BIA 2610 – Statistical Methods Chapter 1 – Data and Statistics.

Slides:



Advertisements
Similar presentations
1 Slide AQA - Business Statistics, Quantitative Analysis Peter Matthews FDA B&M
Advertisements

Introduction to Statistics
Sections 1.3 Types of Data.
An Introduction to Business Statistics
Chapter 1 A First Look at Statistics and Data Collection.
Statistics for Managers Using Microsoft® Excel 5th Edition
East Los Angeles College Math 227 – Statistics Fall 2008
1/71 Statistics Data 2/71 Contents Applications in Business and Economics Data Data Sources Descriptive Statistics Statistical Inference Computers and.
1 1 Slide © 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole.
I need help! Applications in Business and Economics Data Data Sources Descriptive Statistics Statistical Inference Computers & Statistical Analysis.
QMS 6351 Statistics and Research Methods Chapter 2 Descriptive Statistics: Tabular and Graphical Methods Prof. Vera Adamchik.
1 Pertemuan 01 Pendahuluan Matakuliah: I0272 – Statistik Probabilitas Tahun: 2005 Versi: Revisi.
1 1 Slide © 2001 South-Western /Thomson Learning  Anderson  Sweeney  Williams Anderson  Sweeney  Williams  Slides Prepared by JOHN LOUCKS  CONTEMPORARYBUSINESSSTATISTICS.
1 1 Slide © 2006 Thomson/South-Western Chapter 1 Data and Statistics I need help! Applications in Business and Economics Data Data Sources Descriptive.
Welcome to QM Business Statistics. Course Objectives: Again 1.To gain an understanding of descriptive statistics, probability, sampling, interval.
Chapter One An Introduction to Business Statistics McGraw-Hill/Irwin Copyright © 2004 by The McGraw-Hill Companies, Inc. All rights reserved.
Chapter 1 Data and Statistics
1 1 Slide IS 310 – Business Statistics IS 310 Business Statistics CSU Long Beach.
MGS 9920 Data and Statistics.
Statistics - Descriptive statistics 2013/09/23. Data and statistics Statistics is the art of collecting, analyzing, presenting, and interpreting data.
STA 2023 Chapter 1 Notes. Terminology  Data: consists of information coming from observations, counts, measurements, or responses.  Statistics: the.
Basic Business Statistics, 11e © 2009 Prentice-Hall, Inc. Chap 1-1 Chapter 1 Introduction and Data Collection Basic Business Statistics 11 th Edition.
© 2016 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license.
Census A survey to collect data on the entire population.   Data The facts and figures collected, analyzed, and summarized for presentation and.
Econ 3790: Business and Economics Statistics Instructor: Yogesh Uppal
BUSINESS STATISTICS BQT 173
1 1 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole.
1 1 Slide © 2008 Thomson South-Western. All Rights Reserved Slides by JOHN LOUCKS St. Edward’s University.
Copyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Chapter 1 An Introduction to Business Statistics.
Statistics Ⅰ Teacher :刘 伟 公共经济管理学院 统计系 : Office : 2507.
Chapter 1 Data and Statistics I need help! Applications in Economics Data Data Sources Descriptive Statistics Statistical Inference Computers and Statistical.
BUSINESS STATISTICS BQT 173. CHAPTER 1 : DATA & STATISTICS.
© 2006 by Thomson Learning, a division of Thomson Asia Pte Ltd.. 1 Slide Slide Slides Prepared by Juei-Chao Chen Fu Jen Catholic University Slides Prepared.
Sections 1-3 Types of Data. PARAMETERS AND STATISTICS Parameter: a numerical measurement describing some characteristic of a population. Statistic: a.
1 1 Slide 統計學 Fall 2003 授課教師:統計系余清祥 日期: 2003 年 9 月 16 日 第一週:什麼是統計?
Slides by John Loucks St. Edward’s University. Statistics n The term statistics can refer to numerical facts such as averages, medians, percents, and.
1 1 Slide Data and Data Sets n Data are the facts and figures collected, analyzed, and summarized for presentation and interpretation. and summarized.
MADAM SITI AISYAH BINTI ZAKARIA INSTITUT MATEMATIK KEJURUTERAAN UNIVERSITI MALAYSIA PERLIS.
ECON 3790 Statistics for Business and Economics
©The McGraw-Hill Companies, Inc. 2008McGraw-Hill/Irwin What is Statistics? Chapter 1.
1 1 Slide Tuesday August 28 Class 2 Text problems for August 30: Chapter 2 - 2,6 & 10 Aplia Graded Assignment: “Introduction” due September 4, 9:00 am.
1 1 Slide © 2008 Thomson South-Western. All Rights Reserved Slides by JOHN LOUCKS St. Edward’s University.
1 1 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole.
Lecturer: DAO MINH ANH Faculty of Business and Administration Foreign Trade University
© 2016 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license.
Chapter 1 Data and Statistics Applications in Business and Economics Data Data Sources Descriptive Statistics Statistical Inference.
MATH Elementary Statistics. Salary – Company A.
1 1 Slide Chapter 1 Data and Statistics n Applications in Business and Economics n Data n Data Sources n Descriptive Statistics n Statistical Inference.
What Is Statistics Chapter 01 McGraw-Hill/Irwin Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved.
Chapter 2, Part A Descriptive Statistics: Tabular and Graphical Presentations n Summarizing Categorical Data n Summarizing Quantitative Data Categorical.
1 1 Slide © 2002 South-Western /Thomson Learning.
Lecture 1 Stat Applications, Types of Data And Statistical Inference.
Overview and Types of Data
Introduction to Statistics Chapter 1. § 1.1 An Overview of Statistics.
1 1 Slide STATISTICS FOR BUSINESS AND ECONOMICS Seventh Edition AndersonSweeneyWilliams Slides Prepared by John Loucks © 1999 ITP/South-Western College.
Introduction To Statistics
1.  The practice or science of collecting and analyzing numerical data in large quantities, especially for the purpose of inferring* proportions in a.
1 1 Slide Slides Prepared by JOHN S. LOUCKS St. Edward’s University © 2002 South-Western /Thomson Learning.
Biostatistics Introduction Article for Review.
Chapter 1 Introduction to Statistics 1-1 Overview 1-2 Types of Data 1-3 Critical Thinking 1-4 Design of Experiments.
1 1 Slide © 2008 Thomson South-Western. All Rights Reserved Data and Statistics Data and Statistics I need help! n Applications in Business and Economics.
© 2017 Cengage Learning. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted.
Essentials of Modern Business Statistics (7e)
Introduction to Statistics
Statistics Introduction to Data.
© 2017 Cengage Learning. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted.
St. Edward’s University
Introduction and Data Collection
Essentials of Statistics for Business and Economics (8e)
Chapter 1 Data and Statistics
Presentation transcript:

BIA 2610 – Statistical Methods Chapter 1 – Data and Statistics

Chapter 1 Data and Statistics n Data n Data Sources n Descriptive Statistics n Statistical Inference n Statistical Analysis Using Microsoft Excel n Applications in Business and Economics n Statistics

n The term statistics can refer to numerical facts such as averages, medians, percents, and index numbers that help us understand a variety of business and economic situations. n Statistics can also refer to the art and science of collecting, analyzing, presenting, and interpreting data. Statistics

n Economics Public accounting firms use statistical sampling procedures when conducting audits for their clients. Economists use statistical information in making forecasts about the future of the economy or some aspect of it. Financial advisors use price-earnings ratios and dividend yields to guide their investment advice. Finance n Accounting Applications in Business and Economics

A variety of statistical quality control charts are used to monitor the output of a production process. n Production Electronic point-of-sale scanners at retail checkout counters are used to collect data for a variety of marketing research applications. n Marketing A variety of statistical information helps administrators assess the performance of computer networks. n Information Systems Applications in Business and Economics

Data, Data Sets, Elements, Variables, and Observations All the data collected in a particular study are referred to as the data set for the study. Data are the facts and figures collected, analyzed, and summarized for presentation and interpretation. Elements are the entities on which data are collected. A variable is a characteristic of interest for the elements. The set of measurements obtained for a particular element is called an observation. The total number of data values in a complete data set is the number of elements multiplied by the number of variables. A data set with n elements contains n observations.

Scales of Measurement The scale indicates the data summarization and statistical analyses that are most appropriate. The scale determines the amount of information contained in the data. Scales of measurement include: Nominal Ordinal Interval Ratio

Scales of Measurement Nominal A nonnumeric label or numeric code may be used. Data are labels or names used to identify an attribute of the element. Example: Students of a university are classified by the school in which they are enrolled using a nonnumeric label such as Business, Humanities, Education, and so on (or numeric codes could be used).

Scales of Measurement Ordinal A nonnumeric label or numeric code may be used. The data have the properties of nominal data and the order or rank of the data is meaningful. Example: Students of a university are classified by their class standing using a nonnumeric label such as Freshman, Sophomore, Junior, or Senior (or numeric codes could be used).

Scales of Measurement Interval Interval data are always numeric. The data have the properties of ordinal data, and the interval between observations is expressed in terms of a fixed unit of measure. Example: Melissa has an SAT score of 1985, while Kevin has an SAT score of Melissa scored 105 points more than Kevin.

Scales of Measurement Ratio The data have all the properties of interval data and the ratio of two values is meaningful. Variables such as distance, height, weight, and time use the ratio scale. This scale must contain a zero value that indicates that nothing exists for the variable at the zero point.

Scales of Measurement Ratio Example: Melissa’s college record shows 36 credit hours earned, while Kevin’s record shows 72 credit hours earned. Kevin has twice as many credit hours earned as Melissa.

Categorical and Quantitative Data Data can be further classified as being categorical or quantitative. The statistical analysis that is appropriate depends on whether the data for the variable are categorical or quantitative. In general, there are more alternatives for statistical analysis when the data are quantitative.

Categorical Data Labels or names used to identify an attribute of each element Often referred to as qualitative data Use either the nominal or ordinal scale of measurement Can be either numeric or nonnumeric Appropriate statistical analyses are rather limited

Quantitative Data Quantitative data indicate how many or how much: discrete, if measuring how many continuous, if measuring how much Quantitative data are always numeric. Ordinary arithmetic operations are meaningful for quantitative data.

Scales of Measurement Categorical Quantitative Numeric Non-numeric Data Nominal Ordinal Nominal Ordinal Interval Ratio

Cross-Sectional Data Cross-sectional data are collected at the same or approximately the same point in time. Example: data detailing the number of building permits issued in November 2013 in each of the counties of Ohio

Time Series Data Time series data are collected over several time periods. Example: data detailing the number of building permits issued in Lucas County, Ohio in each of the last 36 months Graphs of time series help analysts understand what happened in the past, identify any trends over time, and project future levels for the time series

Descriptive Statistics Most of the statistical information in newspapers, magazines, company reports, and other publications consists of data that are summarized and presented in a form that is easy to understand. Such summaries of data, which may be tabular, graphical, or numerical, are referred to as descriptive statistics.

Example: Hudson Auto Repair The manager of Hudson Auto would like to have a better understanding of the cost of parts used in the engine tune-ups performed in her shop. She examines 50 customer invoices for tune-ups. The costs of parts, rounded to the nearest dollar, are listed on the next slide.

Example: Hudson Auto Repair n Sample of Parts Cost ($) for 50 Tune-ups

Tabular Summary: Frequency and Percent Frequency (2/50)100 Parts Cost ($) Frequency Percent Frequency n Example: Hudson Auto

Graphical Summary: Histogram Parts Cost ($) Frequency Tune-up Parts Cost n Example: Hudson Auto

Numerical Descriptive Statistics Hudson’s average cost of parts, based on the 50 tune-ups studied, is $79 (found by summing the 50 cost values and then dividing by 50). The most common numerical descriptive statistic is the average (or mean). The average demonstrates a measure of the central tendency, or central location, of the data for a variable.

Statistical Inference Population Sample Statistical inference Census Sample survey - the set of all elements of interest in a particular study - a subset of the population - the process of using data obtained from a sample to make estimates and test hypotheses about the characteristics of a population - collecting data for the entire population - collecting data for a sample

Process of Statistical Inference 1. Population consists of all tune- ups. Average cost of parts is unknown. 2. A sample of 50 engine tune-ups is examined. 3. The sample data provide a sample average parts cost of $79 per tune-up. 4. The sample average is used to estimate the population average.

End of Chapter 1