Download AP Statistics - Clear Creek ISD

AP Statistics
Syllabus
Course Overview
Advanced Placement Statistics acquaints students with the major concepts and tools for collecting,
analyzing, and drawing conclusions from data. Students will work on projects involving the hands-on
gathering and analysis of real world data. Ideas and computations presented in this course have
immediate links and connections with actual events. Computers and calculators will allow students to
focus on the concepts involved in statistics. This course prepares students for the Advanced Placement
examination in Statistics.
Texts and Resources: Bock, Velleman, De Veaux. Stats - Modeling the World, 2nd Edition,
Pearson Incorporated, 2007.
Supplemental Texts
Yates, Moore, McCabe. The Practice of Statistics, 2nd edition, W.H. Freeman and Company,
1999
Other Resources
Technology
TI – 83+ caculator
ActivStats software
Computer Labs
Internet Resources for AP Statistics
AP Statistics list serve
Interactive Sites
Rice Virtual Lab in Statistics
http://www.ruf.rice.edu/~lane/stat_sim/index.html
Java applets that illustrate: sampling distributions (wow!), mean & median,
normal approximation to the binomial, components of r, regression by eye, and
repeated measures (independent vs. correlated
t-tests).
Webster West's Java Applets
http://www.stat.sc.edu/~west/javahtml
Histogram applet on Old Faithful data; also regression, confidence intervals, Let's
Make a Deal, and power of a test.
Java Demos for Probability and Statistics
http://www.math.csusb.edu/faculty/stanton/m262/probstat.html
Virtual Laboratories in Probability and Statistics http://www.math.uah.edu/stat
Statistics Home Pages
Al Coons at Buckingham, Brown, & Nichols
http://www.bbns.org/us/math/ap_stats
Daren Starnes' Home Page
http://www.webb.org/math/starnes/ap_statistics.htm
Sanderson Smith’s Page (Herkimer’s Hideaway)
http://www.cate.org/sms99/apst99/stathmp.htm
Page 1 of 21
John Burnette’s Page (Kincaid School)
http://www.kinkaid.org/~jburne/OldFiles/stats_index.html
Assessment
Brian Schott's Database of Questions at Ga. State
http://www.gsu.edu/~dscbms/ibs/ibsd.html
John Burnette's Sample Test Questions
http://www.kinkaid.org/~jburne/
Sources for data
StatLib
http://lib.stat.cmu.edu
U.S. Bureau of the Census Home Page
http://www.census.gov/
Gallup Poll Home Page
http://www.gallup.com/
CUWU Statistics Data Applet
http://www.stat.uiuc.edu/~stat100/cuwu/
Exploring Data
http://exploringdata.cqu.edu.au/
.
Free Software
WinStat by Rick Parris, Phillips Exeter
http://www.exeter.edu/rparris/default.html
This guy is a wizard. His software is free, and will do about anything statistical
you can think of. Better yet, he updates it regularly.
Professional Organizations
American Statistical Association
College Board
National Council of Teachers of Mathematics
http://www.amstat.org/
http://www.collegeboard.org/
http://www.nctm.org/
On-Line Textbooks
HyperStat Online
http://davidmlane.com/hyperstat/index.html
Complete with links to other sites on the web.
Other Sites
Chi square
http://www.mste.uiuc.edu/patel/chisquare/keyprob.html
Ken White's Coin Flipping Page
http://shazam.econ.ubc.ca/flip/index.html
STEPS Statistics glossary
http://www.cas.lancs.ac.uk/glossary_v1.1/main.html
Page 2 of 21
Course Outline
Weeks
1-2
Resources, Assessments, and
Strategies
Unit of Study, Topics, and Student Objectives
Organizing Data: Looking For Patterns and Departures from Patterns
Topics:
 Exploring Data
 Displaying Distributions with Graphs
 Describing Distributions with Numbers
Objectives:
 Gather information from a variety of sources; organize, sort, and categorize the
information; interpret resulting data; make inferences and draw conclusions from
relationships and patterns that emerge; communicate what has been learned.Gaining
specific facts, ideas, vocabulary
 Graphical displays of distributions of univariate data: dot plots, stem-and-leaf plots,
histograms (Center and spread, Clusters and gaps, Outliers and other unusual features,
Shape and skewness);
 Summarizing distributions of univariate data (Measuring central tendency -- mean,
median, mode, Measuring spread -- range, inter quartile range, standard deviation,
Measuring position -- quartiles, percentiles, Box-and-whiskers plots, Changing units and
standardizing scores);
 Comparing distributions of univariate data (dot plots , back-to-back stem-and-leaf plots,
parallel box-and-whiskers plots)
Page 3 of 21
Resources:
 Internet
 Text
 handouts
Assessments:
 Chapter 1 Test
 Quizzes (2)
 Prep for Exam from Golden Binder
 Homework – odd numbered problems
each section as normal assignment
Labs & Other Strategies:
 Lab – Pulse Rates
 Have each person describe themselves
with 2 words and 2 numbers. Object:
Complex objects and phenomenon are
frequently described with a few
numbers.
 Find the mean, median, and mode in
simulated sets of data with both odd
and even numbers of data points.
 Draw box and whiskers plots both on
paper and with TI-83 calculators using
simulated data
3-4
The Normal Distributions
Resources:
 Internet
 Text
 handouts
Topics:
 Density Curves
 Normal Distributions
 Standard Normal Calculations
 Properties of Normal Distributions
 Using as a model for measurements
 Assessing normality
 Using normal probability tables
Assessments:
 Chapter 2 Test
 Quiz (2)
 Prep for Exam from Golden Binder
 Homework – odd numbered problems
each section as normal assignment
Objectives:
 locate median and mean on a density curve
 recognize symmetric, left-skew, and right-skew distributions
 Use the Empirical Rule
 compute the standardized vale of an observation
 use the table or the calculator to calculate the proportion of values above (or below) a
stated number
 calculate a point having a stated proportion of all values above it
 to be able to assess normality
Page 4 of 21
Labs & Other Strategies:
 Lab- Rolling a Normal Distribution
 Lab- A Fine Grained Distribution
 Looking at Normal Distributions in test
scores, heights, weights.
5-6
Examining Relationships
Resources:
 Internet
 Text
 handouts
Topics:
 Scatterplots
 Correlation
 Least-Squares Regression
 Analyzing patterns in scatterplots
 Correlation and linearity
 Median-median line
 Residual plots
 Least Squares line
 Outliers and influential points
 Re-expression of data
 Fitting models to data
Assessments:
 Chapter 3 Test
 Quizzes (3)
 Prep for Exam from Golden Binder
 Homework – odd numbered problems
each section as normal assignment
Labs & Other Strategies:
 Lab- Making SAT/ACT Comparisons
 Analyzing Newspaper Scatterplots
Objectives:
 Identify explanatory and response variables
 Make a scatterplot, and be able to include a categorical variable
 Describe the form, direction, and strength of the overall pattern of a scatterplot, as well as
outliers and potentially influential observations
 Find and know the basic properties of correlation
 Explain slope and intercept from a linear regression equation
 Find the least-squares regression line with a calculator
 Use a regression line for prediction. Recognize extrapolation and be aware of its dangers
 Use r^2 to describe how much of the variation in one variable can be accounted for by a
straight-line relationship with another variable
 Calculate and plot residuals and recognize unusual patterns.
Page 5 of 21
7-8
More on Two-Variable Data
Resources:
 Internet
 Text
 handouts
Topics:
 Modeling Non-Linear Data
 Interpreting Correlation and Regression
 Relations in Categorical Data
Objectives:
 recognize that exponential growth (or decay) results when a variable is multiplied by a
fixed number greater than 1 (or positive number less than 1) in each time period
 recognize that a power function is the result of one variable being proportional to a power
of a second variable
 Use semi-log transformations to linearize exponential data
 use log-log transformations to linearize power relationships
 Use least-squares regression on the transformed points, and inverse transformations to
produce a curvilinear model for the original points
 Plotting residuals for the transformed data against a fitted line makes it easier to
determine deviations from the overall pattern
 understand that both r and the least-squares regression line can be strongly influenced by
a few extreme observations
 recognize possible lurking variables that may explain the observed association between
two variables
 understand that even a strong correlation does not mean that there is a cause-and-effect
relationship between x and y
 from a two-way table of counts, find the marginal distributions of both variables
 express any distribution in percents by dividing the category counts by their total
 describe the relationships between two categorical variables by computing and comparing
percents, usually by comparing the conditional distributions of one variable for the
different categories of the other variable
Page 6 of 21
Assessments:
 Chapter 4 Test
 Quizzes (3)
 Prep for Exam from Golden Binder
 Homework – odd numbered problems
each section as normal assignment
Labs & Other Strategies:
 Lab- Designing a Game of Chance
 Lab- Looking at Annual Percentage
Rates
 Lab- Flu Epidemic (Spreading Rumors)
9-10
Producing Data
Topics:
 Designing Samples -Simple random sampling, Methods of data collection, Planning and
conducting surveys, Questionnaire design
 Designing Experiments - Stratifying to reduce variation, Planning and conducting
experiments, Randomized comparative experiments, Blocking to reduce variation
 Simulating Experiments
Objectives:
 identify the population in a sampling situation
 recognize bias due to voluntary response samples and other inferior sampling methods
 use a table of random digits or a calculator to select a simple random sample (SRS) from
a population.
 recognize the presence of under-coverage and non-response as sources of error in a
sample survey. recognize the effect of the wording of questions on the response.
 Use random digits to select a stratified random sample from a population when the strata
are identified
 recognize whether a study is an observational study or an experiment.
 recognize bias due to confounding of explanatory variables with lurking variables in either
a study or an experiment.
 Identify the factors, treatments, response variables, and experimental units or sub jects in
an experiment.
 outline the design of a completely randomized experiment using a diagram to include the
size of the groups, the specific treatments, and the response variable.
 Use a table of random digits to carry out the random assignment of subjects to groups in a
completely randomized experiment.
 recognize the placebo effect.
 recognize when double-blind techniques should be used.
 Explain why a randomized comparative experiment can give good evidence for causeand-effect relationships.
 Recognize that many random phenomena can be investigated by means of a carefully
designed simulation.
 Run a simulation
 Use a random number table, a calculator, or software to conduct simulations.
Page 7 of 21
Resources:
 Internet
 Text
 handouts
Assessments:
 Chapter 5 Test
 Quizzes (3)
 Prep for Exam from Golden Binder
 Homework – odd numbered problems
each section as normal assignment
Labs & Other Strategies:
 Lab- A Class Survey
 Lab- Modifying A Class Survey
 Lab- Coke vs Pepsi The Real
Challenge
 Using Die, Coins, Cards to Simulate
 Project- “Survey Project” (a sample of
the student handout is included on
page 18.
11-12
Probability: The Study of Randomness
Resources:
 Internet
 Text
 handouts
Topics:
 Randomness
 Probability Models
 More about Probability
 Multiplication, addition, and complement principles
 Probability of events occurring together
 Conditional probability
 Independent and mutually exclusive events
Assessments:
 Chapter 6 Test
 Quizzes (3)
 Prep for Exam from Golden Binder
 Homework – odd numbered problems
each section as normal assignment
Objectives:
 Describe the sample space of a random phenomenon. For a finite number of outcomes,
use the multiplication principle to determine the number of outcomes, and use counting
techniques, Venn diagrams, and tree diagrams to determine simple probabilities
 For the continuous case, use geometric areas to find probabilities (areas under simple
density curves) of events (intervals on the horizontal axis)
 Know the probability rules and be able to apply them to determine probabilities of defined
events. In particular, determine if a given assignment of probabilities is valid.
 Determine if two events are disjoint, complementary, or independent. Find unions and
intersections of two or more events.
 Know the general addition rule for the union of two events, and define joint probability.
Apply these characterizations to solve problems.
 Define conditional probability and use the definition to find conditional probabilities of
events.
 Use the multiplication rule to find the joint probability of two events.
 construct tree diagrams to organize the use of the multiplication and addition rules to
solve problems with several states.
Page 8 of 21
Labs & Other Strategies:
 Using Cards, Die, Coins and Calculator
 Lab- The Spinning Wheel
13-14
Random Variables
Resources:
 Internet
 Text
 handouts
Topics:
 Discrete and Continuous Random Variables
 Means and Variances of Random Variables
Objectives:
 Recognize and define a discrete random variable, and construct a probability distribution
table and a probability histogram for the random variable
 Recognize and define a continuous random variable, and determine probabilities of
events as areas under density curves
 Given a normal random variable, use a table or calculator to find probabilities of events as
areas under the stand normal distribution curve.
 Calculate the man and variance of a discrete random variable. Find the expected payout
in a raffle or similar game of chance.
 use simulation methods and the law of large numbers to approximate the mean of a
distribution.
 Use rules for mans and rules for variances to solve problems involving sums and
differences of random variables.
Page 9 of 21
Assessments:
 Chapter 7 Test
 Quizzes (2)
 Prep for Exam from Golden Binder
 Homework – odd numbered problems
each section as normal assignment
Labs & Other Strategies:
 Lab- The Game of Craps
 Project- Write a program to play the
game of Craps
15-16
Binomial and Geometric Distributions
Topics:
 Binomial Distributions - Binomial Probabilities, Binomial Formulas, Simulating Binomial
Experiments, Mean and Standard Deviation of a Binomial Random Variable
 Geometric Distributions - Geometric Probabilities, Mean of a Geometric Random Variable
Objectives:
 identify parameters and statistics in a sample or experiment.
 Recognize the fact of sampling variability: a statistic will take different values when you
repeat a sample or experiment.
 Interpret a sampling distribution as describing the values taken by a statistic in all possible
repetitions of a sample or experiment under the same condition.
 Describe the bias and variability of a statistic in terms of the man and spread of its
sampling distribution.
 Understand that the variability of a statistic is controlled by the size of the sample (larger
samples are less variable).
 Recognize when a problem involves a sample proportion, p-hat.
 Find the mean and standard deviation of a sample proportion for an SRS of size n from a
population having population proportion pi.
 Know that the standard deviation of the sampling distribution of p-hat gets smaller at the
rate of the square root of n, as n gets larger.
 Recognize when you can use the normal approximation to the sampling distribution of phat. Use the normal approximation to calculate probabilities that concern p-hat.
 Recognize when a problem involves the mean x-bar of a sample.
 Find the mean and standard deviation of a sample mean x-bar from an SRS of size n
when the mean mu and standard deviation sigma of the population are known.
 Know that the standard deviation of the sampling distribution of x-bar gets smaller at the
rate of the square root of n as the sample size n gets larger.
 Understand that x-bar has approximately a normal distribution when the sample is large
(central limit theorem). Use this normal distribution to calculate probabilities that concern
x-bar.
 Use the law of large numbers to interpret the population mean mu as the average of an
indefinitely large number of observations drawn from the population
Page 10 of 21
Resources:
 Internet
 Text
 handouts
Assessments:
 Chapter 8 Test
 Quizzes (2)
 Prep for Exam from Golden Binder
 Homework – odd numbered problems
each section as normal assignment
Labs & Other Strategies:
 Lab- A Gaggle of Girls
 Activity- Simulations of Binomial and
Geometric Settings
17-18
Sampling Distributions
Topics:
 Sampling Distributions - Parameter vs. Statistic, Bias and Variability,
 Sample Proportions – Means and Standard deviation of sample proportions
 Sample Means – Means and Standard deviation of sample means
 Sample Means from a Normal Population
 Central Limit Theorem;
 Law of Large Numbers
Objectives:
 identify parameters and statistics in a sample or experiment.
 Recognize the fact of sampling variability: a statistic will take different values when you
repeat a sample or experiment.
 Interpret a sampling distribution as describing the values taken by a statistic in all possible
repetitions of a sample or experiment under the same condition.
 Describe the bias and variability of a statistic in terms of the man and spread of its
sampling distribution.
 Understand that the variability of a statistic is controlled by the size of the sample (larger
samples are less variable).
 Recognize when a problem involves a sample proportion, p-hat.
 Find the mean and standard deviation of a sample proportion for an SRS of size n from a
population having population proportion pi.
 Know that the standard deviation of the sampling distribution of p-hat gets smaller at the
rate of the square root of n, as n gets larger.
 Recognize when you can use the normal approximation to the sampling distribution of phat. Use the normal approximation to calculate probabilities that concern p-hat.
 Recognize when a problem involves the mean x-bar of a sample.
 Find the mean and standard deviation of a sample mean x-bar from an SRS of size n
when the mean mu and standard deviation sigma of the population are known.
 Know that the standard deviation of the sampling distribution of x-bar gets smaller at the
rate of the square root of n as the sample size n gets larger.
 understand that x-bar has approximately a normal distribution when the sample is large
(central limit theorem). Use this normal distribution to calculate probabilities that concern
x-bar.
 use the law of large numbers to interpret the population mean mu as the average of an
indefinitely large number of observations drawn from the population.
Page 11 of 21
Resources:
 Internet
 Text
 handouts
Assessments:
 Chapter 9 Test
 Quizzes (3)
 Prep for Exam from Golden Binder
 Homework – odd numbered problems
each section as normal assignment
Labs & Other Strategies:
 Lab- Sampling Pennies
 Lab- The Distribution of Height
19-20
Introduction to Inference
Topics:
 Estimating with Confidence - Logic of confidence intervals,
 Tests of Significance - null and alternative hypotheses, p-values, one and two-sided tests
 Using Significance Tests
 Inference as Decision - Type I and Type II errors, power against the alternative
Objectives:
 State in non-technical language what is meant by "95% confidence." Interpret confidence
level as well as the interval.
 Calculate a confidence interval for the mean mu of a normal population with known
standard deviation sigma.
 Recognize when you can safely use the confidence interval formula and when the sample
design or a small sample from a skewed population makes it inaccurate.
 Understand how the margin of error of a confidence interval changes with the sample size
and the level of confidence C.
 Find the sample size required to obtain a confidence interval of specified margin of error
m when the confidence level and other information are given.
 Determine what Type I and Type II errors are in the context of a situation. Recognize that
for a significance level alpha, alpha is the probability of a Type I error, and the power
against a specific alternative is 1 minutes the probability of a Type II error for that
alternative.
 Recognize that increasing the size of the sample increases the power (reduces the
probability of Type II error) when the significance level remains fixed.
 State the null and alternative hypotheses in a testing situation when the parameter in
question is a population mean mu.
 Explain in non-technical language the meaning of the P-value when you are given th4e
numerical value of p for a test.
 Calculate the z statistic and the P-value for both one-sided and two-sided tests about the
mean mu of a normal population.
 Assess statistical significance at standard levels alpha, either by comparing p to alpha or
by comparing z to standard normal critical values.
 Recognize that significance testing does not measure the size or importance of an effect.
 Recognize when you can use the z test and when the data collection design or a small
sample from a skewed population makes in appropriate.
Page 12 of 21
Resources:
 Internet
 Text
 handouts
Assessments:
 Chapter 10 Test
 Quizzes (4)
 Prep for Exam from Golden Binder
 Homework – odd numbered problems
each section as normal assignment
Labs & Other Strategies:
 Lab- Pick a Card
 Lab- “Thumbtacks” up or down and
their proportions
21-23
Inference for Distributions
Resources:
 Internet
 Text
 handouts
Topics:
 Inference for the Mean of a Population
 Comparing Two Means
 t-distributions;
 t-confidence intervals and tests;
 matched pairs t-procedures;
 choosing sample size
 comparing two means
 two-sample t-procedures;
 robustness;
 technology for more accurate levels in the t-procedures;
 pooled two-sample t-procedures;
Assessments:
 Chapter 11 Test
 Quizzes (2)
 Prep for Exam from Golden Binder
 Homework – odd numbered problems
each section as normal assignment
Objectives:
 Recognize when a problem requires inference about a mean or a proportion.
 Recognize from the design of a study whether one-sample, matched pairs, or a single
proportion procedure is needed.
 use the t-procedures to obtain a confidence interval at a stated level of confidence for the
mean mu of a population.
 Carry out a t-test for the hypothesis that a population mean mu has a specified value
against either a one-sided or a two-sided alternative.
 recognize when the t-procedures are appropriate in practice, in particular that they are
quite robust against lack of normality but are influenced by outliers.
 Also recognize when the design of the study, outliers, or a small sample from a skewed
distribution make the t-procedures risky.
 Recognize matched pairs data and use the t-procedures to obtain confidence intervals
and to perform tests of significance for such data.
 Give a confidence interval for the difference between two means. Use the two-sample t
statistic with conservative degrees of freedom or the calculator.
 Recognize when the two-sample t procedures are appropriate in practice.
Page 13 of 21
Labs & Other Strategies:
 Lab- Paper Airplane Experiment
 Lab- Dominant & Non-Dominant Hand
Activity
 Lab- Pulse Rates between male and
female students
24-25
Inference for Proportions
Resources:
 Internet
 Text
 handouts
Topics:
 Inference for a Population Proportion
 Comparing Two Proportions
 assumptions for inference about a proportion
 z procedures;
 choosing sample size;
 the sampling distribution of p-hat 1 minus p-hat 2;
 confidence intervals for pi 1 minus pi 2;
 significance tests for pi 1 minus pi 2.
Assessments:
 Chapter 12 Test
 Quizzes (2)
 Prep for Exam from Golden Binder
 Homework – odd numbered problems
each section as normal assignment
Objectives:
 Calculate from sample counts the sample proportion that estimates the population
proportion.
 Use the z procedure to give a confidence interval for a population proportion pi.
 Use the z statistic to carry out a test of significance for the hypothse about a population
proportion pi against either a one-sided or a two-sided alternative.
 Check that you can safely use z procedures in a particular setting.
 Test the hypothesis that two populations have equal means against either a one-sided or
two-sided alternative. use the two-sample t test with conservative degrees of freedom or
[preferably] a calculator or software.
 use the two-sample z procedure to give a confidence interval for the difference pi 1 - pi 2
between proportions in two populations based on independent samples from the
population.
 use a z statistic to test the hypothesis that proportions in two distinct populations are
equal.
check that you can safely use 2-proportion z procedures in a particular setting.
Page 14 of 21
Labs & Other Strategies:
 Lab- Is One Side of a Coin Heavier?
 Lab- Testing M&M proportions
26-27
Inference for Two-Way Tables: Chi Square
Topics:
 Inference for Two-Way Tables
 Test for Goodness of Fit
 test for goodness of fit;
 chi-square distributions;
 expected counts;
 inference for two-way tables:
 a single SRS with each individual classified according to both of two categorical variables;
 an entire population, with each individual classified according to both of two categorical
variables
Objectives:
 For goodness of fit, calculate expected counts for each category in a distribution, the chisquared statistic, and the p-value.
 State null and alternative hypotheses for a difference between two distributions.
 If the test is significant, use components of the chi-square statistic to identify the most
important deviations between observed and expected counts.
 Arrange data on successes and failures in several groups into a two-way table of counts
of successes and failures in all groups.
 Use percents to describe the relationships between two categorical variables starting from
the counts in a two-way table.
 Locate expected cell counts, the chi-square statistic, and its P-value in output from
software or calculator.
 Explain what null hypothesis the chi-square statistic tests in a specific two-way table.
 If a test of independence is significant, use percents, comparison of expected and
observed counts, and the components of the chi-square statistic to see what deviations
from the null hypothesis are most important.
 In doing the chi-square test, calculate the expected count for any cell from the observed
counts in a two-way table.
 Calculate the component of the chi-square statistic for any cell, as well as the overall stat
 Give the degrees of freedom of a chi-square statistic.
 Use the chi-square critical values from a table to approximate the P-value of a chi-square
test.
Page 15 of 21
Resources:
 Internet
 Text
 handouts
Assessments:
 Chapter 13 Test
 Quizzes (2)
 Prep for Exam from Golden Binder
 Homework – odd numbered problems
each section as normal assignment
Labs & Other Strategies:
 Lab- “I Didn’t Get Enough Blues!”
 What type of distribution is most useful
for evaluating surveys for voting or yes
or no questions?
 Why does the binomial distribution start
to look like a normal distribution when
the sample size is large?
28-29
Inference for Regression
Resources:
 Internet
 Text
 handouts
Topics:
 Inference about the Model
 Inference about Prediction
 Checking the Regression
 Assumptions
Objectives:
 Assumptions for inference:
 SINR
 [S: Standard deviation of responses about the true line is the same for each x]
 [I: Independent observations]
 [N: Normal distribution of responses about the line [check residuals for normality!]
 [R: Randomly selected observations]
 calculate standard error s about the line
 calculate confidence interval for regression slope
 test the hypothesis of no linear relationship [test for regression slope]
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Assessments:
 Chapter 14 Test
 Quizzes (3)
 Prep for Exam from Golden Binder
 Homework – odd numbered problems
each section as normal assignment
Labs & Other Strategies:
 Labs- The Legacy of Jet Skis
 Labs- ACT/SAT Scores
 Activity- “Leaning Tower of Pisa”
30-34
AP Exam Review
Resources:
 Internet
 Text
 handouts
Topics:
 Review 1st Semester
 Review 2nd Semester
Objectives:
 Complete and grade practice exams
 Learn Grading processes for AP Exam
 Learn Testing Strategies for Multiple Choice, Short Answer, and Essay
 Scoring Guidelines for AP Exams
 Rubric of Scoring Guidelines
Labs & Other Strategies:
 Hints and Reminders about AP Exam
 Key Phrases to Remember
 Common Mistakes of Students
 Review Project – Students split into
groups and prepare an oral
presentation and a written assessment
over assigned topics from throughout
the year.
Resources:
 Internet
 Text
 handouts
Final Project
Topics:

Review 1st Semester

Review 2nd Semester
Assessments:

Objectives:

Assessments:
 Previous AP Exams
 Quizzes (Previous AP Questions)
 Prep for Exam from Golden Binder
 Homework – odd numbered problems
each section as normal assignment
Demonstrate ability to combine topics from throughout the course in order to effectively
apply statistical procedures to a real-world topic.
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A copy of both the student handout
and a simplified grading rubric
from Stats: Modeling the World is
provided on pages 19-21.
Statistics Group Survey Project
Group Members:
________________________
________________________
________________________
The Survey must:
 Have an appropriate Parameter it is attempting to investigate.
 Include at least 5 questions
 Have at least 50 respondents.
 Use an appropriate form of random sampling.
The Written Report must include:
 Copy of your survey
 Copy of your results.
 Description and analysis of your sampling method.
 Discussion of your Population, Parameter, and Sampling Frame
 Discussion of any potential forms for bias affecting your sample.
 Discussion of any recommended ways to enhance/improve your
survey design.
The Oral Report must include:
 between 2-5 minutes in length
 Visual Aid
o neat
o clearly visible for entire class during presentation
 All topics covered in the written report must be included.
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