Quantitative Methods II : Exam I
 Wednesday, April 19     8:00 pm


You will need a calculator for this exam

I. Introduction
A. Using statistics
    1. meaning, context, definition
    2. methodology
    3. descriptive statistics, uncertainty, estimation

B. Reporting statistics
    1. publication bias
        a. "positive" results
        b. sponsor
    2. conclusions may not reflect statistical results
    3. results may not be statistically significant
        - is a confidence level or margin of error reported?
    4. results may not have real world significance
    5. biased samples or biased surveys will give meaningless results
        - is methodology reported?

II. Describing Data

A. Graphs
    1. qualitative variables
        a. pie charts
        b. bar charts
    2. quantitative variables
        a. histograms
        b. stemplots
 
B. Measures of central tendency
    1. mean
    2. median
    3. mode

C. Measures of dispersion
    1. variance
    2. standard deviation
    3. range
    4. quartiles, quintiles, percentiles

D. Summarizing distributions
    1. mean, standard deviation
    2. five-number summary
    3. boxplot
 
E. Descriptive Statistics and probability
    1. Chebyshev's theorem
    2. The Empirical Rule
        a. normal distribution
        b. Six Sigma

III. The Normal distributions
    1. characteristics
        a. family of curves
        b. symmetric
        c. bell-shaped
    2. the standard normal
        a. mean = 0, standard deviation = 1
        b. tables and probabilities
        c. transforming any normal to standard form
        d. solving problems

IV. Relations between variables

    1. scatterplots
    2. covariance
    3. correlation
        a. positive, negative, or zero correlation
        b. strong or weak correlation
    4. regression
 
    5. correlation does not imply causality
        a. X may cause Y
        b. Y may cause X
        c. X and Y may interact
        d. a third  variable may cause both X and Y
        e. the correlation might be spurious

    6. Ordinary Least Squares regression estimates
        a. best, linear, unbiased estimate
        b. computer estimation
        c. r-squared
        d. residuals

    7. Regression issues
     a. regression does not show causality
     b. Data collection must be methodologically sound
        i. random sample, representative of population
        ii. avoid bias in survey questions
        iii. results are sensitive to extreme values (outliers)
        iv. model must be correctly specified
            - missing variables
            - spurious relations
 
 V. Data Collection

A. Samples must be random and representative

1. sampling design
    a. random sample
    b. systematic sample
    c. stratified sample
    d. cluster sample

2. response rates
          3. Sample bias
            a. good samples are representative of population
            b. biased samples-- meaningless results

B. Estimation
    1. sample statistics are imperfect estimates of population parameters
    2. variability
        a. sampling error
        b. larger samples reduce variability
        c. standard error
    3. interval estimates may be preferable

C. Survey Methods
    1. write questions carefully
        i. avoid bias
            a. leading questions
            b. confusing questions
            c. over reliance on memory
        ii. group like questions together
    2. test survey
    3. revise

D. Direct observation

E. Experiments
    1. control group and study group
        a. random
        b. placebo
    2. blind and double blind

  

 


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