Stat 1040, Section 002:
Study Guide for the Final (Spring 2009)
Final Exam Date
Your final exam is scheduled for
Tuesday, April 28, 2009, 7:30am to 9:20am.
The final exam will be held in: BUS 319 (our regular classroom).
The final exam is worth 40% of your final grade.
Review Session
A review session for Section 002 will be held
at the following place and time:
-
Monday, April 27, 2009, 5:00pm to 7:00pm, Room to be announced.
Note that final exams from previous
semesters can be found in the Stat 1040 workbook. Solutions for these
final exams and final exams from earlier semesters
can be found on the course Web site at
http://www.math.usu.edu/~symanzik/teaching/2009_stat1040/stat1040.html.
Final Exam Outline
The final exam is comprehensive and
will cover material from Chapters 1 through 29
of the Freedman, Pisani, and Purves book.
Chapter 7, Chapter 11: Sections 4 and 5, Chapter 15,
Chapter 22, Chapter 24, Chapter 25,
and the "finite population correction factor"
will not be examined in the final exam.
You will be given about 110 minutes to complete the final exam. The exam
will be a closed-book exam, which means that you are not allowed to
use the textbook or the lecture notes. All required tables (such
as for the normal curve) will be provided and required
formulas will be listed on the final exam. You should bring your
calculator!
You will not be allowed to use your cell phone (either for
phone calls, text messaging, or as a calculator) during the final exam.
Any violation of this policy will automatically result in a score
of zero for the final exam.
To prepare for the final exam, you should solve old exam questions,
review questions, and regular exercises from the textbook.
During the review session, it is
planned to discuss selected questions from old exams.
Make sure that you are familiar with the keywords and concepts
listed below in the Chapter Contents.
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-Square Test:
Chi-Square test, Chi-Square statistic, Chi-Square table,
Chi-Square test used for checking consistency of data with a
chance model,
Chi-Square test used for testing independence
- Chapter 29 - A Closer Look at Tests of Significance:
significance of result, many tests, data snooping,
is a result important?
Note that even if a keyword is only listed once, it may be related
to more than one chapter (e.g., confounding or SD).
Topics in previous Stat 1040 Final Exams
Below is a summary of questions asked in Stat 1040 exams between
Fall 1998 and Fall 2000, as published on the course Web page.
The number in parentheses indicates how many questions in this set of exams
were related to a particular topic.
- 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)
This summary should only provide you with a rough estimate of the chance
that a particular topic will be asked in the final exam (please
check yourself that we did not miss any topics in this summary). There is absolutely
no guarantee that a topic that has not been asked before
does not show up in our final exam this semester. In particular, note
how many topics have been asked in only one exam - so there is
actually a high chance that our final exam also contains some topic(s) that
have not been asked before. Just studying the old questions from the workbook
and the course Web page will not be enough for a very good grade.