Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Ch01sp10

785 views

Published on

Published in: Technology
  • Be the first to comment

  • Be the first to like this

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>

×