The final exam will be given at 12:30 p.m. on Wednesday, December 10 in VWF 238.  Don't forget to bring a calculator and a page of notes to use during the test.  The test covers the following material.

Chapter 1: Statistics: The Art and Science of Learning from Data - You should know what the design, description, and inference for a statistical study are.  You should know the difference between a parameter and a statistic as well as a population and a sample.

Chapter 2: Exploring Data with Graphs and Numerical Summaries - You should be able to construct and interpret histograms, bar graphs, stemplots, pie charts, and time plots. You should be able to determine if a distribution is symmetric or skewed, or if it has any outliers. You should also know the difference between categorical and quantitative variables.  Given a data set, you should be able to determine its mean, median, standard deviation, quartiles, and five number summary. You should know when a mean is appropriate to use and when a median is appropriate and how the shape of a distribution affects the mean and median. You should know what the interquartile range is and be able to construct boxplots.  You should also be able to estimate the standard deviation of a data set, use the Empirical Rule, and know what a z-score is.

Chapter 3: Association: Contingency, Correlation, and Regression - You should know the difference between explanatory and response variables. You should be able to read a contingency table and understand the difference between marginal and conditional distributions. You should also understand Simpson's paradox and why a reversal of a comparison can happen when data is aggregated. You should be able to construct a scatterplot and describe its direction, form, and strength. You should know what correlation measures, some of its properties, and its limitations. You should also know how to roughly determine the correlation by just looking at a scatterplot. You should be able to plot a regression line, use a regression equation to predict an outcome for a given input, and know what the y-intercept and slope mean in the context of the application. You should know how to calculate residuals. You should know what the following terms mean and how they affect correlation and regression: lurking variables, influential observations, and extrapolation. You should know the relationship between correlation and causation. You should also know how a correlation based on averages is different than a correlation not based on averages.

Chapter 4: Gathering Data - You should know the difference between an experiment and an observational study.  You should know what bias and variability are and how each are controlled. You should know what a simple random sample is. You should also know how volunteer response affects a sample.  You should know what response bias, non response bias, and sampling bias are.  You should know what the following words mean in terms of designing experiments: experimental units (subjects), factors, response variable, placebo effect, control group, and double-blind.

Chapter 5: Probability in our Daily Lives - You should be able to describe what the probability of some event means.  You should know what the following terms are: probability model, sample space, event, complement, and disjoint. You should also know and be able to use the seven basic properties of probability mentioned in this chapter. You should also know what a random variable is and what a probability distribution is.

You should know what independent events are and know how to determine if events are independent.  You should understand (and be able to use) the multiplication rule for independent events. You should understand (and be able to use) the general addition rule for any two events.   Given a two-way table (with either counts or probabilities), you should know how to find conditional probabilities. You should understand (and be able to use) the general multiplication rule for any two events. You should be able to use the formula given as the definition of conditional probability.  You should be able to use tree diagrams and Venn diagrams to help you solve probability problems. You should know what sensitivity, specificity, false positive, and false negative are.

You should know what a binomial distribution is, when it can be used, and be able determine probabilities associated with a binomial distribution using either the formula or your calculator. You should also be able to determine the mean and the standard deviation of a binomial count as well as those for a sample proportion.

Chapter 7: Sampling Distributions - You should know what the Central Limit Theorem is and how it is used. You should know what a sampling distribution is. You should be able to find probabilities, using the normal distribution, associated with a sampling distribution.

Chapter 8: Statistical Inference---Confidence Intervals - You should know what factors influence the width of a confidence interval and how they influence it. You should be able to describe what a confidence interval really means. You should also be able to determine the sample size required for a confidence interval to have a desired margin of error.

You should know how to determine a confidence interval for a single population proportion. You should also be able to determine the sample size needed to construct a confidence interval with a certain margin of error for a single population proportion both with a estimated sample proportion and a totally unknown sample proportion.

You should know how to determine a confidence interval for the mean of a population. You should know some of the characteristics of the t-distribution. You should also know the assumptions and limitations of using the t procedures.  You should also know how to estimate the sample size needed for a confidence interval to have a desired margin of error.

You should know how to perform a test of significance for a single population proportion.  You should also know the assumptions and limitations of using our inference methods on population proportions. 

You should know how to perform a test of significance for the mean of a population.  You should also know the assumptions and limitations of using the t procedures.

You should know how to perform a test of significance and determine a confidence interval for a difference between two population proportions.

Chapter 11: Analyzing the Association Between Categorical Variables - You should also be able to perform a chi-square test for independence on a two-way table and know what this test is testing for.  You should know how the expected frequencies are determined and what these mean.

Chapter 12: Analyzing the Association Between Quantitative Variables - You should be able to perform tests of significance for the slope of a regression equation. You should also be able to understand how a confidence interval and prediction interval for the output of a regression equation differ.

Chapter 14: Comparing Groups: One-Way Analysis of Variance - You should understand what an ANOVA test is testing for and be able to perform an ANOVA test.  You should generally understand how the test statistic is determined.  In particular what the numerator and denominator of the F statistic represent.