Ch01sp10

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Chapter 1What is Statistics?

GOALS: Upon successful completion, you should be able to:

1. Define “Business Statistics”

2. Differentiate between the different types of

data and levels of measurement

3. Describe key data collection methods

4. Identify common sampling methods

5. Distinguish the different areas of statistics

6. Explain why you study statistics

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What is Meant by Statistics?

Statistics is the science of collecting, organizing, presenting, analyzing, and interpreting data to assist in making more effective decisions.

Tools & Techniques

DATAMeaningfulInformatio

n

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Types of Data

Classified as:

Quantitative / Qualitative

and

Time-Series / Cross-Sectional

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Types of Data

Quantitative Qualitative

Mathematical Categorical

Age, height, weight, salary, miles per gallon, life of a light bulb

Gender, hair color, major, classification, marital status, Likert-style data, zip code, ssn, phone number

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Types of Data

Time-Series Cross-Sectional

Data observed over time Data observed at one point in time

Quarter enrollment, weekly sales, daily sales price of a gallon of gas

Number of business [act, fin, is, …] majors enrolling this term

Stock price of Taco Bell, KFC, & Subway at end of day

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Levels of Measurement

Lowest

Highest

Nominal

Ordinal

Interval

Ratio

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Levels of Measurement

Nominal

Coded data, codes may or may not be a number, NOT mathematical

Examples:

1. ACT 2. FIN 3. IS

S – Single M – Married D - Divorced

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Levels of Measurement

Ordinal

Data are rank-ordered, order is meaningful, differences between rankings not meaningful

Examples:

Sports rankings, Earthquake magnitude [Richter scale]

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Levels of Measurement

Interval

Similar to ordinal data, WITH differences between data values being meaningful, BUT ratio of two data values not meaningful

Examples:

Temperature, shoe size

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Levels of Measurement

Ratio

Ratio of two data values IS meaningful

Examples:

Income, distance, time, weight, height

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Data Collection Methods

Primary Secondary

Data collected first-hand Data obtained from another source

ExperimentsTelephone surveysDirect observationPersonal Interviews

Data collection organizationsGovernment agenciesIndustry associationsInternet

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Data Collection Issues - Errors

Sampling Non-sampling

Bad Luck Interviewer/Instrument BiasNon-response BiasSelection BiasInterviewee LieMeasurement ErrorObserver Bias

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Data Errors

1. BIRMINGHAM 2. B IRMINGHAM 3. BHAM 4. BHAMI 5. BIARMINGHAM

6. BIMRINGHAM 7. BIRIMINGHAM 8. BIRINGHAM 9. BIRMIGHAM 10. BIRMIGNHAM 11. BIRMIINGHAM 12. BIRMIMGHAM

13. BIRMINGAHM

14. BIRMINGHA M 15. BIRMINGHAH 16. BIRMINGHAM 17. BIRMINGHAM`

18. BIRMINHAM 19. BIRMINHGAM 20. BIRMINHGHAM

21. BIRMINNGHAM

22. BIRMNGHAM 23. BIRNINGHAM 24. BRIMINGHAM 25. BRMINGHAM 26. BURMINGHAM

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Statistics Terminology

Sample

A portion, or part, of the population of interest

Population

The collection of all possible individuals, objects, or measurements of interest

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Why Sample?

• Time Requirement• Cost of Acquisition• Destructive Sampling

• Sample Results can be very accurate!!

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Sampling Techniques

Convenience

Samples

Non-Probability Samples

Judgement

Probability Samples

Simple Random

Systematic

StratifiedCluster

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Simple Random Sampling

• Every possible subset of n units has the same chance of being selected

• How to do it:– Use random number table or random number generator,

such as Excel• Assign numbers to population• Select n random numbers• Sample population elements that correspond to the

random numbers

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Systematic Random Sampling

• Select every kth where k=N/n, starting with a randomly chosen student from 1 to k.

• Example: Suppose N=5000 students and we want to sample n=200 students.

N/n = 5000/200 = 25.Select a random number from 1 to 25. Suppose you randomly select the 16th student. Then select every 25th student from there: 41, 66, 91,

…

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Stratified Samples

Suppose we want to select 160 students in proportion to college enrollments.

College %

A&S 20%

BUS 35%

ED 30%

NURS 15%

College # in Sample

A&S 32

BUS 56

ED 48

NURS 24

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Cluster Sampling

• Population divided into clusters• Randomly select clusters and randomly

sample or census within the clusters

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Components of Business Statistics

Descriptive Statistics [Ch. 2 & 3]

Probability [Ch. 4, 5 & 6]

Inferential Statistics [Ch. 7 & 8]

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Descriptive Statistics

Methods of organizing, summarizing, and presenting data in an informative way.

Graphical & Tabular [Ch. 2]

Numerical [Ch. 3]

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Descriptive Statistics – Graphical

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Descriptive Statistics – Tabular

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Descriptive Statistics – Numerical

On the Feb. 9, 1964, Ed Sullivan Show

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Probability

Methods of assessing likelihood of sample outcomes given a known population.

POPULATION SAMPLE

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Florida Lotto Ticket - Front

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Florida Lotto Ticket - Back

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Inferential Statistics

A decision, estimate, prediction, or generalization about a population, based on a sample.

SAMPLE POPULATION

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Inferential Statistics - Estimation

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Inferential Statistics – Hypothesis Testing

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Why Should You Study Statistics?

Statistical techniques are used extensively by managers in:

– marketing, – accounting, – quality control, – finance, – economics, – politicians, etc...

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Chapter 1