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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...
End
of
Chapter 1