QTBD Introduction

QTBD Introduction - Transcript

Introduction to Statistics

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

Statistics in Business
Accounting auditing and cost estimation Economics regional national and international economic performance Finance investments and portfolio management Management human resources compensation and quality management Management Information Systems performance of systems which gather summarize and disseminate information to various managerial levels Marketing market analysis and consumer research International Business market and demographic analysis

What is Statistics
Science of gathering analyzing interpreting and presenting data Branch of mathematics Course of study Facts and figures Measurement taken on a sample Type of distribution being used to analyze data

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

Population

Population and Census Data
Identifier RD1 RD2 RD3 RD4 RD5 BL1 BL2 GR1 GR2 GY1 GY2 GY3 Color Red Red Red Red Red Blue Blue Green Green Gray Gray Gray MPG 12 10 13 10 13 27 24 35 35 15 18 17

Sample and Sample Data
Identifier RD2 Color Red MPG 10

RD5

Red

13

GR1

Green

35

GY2

Gray

18

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

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

Symbols for Population Parameters
denotes population param eter



2

denotes population variance

denotes population standard deviation

Symbols for Sample Statistics
x denotes sample mean

S

2

denotes sample variance

S denotes sample standard deviation

Process of Inferential Statistics
Calculate x to estimate

Population parameter

Sample x statistic

Select a random sample

Levels of Data Measurement



Nominal Lowest level of measurement Ordinal Interval Ratio Highest level of measurement

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

Ordinal Level Data
Numbers are used to indicate rank or order Relative magnitude of numbers is meaningful Differences between numbers are not comparable Example Example Example Ranking productivity of employees Taste test ranking of three brands of soft drink Position within an organization 1 for President 2 for Vice President 3 for Plant Manager 4 for Department Supervisor 5 for Employee

Example of Ordinal Measurement

1 6 2 4 3 5

f i n i s h

Ordinal Data
Faculty and staff should receive preferential treatment for parking space
Strongly Agree Agree Neutral Disagree Strongly Disagree

1

2

3

4

5

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

Example Fahrenheit Temperature Example Calendar Time Example Monetary Utility

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 Example Monetary Variables such as Profit and Loss Revenues and Expenses Example Financial ratios such as P E Ratio Inventory Turnover and Quick Ratio

Usage Potential of Various Levels of Data
Ratio Interval Ordinal Nominal

Data Level Operations and Statistical Methods
Data Level Nominal Ordinal Interval Meaningful Operations Classifying and Counting All of the above plus Ranking All of the above plus Addition Subtraction Multiplication and Division All of the above Statistical Methods Nonparametric Nonparametric Parametric

Ratio

Parametric