AP Statistics Syllabus:
Course Description:
From the College Board:
The purpose of the AP course in statistics is to introduce students to the major concepts and tools for collecting,
analyzing, and drawing conclusions from data. Students are exposed to four broad conceptual themes:
1. Exploring Data: Describing patterns and departures from patterns
2. Sampling and Experimentation: Planning and conducting a study
3. Anticipating Patterns: Exploring random phenomena using probability and simulation
4. Statistical Inference: Estimating population parameters and testing hypotheses
Students who successfully complete the course and exam may receive credit, advanced placement, or both for a onesemester introductory college statistics course.
Instructor Remarks:
In addition to the course description offered by the College Board, you can expect to be challenged to think about
Mathematics in a new way. Although theory is an important part of Statistics, the heart of what you will be expected to
do this year, will be application of theory. You are expected to be an active participant in the learning process which can
include lectures, discussions, presentations, guest speakers, and field trips.
The course will integrate the TI-84 Calculator and Computer Programs such as Microsoft Excel and Microsoft Word
extensively. Additionally, successful completion of the course will involve extensive reading and writing activities, many
of which will need to be completed outside of the traditional school day. These reading and writing activities will be
drawn from a number of resources such as scientific journals, online reports, news articles, etc. As you can well imagine,
understanding vocabulary and using the new vocabulary in your course communication is essential.
Evaluation/Assessment/Grading:
Grading System:
The course will use the school’s grading weighting system for AP Classes. Additionally, all practice Free Response
Questions (FRQs) will be graded using the College Board’s AP Statistics Rubric (pages 29 and 30 of the AP Statistics
Course Description). The formulas and charts (pages 13 through 19 of the AP Statistics Course Description) provided by
the College Board for the exam can and should be used for every exam and lab.
Description:
Assessments (Tests, Quizzes,
Labs)
Homework (Including Reading
Guides)
Percentage:
90%
10%
Laboratory Assignments:
“Labs” will be an essential part of your learning for the course and will be weighted as such. When completing the labs,
please remember that although the lab is meant as either an opportunity for you to discover or demonstrate
competency with a new concept, you can and should relate the lab to previously learned materials. Proper grammar,
spelling, and acceptable formatting are expected.
“Labs” will often be holistic in nature. This means that you will have an opportunity to show competency in all aspects of
the course:
1. An experiment, observation, study, survey, etc. will need to be designed and executed in a manner to minimize
bias.
2. Information from a sample will be collected through techniques such as observation, simulation,
experimentation, etc.
3. Data will be summarized graphically, symbolically, and with the written word using formal statistic vocabulary.
4. Inferential Statistics will be used to draw conclusions on a population when information is collected from a
sample.
5. Conclusions will be written in a manner to demonstrate an understanding of the topic explored in the lab,
including derivations from expected results.
It is the expectation of the instructor that the quality of graded work increases as the course progresses as more
statistical techniques become available to you.
Reading Guides:
Reading guides are extended homework assignments which allow you to preview the course material before the teacher
lectures or demonstrate the material. It is essential that the guides are completed before we work on the material in
class. The guides will also list the expected homework problems and other resources to enhance your understanding of
the material. It is expected that you do your homework problems in the space provided on the study guide, thereby
allowing you to have a great review resource as we approach the test in May.
Textbooks/Online Resources:
Primary Textbook and Resources:
1. Starnes, Daren S., Daniel S. Yates, and David S. Moore. The Practice of Statistics. New York: W.H. Freeman,
2012. Print.
2. "Against All Odds: Inside Statistics." Against All Odds: Inside Statistics. Annenberg Media, 2009. Web.
25 May 2010. <http://www.learner.org/resources/series65.html>.
Secondary Textbooks/Resources:
1. Sternstein, Martin. Barron's AP Statistics. Barrons Educational Series Inc, 2012. Print.
2. "Advanced Placement Statistics Exam." StatTrek. StatTrek.com, 2009. Web. 25 May 2010.
<http://www.stattrek.com/AP/Overview.aspx>.
Course Sequence: (Labs are subject to change)
Unit Name and
Number:
Important Topics:
Chapter 1 Exploring
Data
Analyzing Categorical Data
Displaying Quantitative Data with Graphs
Describing Quantitative Data with Numbers
Describing Location in a Distribution
Normal Distributions
Chapter 2 Modeling
Distributions of
Data
Chapter 3 Describing Scatterplots and Correlation
Relationships
Least-Squares Regression
Chapter 4 Designing Sampling and Surveys
Studies
Experiments
Using Studies Wisely
Chapter 5
Randomness, Probability, and Simulation
Probability
Probability Rules
Conditional Probability and Independence
Chapter 6 Random
Discrete and Continuous Random Variables
Variables
Transforming and Combining Random
Variables
Binomial and Geometric Random Variables
Chapter 7 Sampling
What Is a Sampling Distribution
Distributions
Sample Proportions
Sample Means
Chapter 8
Confidence Intervals: The Basics
Estimating with
Estimating a Population Proportion
Confidence
Estimating a Population Mean
Chapter 9 Testing a
Significance Tests: The Basics
Claim
Tests about a Population Proportion
Tests about a Population Mean
Approximate Common Assessment Name
Number of
and Title:
Days:
12
Unit Test
15
Unit Test/Sports Statistics
Lab (Chapters 1 and 2)
13
Unit Test/Vietnam Lab
19
Unit Test/Energy Drink Lab
17
Unit Test/Counting Rules Lab
13
Unit Test
8
Unit Test/Simulation Labs
10
Unit Test/Confidence
Intervals through Simulation
Lab
10
Unit Test/Benford's Law Lab
Chapter 10
Comparing Two
Populations or
Groups
Chapter 11
Inference for
Distributions of
Categorical Data
Chapter 12 More
about Regression
Review for Exam
Comparing Two Proportions
Comparing Two Means
9
Unit Test/Shopping Labs
Chi-Square Goodness-of Fit Tests
Inferences for Relationships
8
Unit Test
Inference for Linear Regression
Transforming to Achieve Linearity
5
Unit Test
10
Daily Quizzes
AP Stat Topic Outline
Theme:
Theme 1: Exploring Data:
Describing Patterns and
Departures from Patterns
(20-30% of Exam)
Overview: Statistics is the
science of finding patterns
in data. Often this data
needs to be organized
and/or summarized before
any sense can be made of
it. We can organize data
simply by putting it in order
from least to greatest.
More often it makes sense
to display it graphically or
to summarize it by stating
its "measures of center".
Centers don't always tell
the whole story, so
measures of dispersion or
rank can be used as well.
Graphical displays,
measures of center, and
measures of dispersion can
also be used to compare
and contrast multiple data
sets. Additionally, bivariate
data can be examined on
the Cartesian Coordinate
plane to look for
correlation; however, it is
important to keep in mind,
that correlation is not
equivalent to causation.
What will I Learn
About?
Book
Sections
A. Constructing and
interpreting graphical
displays of distributions
of Univariate data
(dotplot, stem plot,
histogram, cumulative
frequency plot)
1. Center and Spread
1.2
2. Clusters and Gaps
3. Outliers and Unusual
Features
4. Shape
1.2
1.2
B. Summarizing
Distributions of
Univariate Data
1. Measuring Center:
Median, Mean
2. Measuring Spread:
Range, IQR, Standard
Deviation
3. Measuring Position:
Quartiles, Percentiles,
Z-Scores
4. Using Boxplots
5. The Effect of
Changing Units on
Summary Measures
1.3
1. Comparing Center
and Spread
1.2,1.3
2. Comparing Clusters
and Gaps
3. Comparing Outliers
and Unusual Features
1.2,1.3
4. Comparing Shape
1.2,1.3
1. Analyzing patterns in
scatterplots
2. Correlation and
linearity
3. Least-squares
regression line
4. Residual plots,
outliers, and influential
points
5. Transformations to
achieve linearity
3.1
1. Frequency tables and
bar charts
2. Marginal and joint
frequencies for twoway tables
1.1
C. Comparing
Distributions of
Univariate Data
(dotplots, back-to-back
stemplots, parallel
boxplots)
D. Exploring Bivariate
Data
E. Exploring Categorical
Data
1.2
1.3
Technology
Competencies
Excel: Mean,
Median, Mode,
Histograms, Sorted
Data TI89: Means,
Medians, Modes,
Sorted Data,
Histograms
Excel: Measures of
Spreads (all) TI84:
Measures of Spreads
(all), Boxplots
1.3, 2.1
1.3
2.1
1.2,1.3
3.1
Excel: Linear
Regression Skills
TI84: Linear
Regression Skills
3.2
3.2
12.2
1.1, 5.2
Excel: Bar Charts
Theme 2: Sampling and
Experimentation: Planning
and Conducting a Study
(10-15%)
Overview: Data needs to be
produced before it is
analyzed. Data can be
produced from a number of
different methods including
censuses, surveys,
experiments, and studies.
We can look at either the
population information or
information from a well
chosen sample (one that
makes every attempt to
remove bias). Proper
techniques must be used in
order to prevent criticism
of research methods and to
increase reliability and
validity of results.
3. Conditional relative
frequencies and
associations
4. Comparing
distributions using bar
charts
5.3
A. Overview of
Methods of Data
Collection
1. Census
4.1
2. Sample Survey
3. Experiment
4. Observational Study
4.1
4.2
4.2
B. Planning and
Conducting Surveys
1. Characteristics of a
well-designed and wellconducted survey
2. Populations, samples,
and random selection
4.1
3. Sources of bias in
sampling and surveys
4.1
4. Sampling methods,
including SRS, stratified
random sampling, and
cluster sampling
C. Planning and
1. Characteristics of a
Conducting Experiments well-designed and wellconducted experiment
2. Treatments, control
groups, experimental
units, random
assignments, and
replication
3. Sources of bias and
confounding, including
placebo effect and
blinding
4. Completey
randomized design
5. Randomized block
design, including
matched pair design
D. Generalizability of
Results and Types of
Conclusions that can be
drawn from
Observational Stuides,
Experiments, and
Surveys
1.1
4.1
4.2
4.2
4.2
4.2
4.2
4.3
Excel: Random
Number Generator
TI84: Random
Number Generator
Theme 3: Anticipating
Patterns: Exploring
Random Phenomena Using
Probability and Simulation
(20-30%)
A. Probability
Overview: Probability is the
branch of mathematics that
deals with the analysis of
random phenomena.
Statisticians have a number
of methods of assigning
probability to events and
the haphazard occurrence
of events tend to disappear
as we look at a large
number of occurrences. The
results of the large number
phenomena, whether
observed or theoretical, can
be summarized in
probability distributions of
data. From these
distributions, it is important
to know how to find simple
and compound
probabilities, expected
B. Combining
values, and standard
Independent Random
deviations.
Variables
C. The Normal
Distribution
D. Sampling
Distributions
1. Interpreting
probability including
long-run relative
frequency
interpretation
2. Law of Large
Numbers
3. Addition Rule,
Multiplication Rule,
Conditional Probability
and Independence
4. Discrete Random
Variables and their
Probability
Distributions, including
Binomial and Geometric
5. Simulation of
Random Behavior and
Probability Distributions
6. Mean (Expected
Value) and Standard
Deviation of a Random
variable, and Linear
Transformation of a
Random Variable
5.1
1. Notion of
Independence vs.
Dependence
2. Mean and Standard
Deviation for sums and
differences of
independent random
variables
6.2
5.2, 5.3
6.1, 6.3,
5.1
TI84: Binomial and
Discrete
Distributions Simple Probabilities,
Compound
Probabilities,
Expected Value,
Standard Deviation
5.1
6.1, 6.2
6.2
2.2
1. Properties of the
Normal Distribution
2.2
2. Using Tables of the
Normal Distribution
2.2
3. Normal Distribution
as a Model for
Measurements
Chapter
7, 8.3,
10.1,
10.2,
11.1
1. Sampling distribution
of a sample proportion
7.3
Excel: Normal
Distribution, Inverse
of Normal
Distribution TI84:
Normal Distribution
Commands
Excel: Sampling
Distributions
Theme 4: Statistical
Inference: Estimating
Population Parameters and
Testing Hypotheses (3040%)
Overview: Statistical
inference is the process of
making conclusions using
data that is subject to
random variation, for
example, observational
errors or sampling
variation. Point estimation
and confidence intervals
are used to estimate
population parameters
when only sample statistics
are available. Hypothesis
testing is used to determine
when the differences
between information
observed from a sample
and the information from
the population are unlikely
to have occurred due to
chance.
A. Estimation (Point
Estimators and
Confidence Intervals)
2. Sampling distribution
of a sample mean
7.3
3. Central Limit
Theorem
4. Sampling distribution
of a difference between
two independent
sample proportions
5. Sampling distribution
of a difference between
two independent
sample means
6. Simulation of
sampling distributions
7. t distribution
8. Chi-square
distribution
10.1
1. Estimating
population parameters
and margins of error
2. Properties of point
estimators, including
unbiasedness and
variability
3. Logic of confidence
intervals, meaning of
confidence level and
confidence intervals,
and properties of
confidence interval
4. Large-sample
confidence interval for
a proportion
5. Large-sample
confidence interval for
a difference between
two proportions
6. Confidence interval
for a mean
7. Confidence interval
for a difference
between two means
(unpaired and paired).
8. Confidence Interval
for the slope of a leastsquares regression line
Commands TI84:
Sampling Distribution
Menus, T Distribution,
Chi-Square
10.2
7.1
8.3
11.1
Chapter
8, 9.3,
10.1,
10.2,
12.1
8.1
8.1
8.2
10.1
8.3
8.3,10.2
12.1
Chapters
9, 11,
10.1,
Excel: Error
Estimates TI84:
Confidence Interval
Menus
10.2,
12.1
B. Tests of Significance
1. Logic of significance
testing, null and
alternate hypothesis, Pvalues, one-and-twosided tests; concepts of
Type I and Type II
errors; concept of
power
2. Large-sample test for
a proportion
3. Large-sample test for
a difference between
two proportions
4. Test for a mean
5. Test for a difference
between two means
(unpaired and paired).
6. Chi-square test for
goodness of fit,
homogeneity of
proportions, and
independence (oneand-two way tables)
7. Test for the slope of a
least-squares regression
line
9.2
10.1
9.3
9.3, 10.2
Chapter
11
12.1
Excel: Significance
Tests through Data
Analysis TI84:
Significance Test
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