© 2002 Thomson / South-Western Slide 1-1 Chapter 1 Introduction to Statistics with Excel

© 2002 Thomson / South-Western Slide 1-2 Learning Objectives Define statistics Become aware of a wide range of applications of statistics in business Differentiate between descriptive and inferential statistics Classify numbers by level of data and understand why doing so is important Become aware of the statistical analysis capabilities of Excel

© 2002 Thomson / South-Western Slide 1-3 What is Statistics? Science of gathering, analyzing, interpreting, and presenting data Branch of mathematics Course of study Facts and figures A death Measurement taken on a sample Type of distribution being used to analyze data

© 2002 Thomson / South-Western Slide 1-4 Population Versus Sample Population — the whole –a collection of persons, objects, or items under study Census — gathering data from the entire population Sample — a portion of the whole –a subset of the population

© 2002 Thomson / South-Western Slide 1-5 Population

© 2002 Thomson / South-Western Slide 1-6 Population and Census Data IdentifierColorMPG RD1Red12 RD2Red10 RD3Red13 RD4Red10 RD5Red13 BL1Blue27 BL2Blue24 GR1Gree n 35 GR2Gree n 35 GY1Gray15 GY2Gray18 GY3Gray17

© 2002 Thomson / South-Western Slide 1-7 Sample and Sample Data IdentifierColorMPG RD2Red10 RD5Red13 GR1Gree n 35 GY2Gray18

© 2002 Thomson / South-Western Slide 1-8 Descriptive vs. Inferential Statistics Descriptive Statistics — using data gathered on a group to describe or reach conclusions about that same group only Inferential Statistics — using sample data to reach conclusions about the population from which the sample was taken

© 2002 Thomson / South-Western Slide 1-9 Parameter vs. Statistic Parameter — descriptive measure of the population –Usually represented by Greek letters Statistic — descriptive measure of a sample –Usually represented by Roman letters

© 2002 Thomson / South-Western Slide 1-10 Symbols for Population Parameters

© 2002 Thomson / South-Western Slide 1-11 Symbols for Sample Statistics

© 2002 Thomson / South-Western Slide 1-12 Process of Inferential Statistics

© 2002 Thomson / South-Western Slide 1-13 Levels of Data Measurement Nominal - Lowest level of measurement Ordinal Interval Ratio - Highest level of measurement

© 2002 Thomson / South-Western Slide 1-14 Nominal Level Data Numbers are used to classify or categorize Example: Employment Classification –1 for Educator –2 for Construction Worker –3 for Manufacturing Worker Example: Ethnicity –1 for African-American –2 for Anglo-American –3 for Hispanic-American –4 for Oriental-American

© 2002 Thomson / South-Western Slide 1-15 Ordinal Level Data Numbers are used to indicate rank or order –Relative magnitude of numbers is meaningful –Differences between numbers are not comparable Example: Taste test ranking of three brands of soft drink Example: Position within an organization –1 for President –2 for Vice President –3 for Plant Manager –4 for Department Supervisor –5 for Employee

© 2002 Thomson / South-Western Slide 1-16 Example of Ordinal Measurement

© 2002 Thomson / South-Western Slide 1-17 Ordinal Data Faculty and staff should receive preferential treatment for parking space Strongly Agree Strongly Disagree Neutral

© 2002 Thomson / South-Western Slide 1-18 Interval Level Data Distances between consecutive integers are equal –Relative magnitude of numbers is meaningful –Differences between numbers are comparable –Location of origin, zero, is arbitrary –Vertical intercept of unit of measure transform function is not zero Examples: Fahrenheit Temperature, Calendar Time, Monetary Units

© 2002 Thomson / South-Western Slide 1-19 Ratio Level Data Highest level of measurement –Relative magnitude of numbers is meaningful –Differences between numbers are comparable –Location of origin, zero, is absolute (natural) –Vertical intercept of unit of measure transform function is zero Examples: Height, Weight, and Volume Monetary Variables, such as Revenues, and Expenses Financial ratios, such as P/E Ratio, Inventory Turnover

© 2002 Thomson / South-Western Slide 1-20 Usage Potential of Various Levels of Data Nominal Ordinal Interval Ratio

© 2002 Thomson / South-Western Slide 1-21 Data Level, Operations, and Statistical Methods Data Level Nominal Ordinal Interval Ratio Meaningful Operations Classifying and Counting All of above plus Ranking All of above plus Addition, Subtraction, Multiplication, and Division All of the above Statistical Methods Nonparametric Parametric

© 2002 Thomson / South-Western Slide 1-22 Qualitative vs Quantitative Data Qualitative Data is data of the nominal or ordinal level that classifies by a label or category. The labels may be numeric or nonnumeric. Quantitative Data is data of the interval or ratio level that measures on a naturally occurring numeric scale.

© 2002 Thomson / South-Western Slide 1-23 Discrete and Continuous Data Discrete Data is numeric data in which the values can come only from a list of specific values. Discrete data results from a counting process. Continuos Data is numeric data that can take on values at every point over a given interval. Continuous data result from a measuring process.

© 2002 Thomson / South-Western Slide 1-24 Summary of Data Classifications Data nalOrdinal Interl Ratio Qualitative (Categorical) Quantitative Nonnumeric Numeric Discrete Numeric Discrete or Continuous Data Nominal Ordinal IntervalRatio Quantitative Qualitative Numeric Discrete Nonnumeric Discrete or Continuous