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

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 2 2

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 8 11

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 9 12

Population © 2002 Thomson / South-Western 13

Population and Census Data Identifier Color MPG RD1 Red 12 RD2 10 RD3 13 RD4 RD5 BL1 Blue 27 BL2 24 GR1 Green 35 GR2 GY1 Gray 15 GY2 18 GY3 17 © 2002 Thomson / South-Western 11 14

Sample and Sample Data Identifier Color MPG RD2 Red 10 RD5 13 GR1 Green 35 GY2 Gray 18 © 2002 Thomson / South-Western 15

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 13 16

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 14 17

Symbols for Population Parameters © 2002 Thomson / South-Western 15 18

Symbols for Sample Statistics © 2002 Thomson / South-Western 16 19

Process of Inferential Statistics © 2002 Thomson / South-Western 17 20

Levels of Data Measurement Nominal - Lowest level of measurement Ordinal Interval Ratio - Highest level of measurement © 2002 Thomson / South-Western 18 21

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 19 22

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 20 23

Example of Ordinal Measurement © 2002 Thomson / South-Western 21 24

Ordinal Data Faculty and staff should receive preferential treatment for parking space. 1 2 3 4 5 Strongly Agree Disagree Neutral © 2002 Thomson / South-Western

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 22 26

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 23 27

Usage Potential of Various Levels of Data Ratio Interval Ordinal Nominal © 2002 Thomson / South-Western 24 28

Data Level, Operations, and Statistical Methods 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 25 29

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

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

Summary of Data Classifications Nominal nal Ordinal Ordinal Interval Interl Ratio Ratio Qualitative (Categorical) Qualitative Quantitative Quantitative Numeric Numeric Nonnumeric Nonnumeric Numeric Numeric Discrete or Continuous Discrete or Continuous Discrete Discrete © 2002 Thomson / South-Western