Stat 1040, Sections 003 & 004:
Study Guide for the Final (Spring 2003)

Chapter Contents

• Chapter 1 - Controlled Experiments:
  randomized controlled experiment, placebo, double blind, treatment group, control group, treatment, response, historical controls
  
  
• Chapter 2 - Observational Studies:
  association, causation, confounding, controlling a confounding factor
  
  
• Chapter 3 - The Histogram:
  drawing a histogram, comparing areas of a histogram, density scale
  
  
• Chapter 4 - The Average and the Standard Deviation:
  calculating average (mean), standard deviation (SD), median, and interquartile range; effect of long tails on average, 68%-95%-99.7% rule, cross-sectional and longitudinal studies
  
  
• Chapter 5 - The Normal Approximation for Data:
  normal curve, standard units, finding area under the curve, normal approximation for data, percentiles (and normal curve), change of scale
  
  
• Chapter 6 - Measurement Error:
  chance error, outliers (and their treatment), bias (systematic error)
  
  
• Chapter 7 - Plotting Points and Lines:
  [no direct question will be asked in the final - however, knowledge of this chapter is required for Chapters 8 through 10] slope, rise, run, (y-) intercept, algebraic equation for a line, plotting lines
  
  
• Chapter 8 - Correlation:
  scatterplot (scatter diagram), independent (explanatory) and dependent (response) variables, correlation coefficient r, computing r, guessing r, SD line, linear association, strong/medium/weak positive/negative/no association, perfect correlation
  
  
• Chapter 9 - More about Correlation:
  features of r, linear association vs. association in general (effect of non-linear association and outliers on r), association vs. causation, ecological correlation
  
  
• Chapter 10 - Regression:
  regression line, constructing the regression line (based on SD line and r), prediction, regression effect, regression fallacy, two regression lines
  
  
• Chapter 11 (omit Sections 11.4 and 11.5) - The R.M.S. Error for Regression:
  error (residual), r.m.s. error (meaning and computation), residual plots (construction and interpretation)
  
  
• Chapter 12 - The Regression Line:
  slope and intercept for regression line (interpretation and computation), equation of the regression line, prediction using this equation, method of least squares (idea)
  
  
• Chapter 13 - What are the Chances?:
  chance, probability, Opposites Rule, conditional probabilities (conditional and unconditional chance), Multiplication Rule, independence and dependence, drawing with/without replacement from a box, Multiplication Rule for independent events
  
  
• Chapter 14 - More About Chance:
  listing the ways (dice, coins, cards, etc.), mutually exclusive events, Addition Rule for mutually exclusive events, general Addition Rule (see Possibility 1 and Possibility 2)
  
  
• Chapter 16 - The Law of Averages:
  the law of averages (interpretation), box models (construction: what numbers, how many of each number, how many draws), sum of draws
  
  
• Chapter 17 - The Expected Value and Standard Error:
  box average and box SD, expected value (EV) and standard error (SE) of the sum, square root law, using the normal curve for box models, classifying and counting, shortcut formulas for the SD for boxes with only two different tickets and for boxes with only 0 and 1 tickets
  
  
• Chapter 18 - The Normal Approximation for Probability Histograms:
  empirical histogram, probability histogram, similarity between empirical and probability histogram after a large number of draws, central limit theorem, normal approximation for sum of draws (related to "large", symmetric/asymmetric box)
  
  
• Chapter 19 - Sample Surveys:
  population, sample, parameter, statistic, inferences, polls, bias, non-response bias, problems due to wording of questions, probability methods, simple random sample (SRS), voluntary responses
  
  
• Chapter 20 - Chance Error in Sampling:
  expected value (EV) and standard error (SE) for the sample percentage, square root law, shortcut formula for calculating the SE percentage when multiplying the size of a sample by a factor, using the normal curve for 0-1 boxes
  
  
• Chapter 21 - The Accuracy of Percentages:
  bootstrap (when sampling from an unknown 0-1 box), confidence intervals for the population percentage (construction and interpretation, e.g., "we are 90% confident...")
  
  
• Chapter 23 - The Accuracy of Averages:
  expected value (EV) and standard error (SE) for the average, using the normal curve for averages, confidence intervals for the population average
  
  
• Chapter 26 - Tests of Significance:
  test of significance, null hypothesis, alternative hypothesis, test statistic, z-score, P-value, statistically significant and highly statistically significant, "Four-Step Procedure for Hypothesis Testing", t-tests (when to use!), SD+, t-statistic, t-table
  
  
• Chapter 27 - More Tests for Averages:
  two-sample tests, two-sample z-test, comparing two sample averages, SE Diff, when to use (controlled experiments and two independent random samples from two populations) and when not (!) to use
  
  
• Chapter 28 - The Chi-Sqaure Test:
  Chi-Square test, Chi-Square statistic, used to check consistency of data with a chance model, testing independence, Chi-Square table
  
  
• Chapter 29 - A Closer Look at Tests of Significance:
  significance of result, data snooping, is a result important?
  
  

Topics in previous Stat 1040 Final Exams

• Histogram: construction (3) and reading off values (2)
• Scatterplot: guess values for mean and SD (1)
• Probability, e.g., independence, complement rule, etc. (5)
• Binomial distribution (4 - no longer examined !)
• Observational study/controlled experiment (6)
• Confounding variable (6)
• Blind/double-blind (2)
• Cross-sectional study (2)
• Sampling: sample size determines accuracy (6)
• Understanding what happens when 2 samples are combined (average can be adjusted, SD will be larger) (1)
• Bias in voluntary response surveys (3)
• SRS, cluster sample, convenience sample (1)
• Regression: slope, intercept, r.m.s., percentage (6)
• Prediction (3)
• Regression effect (3)
• Correlation, e.g., interpretation, ecological correlation (1)
• Change of scale (2) and effect on correlation coefficient (1)
• CI for percentage (2)
• CI for average: calculation and question what if data does not follow the normal curve (7)
• Test for percentages (3)
• 2-sample z-test (7)
• Chi-Square test (2)
• Chi-Square test for independence (5)
• t-test (3) and what if the data does not follow the normal curve (2)
• Interpretation of statistically significant (1)
• Can we conduct a test (or construct a CI) if the data does not follow the normal curve (3)
• Normal curve: area under the curve and percentiles (2)
• Normal curve related to probability (2)
• Normal curve related to sums (2)
• Normal curve - based on outcome of a test, is the data from a normal curve? (1)
• 68%, 95% and 99.7% rule and what if data does not follow the normal curve (2)
• Central limit theorem (1)

Review Sessions