Statistics
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Transcript

  • 1. STATISTICS
    • DESCRIPTIVE STATISTIC
    • describe & organize data in a meaningful way
    • Summarize data
    • Investigate relationships between variables
    • A preliminary analysis before using inferential techniques
  • 2. INFERENTIAL STATISTICS
    • To draw conclusions
    • To make generalization about population
  • 3. TYPES OF DATA
    • NOMINAL DATA
    • a. Categories of names with no implied in order
    • b. Represent different characteristics of given variable.
    • c. e.g. Gender 1. male 2 Female
    • Name of town
    • Exam subjects
  • 4. ORDINAL DATA
    • Ordered data with spaces or interval between data values –not necessarily equal.
    • The numbers assigned to the items-numerical significance indicate –ranking of those items
    • e.g. strongly agree =1, agree = 2 no opinion = 3, disagree = 4 strongly disagree = 5
    • Birth position in family, exam grades
  • 5. Interval Data
    • Ordered data with spaces or intervals between data values that are equal.
    • The number assigned to the items within a variable have numerical significance, indicate the ranking of those items
    • Indicate the interval between those item
  • 6. e.g. Interval Data
    • IQ Scale
    • The interval between 100 and 101 is the same as those between 101 and 102 and between 102 and 103
    • Exam scores
  • 7. RATIO DATA
    • The ordered data with space intervals between data values that are equal.
    • Contain a true or rational zero point
    • Ratio data have meaningful ratios.
  • 8. ratio
    • e.g. (1) interval between 5 and 6 (correct) is the same as those 6 and 7 and between 7 and 8. (2) there is a true zero point (0 correct) (3) a score 10 is twice as much as score of 5 and half as much as a score of 20
    • Family size, number of exam passes
  • 9. COMPARING TWO MEANS
    • Test the statistical significance of the differences between two means – consideration:
    • a. the nature of data
    • b. the nature of samples
  • 10. Parametric Test (t-test)
    • Interval or ratio data
    • Data normally distributed
    • Homogeneity of variance
  • 11. Samples paired-dependent or correlated
    • Use a t-test for paired sample
  • 12. Sample unpaired – independent or uncorrelated
    • Use a t-test for unpaired sample
  • 13. Non- Parametric Test
    • Nominal or ordinal data
    • If the samples are paired – a Wilcoxon sign-ranked test would be use
    • If the sample are unpaired- a Mann- Whitney U test
  • 14. Comparing Three or More Means
    • Considerations
    • a. Nature of the data
    • b. Interval or Ratio Data
    • c. Data are normally distributed
    • Use analysis of Variance (ANOVA)
  • 15. Use Kruskal – Wallis one – way analysis of variance if
    • Any variable is ordinal data and there is only one factor.
    • Non-parametric
  • 16. ANOVA
    • Common inferential statistical technique