1. 1-1 Chapter One McGraw-Hill/Irwin © 2005 The McGraw-Hill Companies, Inc., All Rights Reserved.

2. 1-2 Chapter One What is Statistics? GOALS When you have completed this chapter, you will be able to: ONE Understand why we study statistics. TWO Explain what is meant by descriptive statistics and inferential statistics. THREE Distinguish between a qualitative variable and a quantitative variable. FOUR Distinguish between a discrete variable and a continuous variable. FIVE Distinguish among the nominal, ordinal, interval, and ratio levels of measurement. SIX Define the terms mutually exclusive and exhaustive. Goals

3. 1-3 What is Meant by Statistics? Statistics is the science of collecting, organizing, presenting, analyzing, and interpreting numerical data to assist in making more effective decisions.

4. 1-4 Who Uses Statistics? Statistical techniques are used extensively by marketing, accounting, quality control, consumers, professional sports people, hospital administrators, educators, politicians, physicians, and many others.

5. 1-5 Types of Statistics EXAMPLE 2: According to Consumer Reports, General Electric washing machine owners reported 9 problems per 100 machines during 2001. The statistic 9 describes the number of problems out of every 100 machines. Descriptive Statistics Descriptive Statistics : Methods of organizing, summarizing, and presenting data in an informative way. EXAMPLE 1: A Gallup poll found that 49% of the people in a survey knew the name of the first book of the Bible. The statistic 49 describes the number out of every 100 persons who knew the answer.

6. 1-6 Types of Statistics Population Collection A Population is a Collection of all possible individuals, objects, or measurements of interest. Sample A Sample is a portion, or part, of the population of interest Inferential Statistics: Inferential Statistics: A decision, estimate, prediction, or generalization about a population, based on a sample.

7. 1-7 Types of Statistics (examples of inferential statistics) Example 2: Wine tasters sip a few drops of wine to make a decision with respect to all the wine waiting to be released for sale. Example 1: TV networks constantly monitor the popularity of their programs by hiring Nielsen and other organizations to sample the preferences of TV viewers. Example 3: The accounting department of a large firm will select a sample of the invoices to check for accuracy for all the invoices of the company.

8. 1-8 Types of Variables Qualitative Attribute Variable For a Qualitative or Attribute Variable the characteristic being studied is nonnumeric.

9. 1-9 Types of Variables Number of children in a family Quantitative Variable In a Quantitative Variable information is reported numerically. Balance in your checking account Minutes remaining in class

10. 1-10 Types of Variables Discrete Variables: Discrete Variables: can only assume certain values and there are usually “gaps” between values. Example: the number of bedrooms in a house, or the number of hammers sold at the local Home Depot (1,2,3,…,etc). DiscreteContinuous Quantitative variables can be classified as either Discrete or Continuous.

11. 1-11 Types of Variables The height of students in a class. Continuous Variable A Continuous Variable can assume any value within a specified range. The pressure in a tire The weight of a pork chop

12. 1-12 Summary of Types of Variables

13. 1-13 Levels of Measurement There are four levels of dataNominalOrdinalIntervalRatio

14. 1-14 Nominal data Nominal level Nominal level Data that is classified into categories and cannot be arranged in any particular order.

15. 1-15 Levels of Measurement Mutually exclusive An individual, object, or measurement is included in only one category. Nominal level variables must be: Exhaustive Each individual, object, or measurement must appear in one of the categories.

16. 1-16 Levels of Measurement During a taste test of 4 soft drinks, Coca Cola was ranked number 1, Dr. Pepper number 2, Pepsi number 3, and Root Beer number 4. Ordinal level Ordinal level : involves data arranged in some order, but the differences between data values cannot be determined or are meaningless.

17. 1-17 Business Statistics, 4e, by Ken Black. © 2003 John Wiley & Sons.175- Example of Ordinal Measurement

18. 1-18 Business Statistics, 4e, by Ken Black. © 2003 John Wiley & Sons.185- Ordinal Data Faculty and staff should receive preferential treatment for parking space. 12345 Strongly Agree Strongly Disagree Neutral

19. 1-19 Levels of Measurement Temperature on the Fahrenheit scale. Interval level Similar to the ordinal level, with the additional property that meaningful amounts of differences between data values can be determined. There is no natural zero point.

20. 1-20 Business Statistics, 4e, by Ken Black. © 2003 John Wiley & Sons.205- 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

21. 1-21 Levels of Measurement Ratio level: Ratio level: the interval level with an inherent zero starting point. Differences and ratios are meaningful for this level of measurement.

22. 1-22 Business Statistics, 4e, by Ken Black. © 2003 John Wiley & Sons.225- 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.

23. 1-23 Business Statistics, 4e, by Ken Black. © 2003 John Wiley & Sons.235- Usage Potential of Various Levels of Data Nominal Ordinal Interval Ratio

24. 1-24 Business Statistics, 4e, by Ken Black. © 2003 John Wiley & Sons.245- Data Level, Operations, and Statistical Methods Data Level Nominal Ordinal Interval Ratio 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 Parametric