Download Review Test I

Review Final
Chapter 1: Using graphs to picture distributions
Categorical variables: pie charts & bar graphs
Quantitative variables: histograms, stem&leaf plots, box-plots
Describing distributions:symmetric( x =M) or skewed (right x >M, left x <M)
unimodal or bimodal
compact or spread out
Chapter 2: Using numbers to describe distributions
Center of distribution: mean, median mode
Spread of distribution: quartiles, 5-number summary, standard deviation
Chapter 3: Normal distribution
Density curves: area under curve is one, on or above x-axis, can find area under curve
between any two points.
68-95-99.7 rule
standard normal distribution (z-scores … z = (x-)/
Know how to use normalcdf (from, to, mu, sigma) and invNorm(area to the left, mu, sigma)
buttons on calculator under DISTR
Chapter 4: Scatter plots and correlation
Explanatory/response relationship
Positive association & negative association
Correlation (r) gives direction and strength of relationship (-1  r  1) close to –1 or 1
is a strong relationship, close to 0 is a weak association.
Correlation does not necessarily imply causation.
Chapter 5: Regression
“regress y on x”
Least square regression equation ŷ =a + bx where a = y -b x and b = r (sy/sx)
Be able to describe what a and b mean in terms of the problem.
Know what r2 is and what it means.
Finding a predicted value for a given x.
Residuals (y- ŷ ) and residual plots
Outliers and influential observations
Extrapolation beyond the range of the data
Lurking variables
Chapter 6: Two-way Tables
Marginal distributions
Conditional distributions
Simpson’s paradox
Chapter 7: Sampling
Observational studies vs. experiments
Population vs. sample (also parameters vs. statistics)
Different ways to sample:
SRS (using random digits table)
Voluntary response (usually bias)
Convenience sample (usually bias)
Probability sample
Stratified random sample
Undercoverage
Nonresponse
Chapter 8: Experiments
Know what subjects, factors, and treatments are.
Be able to diagram an experiment.
Chapter 9: Probability
Definition of probability: random phenomenon whose long-term relative frequency is known.
Know what the following are: sample space, event, probability model (all pi such that
0  pi  1 and sum of pi = 1)
Know the probability rules (pg. 231)
Continuous vs. discrete random variables.
Chapter 10: Sampling distribution
Law of large numbers: as sample size increases, statistic gets closer to parameter is
estimating.
Sampling distribution of a sample mean, x ~N(,/ n ) if x is normal.
Central limit theorem (CLT) when n is large, x is approximately N(,/ n )
Know how to find probabilities associated with the normal curve.
Chapter 11: General Probability Rules
Know rules on page 481.
Know what disjoint events are.
Know what independent events are and the multiplication rule for independence (pg283)
Know the general rule for addition of any two events (pg 287)
Know what conditional probabilities are and how to calculate them (pg 289)
Know general multiplication rule for any two events (pg 291)
Chapter 12: Binomial distributions
Know what a binomial setting is, and the binomial distribution.
Know how to use the binomial probability formula (pg 308) and what the binomial
coefficient is (307)
Know how to find the mean and standard deviation of a binomial random variable.
Know when and how to use the normal approximation to the binomial
Chapter 13: Confidence intervals
Know how to construct a CI for mu (pg 327)
Know how to find a sample size for a desired margin of error. (pg 331)
Chapter 14 & Chapter 15: These chapters lay the foundations for statistical testing.
Know how to state null and alternative hypotheses
Know how to find p-values for any statistical test and what those p-values mean.
Be aware of bad statistical designs that might give erroneous results.
Be aware of multiple testing,  = .05 not being sacred, practical significance vs. statistical
significance.
Chapter 16: Inference about a population mean
One sample and matched pairs t-tests are covered in this section.
Know how to run the t-test and find CIs in both of these scenarios.
Know assumptions and limitation of the t-procedure. (pg. 426)
Know what the t-distribution looks like.
Chapter 17: Two sample problems
Know how to run a t-test and find a CI for the difference in the means of two populations.
Know how this test differs from the matched pairs test
Take a look at limitations (Robustness) pg. 451
Chapter 18: Inference about a population proportion
Know how to run a one-sample proportion z-test and find a CI for a single population
proportion. (know which ps to use to check assumptions)
Be able to determine sample size for a desired margin of error and a given level of
confidence using a given p̂ or an unknown p*.
Chapter 19: Comparing two proportions
Know how to run a two-sample proportion z-test and find a CI for the difference between
two population proportions. (know which ps to use to check assumptions)
Chapter 20: The Chi-square test
Know the chi-square goodness of fit test and when to use this. (testing fit of a given model or
model of equal proportions)
Know the chi-square test for in dependence and when to use this. (testing independence of
two categorical variables … usually seen in tabular form)
Chapter 21: Inference for regression
Be able to use minitab output to do hypothesis testing for beta, the population slope.
Know how to look at residual plots to assess fit of linear model and constant variation
assumptions. (histogram or stem & leaf of residuals to assess assumption of normality)
Know the difference between a CI and a PI and where to find them on the minitab output.
Chapter 22: One-way ANOVA
Be able to carry out an ANOVA test (F test) using an ANOVA table.
Be able to fill in an ANOVA table.