Chapter 01 mis
- 1. ©The McGraw-Hill Companies, Inc. 2008McGraw-Hill/Irwin What is Statistics Chapter 1
- 2. 2 GOALS Understand why we study statistics. Explain what is meant by descriptive statistics and inferential statistics. Distinguish between a qualitative variable and a quantitative variable. Describe how a discrete variable is different from a continuous variable. Distinguish among the nominal, ordinal, interval, and ratio levels of measurement.
- 3. 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. 4 Who Uses Statistics? Statistical techniques are used extensively by marketing, accounting, quality control, consumers, professional sports people, hospital administrators, educators, politicians, physicians, etc...
- 5. 5 Types of Statistics – 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 Voyager 1. The statistic 49 describes the number out of every 100 persons who knew the answer. EXAMPLE 2: According to Consumer Reports, General Electric washing machine owners reported 9 problems per 100 machines during 2015. The statistic 9 describes the number of problems out of every 100 machines. Inferential Statistics: A decision, estimate, prediction, or generalization about a population, based on a sample.
- 6. 6 Population versus Sample A population is a collection of all possible individuals, objects, or measurements of interest. A sample is a portion, or part, of the population of interest
- 7. 7 Types of Variables A. Qualitative or Attribute variable - the characteristic being studied is nonnumeric. EXAMPLES: Gender, religious affiliation, type of automobile owned, state of birth, eye color are examples. B. Quantitative variable - information is reported numerically. EXAMPLES: balance in your checking account, minutes remaining in class, or number of children in a family.
- 8. 8 Quantitative Variables - Classifications Quantitative variables can be classified as either discrete or continuous. A. 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). B. Continuous variable can assume any value within a specified range. EXAMPLE: The pressure in a tire, your weight, or the height of students in a class.
- 9. 9 Summary of Types of Variables
- 10. 10 Four Levels of Measurement Nominal level - data that is classified into categories and cannot be arranged in any particular order. EXAMPLES: eye color, gender, religious affiliation. Ordinal level – involves data arranged in some order, but the differences between data values cannot be determined or are meaningless. EXAMPLE: During a taste test of 4 soft drinks, Mountain Dew was ranked number 1, Sprite number 2, Seven-up number 3, and Coca Cola number 4. 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. EXAMPLE: ? Ratio level - the interval level with an inherent zero starting point. Differences and ratios are meaningful for this level of measurement. EXAMPLES: Monthly income, distance traveled, height, changes in stock prices.
- 11. Four Levels of Measurement Nominal Ordinal Interval Ratio Classify Rank Meaningful Difference Starts from Zero 11
- 12. 12 Summary of the Characteristics for Levels of Measurement
- 13. 13 End of Chapter 1