Ch01sp10
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Ch01sp10 Ch01sp10 Presentation Transcript

  • Chapter 1 What is Statistics?
    • GOALS: Upon successful completion, you should be able to:
    • Define “Business Statistics”
    • Differentiate between the different types of data and levels of measurement
    • Describe key data collection methods
    • Identify common sampling methods
    • Distinguish the different areas of statistics
    • Explain why you study statistics
  • 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 DATA Meaningful Information
  • Types of Data
    • Classified as:
    • Quantitative / Qualitative
    • and
    • Time-Series / Cross-Sectional
  • 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
  • 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
  • Levels of Measurement Nominal Ordinal Interval Ratio Lowest Highest
  • 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
  • Levels of Measurement
    • Ordinal
    • Data are rank-ordered, order is meaningful, differences between rankings not meaningful
    Examples: Sports rankings, Earthquake magnitude [Richter scale]
  • 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
  • Levels of Measurement
    • Ratio
    • Ratio of two data values IS meaningful
    Examples: Income, distance, time, weight, height
  • 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
  • Data Collection Issues - Errors Non-sampling Sampling Interviewer/Instrument Bias Non-response Bias Selection Bias Interviewee Lie Measurement Error Observer Bias Bad Luck
  • Data Errors
    • BIRMINGHAM
    • B IRMINGHAM
    • BHAM
    • BHAMI
    • BIARMINGHAM
    • BIMRINGHAM
    • BIRIMINGHAM
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    • BIRMINGHAH
    • BIRMINGHAM
    • BIRMINGHAM`
    • BIRMINHAM
    • BIRMINHGAM
    • BIRMINHGHAM
    • BIRMINNGHAM
    • BIRMNGHAM
    • BIRNINGHAM
    • BRIMINGHAM
    • BRMINGHAM
    • BURMINGHAM
  • Statistics Terminology Sample A portion, or part, of the population of interest Population The collection of all possible individuals, objects, or measurements of interest
  • Why Sample?
    • Time Requirement
    • Cost of Acquisition
    • Destructive Sampling
    • Sample Results can be very accurate!!
  • Sampling Techniques Convenience Samples Non-Probability Samples Judgement Probability Samples Simple Random Systematic Stratified Cluster
  • 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
  • Systematic Random Sampling
    • Select every k th 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 16 th student.
    • Then select every 25 th student from there: 41, 66, 91, …
  • Stratified Samples
    • Suppose we want to select 160 students in proportion to college enrollments.
    15% NURS 30% ED 35% BUS 20% A&S % College 24 NURS 48 ED 56 BUS 32 A&S # in Sample College
  • Cluster Sampling
    • Population divided into clusters
    • Randomly select clusters and randomly sample or census within the clusters
  • Components of Business Statistics
    • Descriptive Statistics [Ch. 2 & 3]
    • Probability [Ch. 4, 5 & 6]
    • Inferential Statistics [Ch. 7 & 8]
  • Descriptive Statistics
    • Methods of organizing, summarizing, and presenting data in an informative way.
    • Graphical & Tabular [Ch. 2]
    • Numerical [Ch. 3]
  • Descriptive Statistics – Graphical
  • Descriptive Statistics – Tabular
  • Descriptive Statistics – Numerical On the Feb. 9, 1964, Ed Sullivan Show
  • Probability
    • Methods of assessing likelihood of sample outcomes given a known population.
    POPULATION SAMPLE
  • Florida Lotto Ticket - Front
  • Florida Lotto Ticket - Back
  • Inferential Statistics
    • A decision, estimate, prediction, or generalization about a population, based on a sample.
    SAMPLE POPULATION
  • Inferential Statistics - Estimation
  • Inferential Statistics – Hypothesis Testing
  • 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