A Poem The information you have is not the information you want The information you want is not the information you need The information you need is not the information you can obtain The information you can obtain costs more than you want to pay

What is Meant by Statistics? 1-2 What is Meant by Statistics? Statistics is the science of collecting, organizing, presenting, analyzing, and interpreting numerical data for the purpose of assisting in making a more effective decision.

If you torture data hard enough, the data will confess whatever you are willing to hear. ------ Ronald Coase (Nobel Laureate)

1-3 Who Uses Statistics? Statistical techniques are used extensively by marketing, accounting, quality control, consumers, professional sports people, hospital administrators, educators, politicians, physicians, etc...

1-4 Types of 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. EXAMPLE 2: According to Consumer Reports, Whirlpool washing machine owners reported 9 problems per 100 machines during 1995. The statistic 9 describes the number of problems out of every 100 machines.

1-5 Types of Statistics Inferential Statistics: A decision, estimate, prediction, or generalization about a population, based on a 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.

Types of Statistics (examples of inferential statistics) 1-6 Types of Statistics (examples of inferential statistics) 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 2: 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. EXAMPLE 3: Wine tasters sip a few drops of wine to make a decision with respect to all the wine waiting to be released for sale.

1-7 Types of Variables Qualitative or Attribute variable: the characteristic or variable being studied is nonnumeric. EXAMPLES: Gender, religious affiliation, type of automobile owned, state of birth, eye color.

1-8 Types of Variables Quantitative variable: the variable can be reported numerically. EXAMPLE: balance in your checking account, minutes remaining in class, number of children in a family.

1-9 Types of Variables Quantitative variables can be classified as either discrete or continuous. Discrete variables: can only assume certain values and there are usually “gaps” between values. EXAMPLE: the number of bedrooms in a house. (1,2,3,..., etc...).

1-10 Types of Variables Quantitative Variables can be classified as either discrete or continuous. Continuous variables: can assume any value within a specific range. EXAMPLE: The time it takes to fly from Toledo to New York.

Summary of Types of Variables 1-11 Summary of Types of Variables

Sources of Statistical Data 1-12 Sources of Statistical Data Researching problems usually requires published data. Statistics on these problems can be found in published articles, journals, and magazines. Published data is not always available on a given subject. In such cases, information will have to be collected and analyzed. One way of collecting data is via questionnaires.

1-13 Levels of Measurement Nominal level (scaled): Data that can only be classified into categories and cannot be arranged in an ordering scheme. EXAMPLES: eye color, gender, religious affiliation.

1-14 Levels of Measurement Mutually exclusive: An individual or item that, by virtue of being included in one category, must be excluded from any other category. EXAMPLE: eye color. Exhaustive: each person, object, or item must be classified in at least one category. EXAMPLE: religious affiliation.

1-15 Levels of Measurement Ordinal level: involves data that may be arranged in some order, but differences between data values cannot be determined or are meaningless. EXAMPLE: During a taste test of 4 colas, cola C was ranked number 1, cola B was ranked number 2, cola A was ranked number 3, and cola D was ranked number 4.

1-16 Levels of Measurement 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: Temperature on the Fahrenheit scale.

1-17 Levels of Measurement Ratio level: the interval level with an inherent zero starting point. Differences and ratios are meaningful for this level of measurement. EXAMPLES: money, heights of NBA players.

Anecdotal evidence vs. representative evidence Anecdotal evidence is based on haphazardly selected individual cases; Representative evidence is usually based on “randomly selected” evidence.

Observational studies vs. Experiments Basic idea: in the observational study the researcher collects the data as they currently are, he or she is not “in charge” of assignment. In other words, the researcher cannot assign a treatment so for these kinds of studies there are No true treatment groups No true control Observational studies are inexpensive and do not require as much thoughtfulness.

Experiments and their design Treatment (an intervention) Control (what if we never intervened) A measurable response (a real outcome)

Even when we are clever, there may still be problems - Confounding : the effect of an unforeseen characteristic, behavior, event or procedure on the response that cannot be distinguished from the proposed treatment.

Solutions Randomization –eliminates bias Placebo –eliminates the “placebo effect” Double Blind –eliminates bias Blind – may eliminate bias Replication – validate results

Review of Experiments Key ideas: the researcher is able to assign a treatment and observe an outcome Strength: able to control for potential confounding factors Best Method: Random assignment to treatment or control group Goal: to compare outcomes between groups Notes: replication offer strength when placebo/blinding is impossible

Sampling Design Population Vs. Sample Ways to create sample: Voluntary response sample Simple random sample (SRS) Stratified random sample Multistage sample

1-1 To Sum Up GOALS When you have completed this chapter, you will be able to: ONE Define what is meant by 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.Define the terms mutually exclusive and exhaustive. SIX Understand the difference between observational study and experiments SEVEN Understand the sources of data, how to design an experiment