics

...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 statistics :......

Words: 63698 - Pages: 255

Premium Essay

Basic Concepts in Statistics

...University of Phoenix Material Basic Concepts in Statistics Complete the following questions. Be specific and provide examples when relevant. Cite any sources consistent with APA guidelines. Question | Answer | What are statistics and how are they used in the behavioral sciences? Your answer should be 100 to 200 words. | According to FSU (2010), Statistics are a mathematical science that involves the application of quantitative principles for the collections, analyses, as well as presentations of numerical data. In addition, statistics uses data from a few populations so that they could describe it meaningfully, in order to draw a conclusion from it, to make an informed decision. From the behavioral science stand point statistics is a tool that are used to discover systematic signs within asset of data, as well as guides decision making. Statistics are also used in the behavioral sciences for descriptive, inferential correlation, regression analysis of variance as well as nonparametric statistics (B, Michael, 2010).. | Differentiate between descriptive and inferential statistics. What information do they provide? What are their similarities and differences? Your answer should be 250 to 400 words. | According to Whitaker Steven (2013), statistics is a lot of math concentrating on the data, organizations, along with interpretations of a bunch of numbers (Aron, Aron, & Coups, 2009). However, within researches statistics are important for researchers so......

Words: 1081 - Pages: 5

Premium Essay

Statistics

...sier!™ ing Everything Ea Mak ta t i s t i c s S e nt 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......

Words: 31557 - Pages: 127

Premium Essay

Statistics

...Basics of Statistics Jarkko Isotalo 30 20 10 Std. Dev = 486.32 Mean = 3553.8 N = 120.00 0 2400.0 2800.0 2600.0 3200.0 3000.0 3600.0 3400.0 4000.0 3800.0 4400.0 4200.0 4800.0 4600.0 5000.0 Birthweights of children during years 1965-69 Time to Accelerate from 0 to 60 mph (sec) 30 20 10 0 0 Horsepower 100 200 300 1 Preface These lecture notes have been used at Basics of Statistics course held in University of Tampere, Finland. These notes are heavily based on the following books. Agresti, A. & Finlay, B., Statistical Methods for the Social Sciences, 3th Edition. Prentice Hall, 1997. Anderson, T. W. & Sclove, S. L., Introductory Statistical Analysis. Houghton Mifflin Company, 1974. Clarke, G.M. & Cooke, D., A Basic course in Statistics. Arnold, 1998. Electronic Statistics Textbook, http://www.statsoftinc.com/textbook/stathome.html. Freund, J.E.,Modern elementary statistics. Prentice-Hall, 2001. Johnson, R.A. & Bhattacharyya, G.K., Statistics: Principles and Methods, 2nd Edition. Wiley, 1992. Leppälä, R., Ohjeita tilastollisen tutkimuksen toteuttamiseksi SPSS for Windows -ohjelmiston avulla, Tampereen yliopisto, Matematiikan, tilastotieteen ja filosofian laitos, B53, 2000. Moore, D., The Basic Practice of Statistics. Freeman, 1997. Moore, D. & McCabe G., Introduction to the Practice of Statistics, 3th Edition. Freeman, 1998. Newbold, P., Statistics for Business and......

Words: 14125 - Pages: 57

Free Essay

Statistics

...Welcome to… π ∑θ Business statistics (MA-205) 1 Business Statistics π ∑θ Why study Business Statistics? • To become a better consumer of other people’s data • To facilitate communication • To improve computer skills • To overcome either too little or too much information • To develop technical literacy • To improve career mobility 2 Business Statistics π ∑θ Lecturer: Business Statistics Ammara 3 Business Statistics π ∑θ Lecture Overheads and Textbook Lecture overheads: posted on the Business Statistics Group Group name: business_statistics_bba Group home page: http://groups.yahoo.com/group/business_statistics_bba Group email: business_statistics_bba@yahoogroups.com before the week in which lectures are given. Textbook: Richard I. Lavin and David S. Rubin, “Statistics for Management”, Prentice Hall, New York,7th edition(2000) 4 Business Statistics π ∑θ • • • • Assessment Final Examination: Midterm Examination: Quizzes/Homework/Term Project: Total: 45% 35% 20% 100% 5 Business Statistics Regular work and study is the key to success π ∑θ • It can never be emphasised strongly enough how true this is for Business Statistics • This course rewards those students who put constant effort into it over the semester 6 Business Statistics π ∑θ How to do well in the class Statistics is a problem-solving subject. Practice in problem solving, and completing and understanding the assigned reading......

Words: 865 - Pages: 4

Premium Essay

Statistics

...Chapter 22 Correlation Coefficients 22 Correlation Coefficients The Meaning of Correlation Correlation and Data Types Pearson’s r Spearman rho Other Coefficients of Note Coefficient of Determination r2 The concept of correlation was introduced in Chapters 1 and 5. Our focus since Chapter 16 has been basic statistical procedures that measure differences between groups -- one-sample, two-sample, and k-sample tests. Now we turn our attention to basic statistical procedures that measure the degree of association between variables. Dr. Wesley Black studied the relationship between rankings of selected learning objectives in a youth discipleship taxonomy between full-time church staff youth ministers and seminary students enrolled in youth education courses at Southwestern Seminary.1 Questionnaires were returned by 318 students and 184 youth ministers.2 Ten objectives in each of five categories (Personal Ministry, Christian Theology and Baptist Doctrine, Christian Ethics, Baptist Heritage, and Church Polity and Organization) were ranked by these two groups. The basic question raised by Black in this study was whether students prioritized discipleship training objectives for youth in the same way as full-time ministers in the field. Using the Spearman rho correlation coefficient, Black found the correlations of rankings generated by students and ministers of the ten items for each of five categories were as follows: Personal Ministry, 0.915; Christian......

Words: 3527 - Pages: 15

Premium Essay

Introduction to Basic Business Statistics

...DIPLOMA IN BUSINESS ADMINISTRATION | INTRODUCTION TO BASIC BUSINESS STATISTICS | | Table of Content No | Description | Pages | 1 | Introduction / Objective | 3 | 2 | Methodology | 4 | 3 | Analysis of finding | 5 | 4 | Conclusion | 13 | 5 | Recommendation | 14 | | Introduction ZC International Pte Ltd manufacture soaps, detergent and household products. Industry vision: “We benefit society by contributing to the sustainable improvement of the quality and comfort of life through hygiene and cleanliness in a free, competitive and innovative way”. Our mission is to communicate the value embodied in our industry’s vision and any related policies to all appropriate stakeholders effectively and objectively, while taking these stakeholders’s view into account. It does this by: - working with other organization as appropriate, ensuring stakeholder dialogue; and - to continue environmental progress in the design and marketing of product of package for household laundry detergent. Objective Data were collected by market survey and conducted by Nelso PL and consolidate with energy consumption date provided. The result of the survey can be found in Appendix 1. Methodology A set of value was collected using Personal interviews from adults who are working in Singapore. 30 candidates were selected using the random and convenience sampling methods. Out of the candidates 33.33% were males and 66.67% were females. Age of Candidates ......

Words: 1681 - Pages: 7

Premium Essay

Statistics

... Introductory STATISTICS 9TH EDITION This page intentionally left blank Introductory STATISTICS 9TH EDITION Neil A. Weiss, Ph.D. School of Mathematical and Statistical Sciences Arizona State University Biographies by Carol A. Weiss Addison-Wesley Boston Columbus Indianapolis New York San Francisco Upper Saddle River Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal Toronto Delhi Mexico City Sao Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo On the cover: Hummingbirds are known for their speed, agility, and beauty. They range in size from the smallest birds on earth to several quite large species—in length from 2 to 8.5 inches and in weight from 0.06 to 0.7 ounce. Hummingbirds flap their wings from 12 to 90 times per second (depending on the species) and are the only birds able to fly backwards. Normal flight speed for hummingbirds is 25 to 30 mph, but they can dive at speeds of around 60 mph. Cover photograph: Hummingbird, Editor in Chief: Deirdre Lynch Acquisitions Editor: Marianne Stepanian Senior Content Editor: Joanne Dill Associate Content Editors: Leah Goldberg, Dana Jones Bettez Senior Managing Editor: Karen Wernholm Associate Managing Editor: Tamela Ambush Senior Production Project Manager: Sheila Spinney Senior Designer: Barbara T. Atkinson Digital Assets Manager: Marianne Groth Senior Media Producer: Christine Stavrou Software Development: Edward Chappell, Marty Wright C iDesign/Shutterstock Marketing Manager: Alex Gay Marketing......

Words: 377092 - Pages: 1509

Free Essay

Statistics

...Annex A Basic Analysis | | Reciprocating | Scroll | All | Average Price | Europe | $ 31,31 | $ 38,60 | $ 32,28 |   | Latin | $ 38,71 | Does not exist | $ 38,71 |   | North | $ 32,43 | $ 34,69 | $ 33,11 |   | Total | $ 33,73 | $ 35,67 | $ 34,08 | Average Volume | Europe | 111.307,69 | 88.000,00 | 108.200,00 |   | Latin | 67.000,00 | Does not exist | 67.000,00 |   | North | 121.142,86 | 174.500,00 | 137.150,00 |   | Total | 103.054,05 | 152.875,00 | 111.911,11 | Average BTU | Europe | 410,00 | 850,00 | 468,67 |   | Latin | 482,50 | Does not exist | 482,50 |   | North | 523,21 | 893,33 | 634,25 |   | Total | 472,43 | 882,50 | 545,33 | Average Efficiency | Europe | 4,49 | 4,77 | 4,53 |   | Latin | 4,94 | Does not exist | 4,94 |   | North | 3,93 | 3,35 | 3,76 |   | Total | 4,40 | 3,71 | 4,28 | Total parts | Europe | 13,00 | 2,00 | 15,00 |   | Latin | 10,00 | Does not exist | 10,00 |   | North | 14,00 | 6,00 | 20,00 |   | Total | 37,00 | 8,00 | 45,00 | Annex B1 Correlation Matrix   | Price | Capacity | Weight | EER* | Unit | Latin | North | Europe | Reciprocating | Scroll | Price | 1 | | | | | | | | |   |   |   | | | | | | | | |   | Capacity | 0,323 | 1 | | | | | | | |   |   | 0,03 |   | | | | | | | |   | Weight | 0,319 |...

Words: 1186 - Pages: 5

Premium Essay

Statistics

...Descriptive and Inferential Statistics ________________________________________ Statistics can be broken into two basic types. The first is known as descriptive statistics. This is a set of methods to describe data that we have collected. Ex. Of 350 randomly selected people in the town of Luserna, Italy, 280 people had the last name Nicolussi. An example of descriptive statistics is the following statement : "80% of these people have the last name Nicolussi." Ex. On the last 3 Sundays, Henry D. Carsalesman sold 2, 1, and 0 new cars respectively. An example of descriptive statistics is the following statement : "Henry averaged 1 new car sold for the last 3 Sundays." These are both descriptive statements because they can actually be verified from the information provided. The second type of statistics in inferential statistics. This is a set of methods used to make a generalization, estimate, prediction or decision. Ex. Of 350 randomly selected people in the town of Luserna, Italy, 280 people had the last name Nicolussi. An example of inferential statistics is the following statement : "80% of all people living in Italy have the last name Nicolussi." We have no information about all people living in Italy, just about the 350 living in Luserna. We have taken that information and generalized it to talk about all people living in Italy. The easiest way to tell that this statement is not descriptive is by trying to verify it based upon the information provided. Ex....

Words: 2703 - Pages: 11