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# Ch01sp10

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### Ch01sp10

1. 1. Chapter 1 What is Statistics? <ul><li>GOALS: Upon successful completion, you should be able to: </li></ul><ul><li>Define “Business Statistics” </li></ul><ul><li>Differentiate between the different types of data and levels of measurement </li></ul><ul><li>Describe key data collection methods </li></ul><ul><li>Identify common sampling methods </li></ul><ul><li>Distinguish the different areas of statistics </li></ul><ul><li>Explain why you study statistics </li></ul>
2. 2. What is Meant by Statistics? <ul><li>Statistics is the science of collecting, organizing, presenting, analyzing, and interpreting data to assist in making more effective decisions. </li></ul>Tools & Techniques DATA Meaningful Information
3. 3. Types of Data <ul><li>Classified as: </li></ul><ul><li>Quantitative / Qualitative </li></ul><ul><li>and </li></ul><ul><li>Time-Series / Cross-Sectional </li></ul>
4. 4. Types of Data Qualitative Quantitative Categorical Mathematical Gender, hair color, major, classification, marital status, Likert-style data, zip code , ssn , phone number Age, height, weight, salary, miles per gallon, life of a light bulb
5. 5. Types of Data Cross-Sectional Time-Series Data observed at one point in time Data observed over time Number of business [act, fin, is, …] majors enrolling this term Stock price of Taco Bell, KFC, & Subway at end of day Quarter enrollment, weekly sales, daily sales price of a gallon of gas
6. 6. Levels of Measurement Nominal Ordinal Interval Ratio Lowest Highest
7. 7. Levels of Measurement <ul><li>Nominal </li></ul><ul><li>Coded data, codes may or may not be a number, NOT mathematical </li></ul>Examples: 1. ACT 2. FIN 3. IS S – Single M – Married D - Divorced
8. 8. Levels of Measurement <ul><li>Ordinal </li></ul><ul><li>Data are rank-ordered, order is meaningful, differences between rankings not meaningful </li></ul>Examples: Sports rankings, Earthquake magnitude [Richter scale]
9. 9. Levels of Measurement <ul><li>Interval </li></ul><ul><li>Similar to ordinal data, WITH differences between data values being meaningful, BUT ratio of two data values not meaningful </li></ul>Examples: Temperature, shoe size
10. 10. Levels of Measurement <ul><li>Ratio </li></ul><ul><li>Ratio of two data values IS meaningful </li></ul>Examples: Income, distance, time, weight, height
11. 11. Data Collection Methods Secondary Primary Data obtained from another source Data collected first-hand Data collection organizations Government agencies Industry associations Internet Experiments Telephone surveys Direct observation Personal Interviews
12. 12. Data Collection Issues - Errors Non-sampling Sampling Interviewer/Instrument Bias Non-response Bias Selection Bias Interviewee Lie Measurement Error Observer Bias Bad Luck
13. 13. Data Errors <ul><li>BIRMINGHAM </li></ul><ul><li>B IRMINGHAM </li></ul><ul><li>BHAM </li></ul><ul><li>BHAMI </li></ul><ul><li>BIARMINGHAM </li></ul><ul><li>BIMRINGHAM </li></ul><ul><li>BIRIMINGHAM </li></ul><ul><li>BIRINGHAM </li></ul><ul><li>BIRMIGHAM </li></ul><ul><li>BIRMIGNHAM </li></ul><ul><li>BIRMIINGHAM </li></ul><ul><li>BIRMIMGHAM </li></ul><ul><li>BIRMINGAHM </li></ul><ul><li>BIRMINGHA M </li></ul><ul><li>BIRMINGHAH </li></ul><ul><li>BIRMINGHAM </li></ul><ul><li>BIRMINGHAM` </li></ul><ul><li>BIRMINHAM </li></ul><ul><li>BIRMINHGAM </li></ul><ul><li>BIRMINHGHAM </li></ul><ul><li>BIRMINNGHAM </li></ul><ul><li>BIRMNGHAM </li></ul><ul><li>BIRNINGHAM </li></ul><ul><li>BRIMINGHAM </li></ul><ul><li>BRMINGHAM </li></ul><ul><li>BURMINGHAM </li></ul>
14. 14. Statistics Terminology Sample A portion, or part, of the population of interest Population The collection of all possible individuals, objects, or measurements of interest
15. 15. Why Sample? <ul><li>Time Requirement </li></ul><ul><li>Cost of Acquisition </li></ul><ul><li>Destructive Sampling </li></ul><ul><li>Sample Results can be very accurate!! </li></ul>
16. 16. Sampling Techniques Convenience Samples Non-Probability Samples Judgement Probability Samples Simple Random Systematic Stratified Cluster
17. 17. Simple Random Sampling <ul><li>Every possible subset of n units has the same chance of being selected </li></ul><ul><li>How to do it: </li></ul><ul><ul><li>Use random number table or random number generator, such as Excel </li></ul></ul><ul><ul><ul><li>Assign numbers to population </li></ul></ul></ul><ul><ul><ul><li>Select n random numbers </li></ul></ul></ul><ul><ul><ul><li>Sample population elements that correspond to the random numbers </li></ul></ul></ul>
18. 18. Systematic Random Sampling <ul><li>Select every k th where k=N/n, starting with a randomly chosen student from 1 to k. </li></ul><ul><li>Example: Suppose N=5000 students and we want to sample n=200 students. </li></ul><ul><li>N/n = 5000/200 = 25. </li></ul><ul><li>Select a random number from 1 to 25. </li></ul><ul><li>Suppose you randomly select the 16 th student. </li></ul><ul><li>Then select every 25 th student from there: 41, 66, 91, … </li></ul>
19. 19. Stratified Samples <ul><li>Suppose we want to select 160 students in proportion to college enrollments. </li></ul>15% NURS 30% ED 35% BUS 20% A&S % College 24 NURS 48 ED 56 BUS 32 A&S # in Sample College
20. 20. Cluster Sampling <ul><li>Population divided into clusters </li></ul><ul><li>Randomly select clusters and randomly sample or census within the clusters </li></ul>
21. 21. Components of Business Statistics <ul><li>Descriptive Statistics [Ch. 2 & 3] </li></ul><ul><li>Probability [Ch. 4, 5 & 6] </li></ul><ul><li>Inferential Statistics [Ch. 7 & 8] </li></ul>
22. 22. Descriptive Statistics <ul><li>Methods of organizing, summarizing, and presenting data in an informative way. </li></ul><ul><li>Graphical & Tabular [Ch. 2] </li></ul><ul><li>Numerical [Ch. 3] </li></ul>
23. 23. Descriptive Statistics – Graphical
24. 24. Descriptive Statistics – Tabular
25. 25. Descriptive Statistics – Numerical On the Feb. 9, 1964, Ed Sullivan Show
26. 26. Probability <ul><li>Methods of assessing likelihood of sample outcomes given a known population. </li></ul>POPULATION SAMPLE
27. 27. Florida Lotto Ticket - Front
28. 28. Florida Lotto Ticket - Back
29. 29. Inferential Statistics <ul><li>A decision, estimate, prediction, or generalization about a population, based on a sample. </li></ul>SAMPLE POPULATION
30. 30. Inferential Statistics - Estimation
31. 31. Inferential Statistics – Hypothesis Testing
32. 32. Why Should You Study Statistics? <ul><li>Statistical techniques are used extensively by managers in: </li></ul><ul><ul><li>marketing, </li></ul></ul><ul><ul><li>accounting, </li></ul></ul><ul><ul><li>quality control, </li></ul></ul><ul><ul><li>finance, </li></ul></ul><ul><ul><li>economics, </li></ul></ul><ul><ul><li>politicians, etc... </li></ul></ul>
33. 33. <ul><li>End </li></ul><ul><li>of </li></ul><ul><li>Chapter 1 </li></ul>