SCCC 312A -- EXAM 1 REVIEW SHEET

I.  Basic Fundamentals and Definitions
 A.  Basic Terms
  1.  Descriptive and Inferential Statistics
  2.  Individuals, Variables, Data
  3.  Population vs. Sample, Parameters vs. Statistics
  4.  Observational Study vs. Experiment
 B.  Types of Variables
  1.  Categorical (Nominal, Ordinal)
  2.  Numerical (Discrete, Continuous)
 C.  Principles of Data Collection
  1.  Types of Biased Samples and "Good" Samples
  2.  Convenience Samples, Volunteer Samples
  3.  Probability Samples
    a.  Simple Random Sample
    b.  Stratified Random Sample, Proportional Sample, Cluster Sample

II. Descriptive Statistical Methods for Univariate Data
 A.  Graphs and Plots
   1. Circle graph, Bar graph
   2. Dotplot, Stem-and-Leaf Plot, Frequency Distributions, Histograms
   3. Interpreting Shapes of Histograms
   4.  Possible Advantages and Disadvantages of the Graphical Methods
 B.  (Numerical) Summary Statistics
   1. Measures of Central Tendency
     a. Sample Mean, Median, Mode, Midrange
     b. Know how to calculate for small data sets
     c. Advantages/disadvantages for the different statistics
   2. Measures of Dispersion (Spread)
     a.  Why are they important?
     b.  Sample Variance, Standard Deviation, Range, IQR
     c.  Know Definitions for Each
     d.  Possible advantages/disadvantages
   3. Measures of Position
     a.  Quartiles and 5-Number Summary; Percentiles
     b.  Box-and-whisker Plot and Interpreting It
   4. Standard (z) scores for Data
     a.  Interpretions of Z-scores
  C. Empirical Rule and Chebyshev's theorem
   1.  What can we conclude for them?
   2.  When does each one apply?

III. Methods for the Analysis of Bivariate Data
  A. Analysis of Two Categorical Variables
   1. Contingency Tables
  B. Analysis of One Categorical Variable and One Numerical Variable
   1. Side-by-Side Graphs and Summaries
  C. Analysis of Two Numerical Variables
   1. Independent and Dependent Variables
   2. Scatterplots and their Interpretions
     a. Overall pattern, strength, direction
     b. Negative and positive association
   3. Linear Correlation
     a. What it measures and how it is interpreted
     b. Type of variables for which correlation can be measured
     c. Linear association vs. Curved association
   4. Linear Regression
     a. Role of Independent and Dependent Variables
     b. Difference between Goals of Correlation and Regression
     c. When is Linear Regression Approriate?
     d. Rationale Behind Least-Squares Line
     e. Interpretations for Slope and Intercept
     f. Using regression line for prediction
     g. Cautions about Extrapolation
     h. Interpretation of R-squared