AP Statistics Syllabus – Revised June 2011
Course Number 1210320
Text:
 Stats Modeling the World, Bock, Velleman, De Veaux, 2010. Problem
assignment references in the syllabus are to the corresponding section in the text.
Course Design:
 The primary text provides the general design for this course. Students are
required to read the chapters from the textbook before the topics are
discussed in class so that students are more familiar with the topic and more
time can be devoted to investigative activities and less time on lectures.
 Each student will be assigned a TI graphing calculator at the beginning of the year
and it will be their property until the last day of class. If a student elects to use
some other calculator, like the TI-Nspire or TI-89 they are responsible for being
able to use it in a statistical setting.
 In all cases, students will be required to do all calculations (w/o graphing
calculator function) until they have a good understanding what is
involved in the calculation. Once that student has mastered the concepts
involved in the calculation they will be instructed on how to do the same
calculations using the TI-84 function and from that point on the students are free
to answer questions with or w/o a graphing calculator.
 There will be several occasions throughout the year in which students will do
activities involving computer generated reports. These activities will require
students to be able to interpret the results and communicate their finding in a
written report. The emphasis on all homework, classwork, and activity reports
will be on the student’s ability to arrive at the correct conclusion along with
communicating their results in appropriate statistical language.
 Hopefully, by the end of this course, students will realize that writing complete
responses using appropriate justifications is a critical aspect of gaining statistical
proficiency.
 Students will be assigned an investigative task for most chapters. These tasks will
require the students to use, apply and analyze the topics they have learned in that
chapter in a new setting.
 Student progress will be assessed using the standard chapter quizzes, unit tests,
along with grades for homework, activity reports and there will be two major
projects.
 Their midterm grade will be a comprehensive report on the material we covered
in the first half of the year (one or two variable descriptive statistics) and the
second major report will be their final grade and this report should be on the
material we covered in the second half of the year (Inferential Statistics).
Midterm and Final Project:
 Students will collect data or design and conduct an experiment to investigate a
topic of their choosing. The written report should include a title and the following
sections;
o Introduction: Describe your topic to the reader and your motivation for
picking this topic.
o Methodology: How did you gather your data or how the experiment was
conducted?
o State all resources.
o Results: Present the data in table or graph form in such a way that
conclusions can easily be made. Make sure graphs are labeled
appropriately.
o Conclusions: State your conclusions in appropriate statistical terms or
what conclusions can be drawn from your experiment. State any unusual
finding that might cause concern. What was learned from this project?
o Students will be given examples of excellent reports from past students
along will reports that were considered unacceptable, so that students will
have a better idea of what is expected of them. Students will be graded
based on a rubric that they will have before the report is graded.
Primary Textbook References and Resource Material
(Noted with the following letters in the Course outline)
 Bock, Velleman, De Veaux, Stats Modeling the World. 3rd edition. Boston:
Addison-Wesley, 2010. (BVD)
 Yates, Daniel, Moore, David, and Starnes, Daren. The Practice of
Statistics. 2nd edition. New York: W.H.Freeman and Co., 2003. (YMS)
 V Annenber/CPB. Against All Odds: Inside Statistics. 26-30 minute video
clips. Washington D.C.: The Annenburg/CPB Collection. 1989 Videocassettes
www.learner.org Note: not all videos listed are shown during class time.
Often students take the video home to review for a class or test.
 TI Texas Instrument TI-84 graphing calculator.
 Printed Test Bank and Resource Guide (TB) / Golden Binder (GB) are the
ancillary materials provided by the texts BVD, YMS respectively. Both have a
wide variety of activity exercises in which the students are encouraged to explore
some phenomena and come to some type of statistical conclusion about what is
happening. These activities will give students practice in communicating
methods, results and interpretations using the vocabulary of statistics along with
drawing connections between all aspects of the statistical process, including
design, analysis and conclusions.
 Study Island: http://www.studyisland.com/ (SI)
 AP The College Board AP Statistics released exam problems will be used
throughout the course with a heavier emphasis as we get closer to the AP exam.
Homework problems
 Will be assigned from the Primary textbook unless otherwise noted. All
questions will be answered in complete sentences and all conclusions will be
written in context of the problem. Homework problems that involve the graphing
calculator must first be set up with an appropriate equation and then the final
answer can be recorded.
ADDITIONAL TOPICS
1. Significant familiarity with the TI-84, shortcuts and special functions.
2. Additional work on simulations beyond the scope of the text.
3. Biographies of mathematicians who have contributed to the study
of statistics.
4. Students will be given a weekly AP test practice problem. More intensive work on
practice AP exams is done just before the exam.
Curriculum and Pacing Guide
Mathematics: AP Statistics
Unit 1: Exploring and Understanding Data
In this unit we cover data displays and summaries. Many students will recognize some of
the material from middle and high school, so our emphasis is on statistical thinking. Of
course, we define terms and provide examples. But we also discuss why methods
presented are used, and what we hope to learn from them. These are concepts that appear
throughout the course. Even more important than what to look for in a histogram or how
to summarize the spread of a distribution is the underlying lesson that there are reasons
for displaying and summarizing data. These reasons inform and motivate the entire
course.
Essential Questions:
 Why is statistics important?
 What is the nature of the data?
 Describe the type of graph that would be most appropriate for different data sets,
and justify the choice.
 What are common distribution shapes and describe at least one characteristic of
each.
 How can you quickly order data and, at the same time, reveal the distribution
shape?
 What are commonly used measures of central tendency? What information do
they provide?









How do variance and standard deviation measure data spread? Why is this
important?
Given a boxplot, determine the five number summary and explain what is
revealed about the spread of the data?
Analyze the effects of adding/subtracting, multiplying/dividing on the mean and
variance of a data set.
What are some characteristics of a normal distribution?
What does the empirical rule tell you about data spread about the mean?
Can you compare apples and oranges?
What is a standard normal distribution?
What is a standard z score?
How do you convert any normal distribution to a standard normal distribution?
Days
Chapter
Topic
0
1
1
2
What is Statistics?
- TB Class Survey
1-5, 1-6
Data
4
3
6
4
Displaying and Describing
Categorical Data
- TB WS 3-7
- (SI) Categorical
Data
Displaying and
Summarizing Quantitative
Data
- Refer to YMS Ch 1
#6for constructing
bar graph and
circle graph
- Refer to YMS Ch 1
# 14 for
constructing a
histogram
- Supplementary
material YMS Ch
1 #32 and 34 ;
Resistant &
Nonresistant
- (SI) Central
Tendency &
Spread
- (SI) Dotplots
Concepts & Terms
Assignment
Read Ch. 1 pgs. 2-6
Data, Data table, Case, Population,
Sample, Variable, Units,
Categorical Variable, Quantitative
Variable
Frequency table, Relative frequency
table, Distribution, Area principal,
Bar Chart, Pie Chart, Categorical
data condition, Contingency table,
Marginal distribution, Conditional
distribution, Independence,
Independence, Segmented bar chart,
Simpson’s paradox
Distribution, Histogram, Gap,
Stem-and-leaf display, Dotplot,
Shape, Center, Spread, Mode,
Unimodal, Bimodal, Uniform,
Symmetric, Tails, Skewed, Outliers,
Median, Range, Quartile,
Interquartile range, Percentile, 5Number Summary, Mean,
Resistant, Variance, Standard
deviation
Pgs.16-18 #2, 4, 8, 10,
14, 16, 26
D1:Pgs. 38-39 #6, 8-10,
12-16 even
D2:Pgs. 39-40 #18-24
even
D3:Pg. 41 #26-30 even
D4:Pgs. 42-43 #32-38
even
TB Investigative Task:
“Race and the Death
Penalty”
D1: GB Quiz 1.1A for #1
do a bar graph and a
circle graph; #2 do a
histogram and a split
stem plot. No
description necessary.
D2:GB Quiz 1.1A For
both questions, describe
the distribution.
D3:Pgs.72-73 #6-14 even
D4:Pgs. 73-75 #16-28
even
D5:Pgs. 75-76 #30-42
even
D6:Pgs 77-78 #48
TB Investigative Task
“Dollars for Students”
-
4
(SI) Stemplots
(SI) Histogram
(SI) Cumulative
Frequency Plots
Understanding and
Comparing Distribution
TB WS 5-5
- (SI) Boxplots
5
4
6
The Standard Deviation as a
Ruler and the Normal
Model
- TB Class Example
6-4
2
1
21
Review
Test
Total
Boxplot, Outlier, Far Outlier,
Comparing distribution, Comparing
boxplots, Timeplot
Standardizing, Standardized value,
Shifting, Rescaling, Normal model,
Parameter, Statistic, Z-score
D1:GB Quiz 1.2 B or C
D2: Pgs.95-96 #6-12
even
D3: Pgs. 97-100: #14, 16,
20, 24, 28
D4; Pg. 101 #34, 36
TB Investigative Task
“Auto Safety”, “SUV
Insurance”
D1 & 2:Pgs. 129-130 #222 even
D3: Pg. 131 #26-30 even
D4: Pgs. 132-133 #38-42
even
TB Investigative Task
“Normal Model”
Total Days: 21
Unit 2: Exploring Relationships between Variables
In this unit we expand on the idea of considering a second variable. Chapters7, 8, and 9
discuss relationships between two quantitative variables, introducing scatterplots,
correlation, and regression. The discussion is sophisticated and rich in new concepts and
points of view, even though there is not mention of inference. We’ll see these methods
(and inference for them) again in Chapter 27.
Essential Questions:
• How can a scatter plot be used to describe the association between quantitative
variables in terms of form, strength, and direction?
• Tell how to compute the correlation coefficient and what does it reveal about the
strength of the linear relationship between two random variables?
• What is the least-squares criterion? How do you find the equation of the least-squares
line?
• Explain the significance of the residual plot in an analysis of the least squares
regression model.
• What is the coefficient of determination, and what does it tell you about the explained
variation of y in a random sample of data pairs (x,y)?
• How would the slope of the least squares regression line be interpreted?
• What procedure should be used, if any, when a nonlinear scatterplot is encountered?
Days
Chapter
Topic
Concepts & Terms
Assignment
3
7
Scatterplots, Association,
and Correlation
- TB Class Example
7-3 & 7-7
- (SI) Scatterplots
Scatterplots, Association, Outlier,
Response variable, Explanatory
variable, X-variable, Y-variable,
Correlation Coefficient
4
8
Linear Regression
- TB WS 7-7 Goes
with Ch. 7&8
- TB Class Example
8-5
- TB WS 8-9
- (SI) Scatterplots
Model, Linear model, Predicted
value, Residuals, Least squares,
Regression to the mean,
Regression line, Line of best fit,
Slope, Intercept, Standard
deviation of the residuals, R2
D1: Pgs.164-165 #2-10
even
D:2 Pgs. 165-167 #12-26
even
D:3 Pg. 168 # 32 & 34
D4: Pg. 169 #36
D1: Pgs. 192-193 #2-10
even
D2: Pg. 193 # 12-22 even
D3: Pgs. 194-195 #24-32
even
D4: Pgs. 195-196 # 34 &
36
4
9
4
10
2
1
18
Review
Test
Total
Regression Wisdom
- TB Class Example
9-3
- TB Class Activity
9-5 & 9-6
- TB WS 27-9
(SI) Scatterplots
Re-expressing Data: Get it
Straight
- TB Class examples
10-3, 10-4, 10-5
- TB WS 10-7
- GB Quiz 4.1 A, B,
or C
- (SI) Scatterplots
Extrapolation, Outlier, Leverage,
Influential point, Lurking variable
Re-expression, Ladder of Powers,
Linear Transformation,
exponential growth model, Power
law model
TB Investigative Task
“Smoking”
D1: Pgs. 214-215 #2-8
even
D2: Pg. 216 #12-16 even
D3: Pgs. 217-218 #20, 22
D4: Pgs. 218-219 #24, 26
Pg. 208 Just checking
TB Investigative Task
“Olympic Long Jumps”
D1: Pg. 239 #2, 4
D2: Pgs. 239-241 #6-12
even
D3: Pg. 241 #14
D4: Pg. 242 #18, 20
Total Days: 39
Unit 3: Gathering Data
We’ve been examining data, noting patterns and relationships, and finding models to
describe them. Now we address where these data come from. The essential insight in
gathering data for statistical analysis is the central role of randomness. We sample
randomly or assign subjects to treatments at random. We randomize to minimize biases
and to reduce the influence of effects we cannot control.. Even more important, we
randomize to make it possible to use statistical inference methods that can extend our
understanding beyond the data at hand to the world at large. In this unit we discuss
randomness, apply it to gathering data, formalize it with probability, and then connect it
to the summaries and models of the first two parts with inference.
Essential Questions:
• How can a random sample be obtained?
• What is the difference between a random sample and a simple random sample?
• What are the other types of sampling techniques? Discuss the advantages and
disadvantages of each type.
• What types of bias can be encountered and why is it important to reduce bias?
• Discuss the different types of random sampling designs and when it is appropriate to
use each design.
• Describe the difference between an observational study and an experiment?
• Discuss the value of a control group.
• What is the correct procedure for designing an experiment?
Days
Chapter
Topic
Concepts & Terms
Assignment
2
11
Understanding Randomness
- GB Quiz 5.1 B #3
- GB Quiz 5.3 C #3
- TB Class examples
11-4 &11-5
Random, Generating random
numbers, Simulation, Simulation
component, Trial, Response
variable
D1: Pg. 265 #2-10 even
D2: Pg. 265 #12-16 even
Population, Sample, Sample
survey, Bias, Randomization,
Sample size, Census, Population
parameter, Statistic, Sample
statistic, Representative, Simple
random sample (SRS), Sampling
frame, Sampling variability,
Stratified random sample, Cluster
sample, Multistage sample,
Systematic sample, Pilot,
Voluntary response bias
Observational study, Retrospective
study, Experiment, Random
assignment, Factor, Response,
Experimental units, Level,
Treatment, Principles of
experimental design, Statistically
significant, Control group,
Blinding, Simple-blind, Doubleblind, Placebo, Placebo effect,
Blocking, Matching, Designs,
Confounding
D1: Pg. 288 #2-10 even
D2: Pg. 289 #12-20 all
D3: Pg. 290 #22-30 even
D4: Pgs. 290-291 #32-36
even
4
12
Sample Surveys
- TB Class examples
12-3, 12-4, 12-5
- GB Quiz 5.1 B
#1,2
- (SI) Data
Collection
- (SI) Surveys
4
13
Experiments and
Observational Studies
- TB Class examples
13-4, 13-5, 13-6,
13-7
- (SI) Data
Collection
- (SI) Experiments
1
1
12
Total Days: 51
Review
Test
Total
TB Investigative Task
“ESP”
D1: Pg. 312-313 #2-10 even
D2: Pg. 313 #14-18 even
D3: Pg. 313-314 #20-26
even
D4: Pg. 315 #36-40 even
TB Investigative Task
“Backhoes and Forklifts”
Unit 4: Randomness and Probability
In this unit we introduce the formal concepts of probability and distribution. These
concepts have been informally discussed all along. In chapters 14 and 15, we lay the
foundations for probability and discuss how it works through conditional probabilities
and tree diagrams. Chapters 16 and 17 discuss random variables and probability models.
Together, these four chapters lay the foundation that will allow students to understand
statistical inference.
Essential Questions:
• How can the basic definitions and rules of probability be used?
• How can counting techniques and tree diagrams be used to solve probability
problems?
• What is a random variable? How do you compute µ and σ for a discrete random
variable?
• How can the binomial probability distribution be used to compute the probability of
n successes?
• How can the geometric probability distribution be used to compute the probability of
success on the nth trial.
• How can µ and σ be computed for the binomial and geometric distributions?
• How can the probabilities of standardized events be computed?
Days
4
Chapter
14
Topic
From Randomness to
Probability
- TB Class Examples
14-4, 14-5
- (SI) Independent
and Dependent
Variables
4
15
Probability Rules
- TB Class Examples
15-3, 15-4, 15-5
- (SI) Probability
Concepts & Terms
Random phenomenon, Trial,
Outcome, Event, Sample space,
Law of large numbers,
Independence (informally),
Probability, Empirical probability,
Theoretical probability, Personal
probability, The Probability
Assignment Rule, Complement
rule, Disjoint (mutually exclusive),
Addition rule, Legitimate
probability assignment,
Multiplication rule, Independence
assumption
General addition rule, Conditional
probability, General multiplication
rule, Independence (used
formally), Tree diagram
5
16
Random Variables
- TB Class Examples
16-3, 16-4, 16-5,
16-6, 16-7, 16-8
- (SI) Random
Variables &
Probability
Distributions
Random variable, Discrete random
variable, Continuous random
variable, Probability model,
Expected value, Variance,
Standard deviation, Changing a
random variable by a constant,
Adding or subtracting random
variables
Assignment
D1: Pg. 338 #2-10 even
D2: Pg. 338-339 #12-20
D3: Pgs. 339-340 #22,
26-30 even
D4: Pgs. 340-341 #32-36
even
D1: Pgs. 361-362 #2-6 even
D2: Pg. 362 #8-12 even
D3: Pgs. 362-363 #16-20
even
D4: Pgs. 363-364 #22, 24,
34, 38
D1: Pg. 383 #2-8 even
D2: Pgs. 383-384 #10-18
even
D3: Pg. 384 #20-24 even
D4: Pgs. 384-385 #26-30
even
D5: Pg. 385 #38,40
6
17
2
1
22
Review
Test
Total
Probability Models
- TB Class Examples
17-3, 17-4
Bernoulli trials, Geometric
probability model, 10% condition,
Success/Failure condition
D1: Pg. 401 #2-8 even
D2: Pg. 402 #10-18 even
D3: Pgs. 402-403 #20-26
even
D4: Pg. 403 #32, 34
D5: Pg. 403 #36
D6: Pg. 399 Just Checking
Total: 73 days
Unit 5: From the Data at Hand to the World at Large
In this unit we come to statistical inference. Problems of statistical inference require us
to draw a sample of observations from a larger population. Conclusions about the
population proportions can be obtained from sample data by the use of statistical
estimates and tests of significance.
Essential Questions:
• What is a probability sampling distribution?
• How do sampling distributions assist statisticians in making good decisions for a
population?
• What is the relationship between sample size and margin of error?
• How can the proportion p of successes in a binomial experiment be estimated given
the number of successes and sample size? How does the normal approximation fit
into this process?
• When given incomplete (sample) information, how can it be decided whether to
accept or reject a proposal?
• What procedure should be used to estimate a population proportion?
• Describe the difference between a critical value and a test statistic?
• What is the significance of the standard error?
• What is the P-value of a statistical test? What does this measurement have to do with
performance reliability?
• How can a statistical test be constructed for the proportion p of successes in a
binomial experiment?
• How do we use sample data to compare proportions from two independent
populations?
• Identify the conditions necessary to perform a confidence interval and hypothesis test
for a population proportion.
• Discuss the ramifications of a Type I or Type II error.
Days
Chapter
Topic
Concepts & Terms
Assignment
5
18
Sampling Distribution
Models
- TB Class Examples
18-6 through 18-11
- (SI) Sampling
Distributions
Sampling distribution model,
Sampling variability/Sampling
error, Sampling distribution,
Model for a proportion, Central
Limit Theorem, Sampling
distribution model for a mean
D1: Pgs. 432-433 #2-6 even
D2: Pg. 433 #8-12 even
D3: Pg. 434 #16-20 even
D4: Pg. 434 #28
Pg. 436 #32
D5: Pg. 436 #34, #42
4
19
5
20
4
21
Confidence Intervals for
Proportions
- Inference Toolbox
YMS Pg. 548
- TB Class Examples
19-3, 19-4, 19-5
- (SI) Sampling
Distributions
- (SI) Estimation
Testing Hypotheses about
Proportions
- Inference Toolbox
YMS Pg. 471
- TB Class Examples
20-5, 20-6, 20-7
- (SI) Sampling
Distributions
- (SI) Tests of
Significance
More About Tests and
Intervals
- TB Class Examples
21-3, 21-4
- (SI) Tests of
Significance
Standard error, Confidence
interval, One-proportion z-interval,
Margin of error, Critical value
TB Investigative Task
“Simulated Coins”
D1: Pg. 455 #2-6 even
D2: Pgs. 455-456 #8-14
even
D3: Pgs. 456-457 #16-22
even
D4: Pgs. 457-458 #24, 30,
32
Null Hypothesis, Alternative
hypothesis, Two-tailed alternative,
One-tailed alternative, P-value,
One-proportion z-test
D1: Pgs. 476-477, #2-8 even
D2: Pg. 477 #10-14 even
D3: Pg. 478 #16-20 even
D4: Pg. 478 #22-26 even
D5: Pg. 479 #28-32 even
Alpha level, Statistically
significant, Significance level,
Type I error, Type II error, Power,
Effect size
D1: Pgs. 499-500 #2-12
even
D2: Pgs. 500-501 #14-18
even
D3: Pg. 502 #24, 26
D4: Pg. 503 #34
TB Investigative Task
“Life After High School?”
4
22
2
1
25
Review
Test
Total
Total Days: 98
Comparing Two Proportions
- TB Class Examples
- 22-3 through 22-6
- (SI) Sampling
- Distributions
- (SI) Estimation
- (SI) Tests of
Significance
Variances of independent random
variables add, Sampling
distribution of the difference
between two proportions, Twoproportion z-interval, Pooling,
Two-proportion z-test
D1: Pg. 519 #2-6 even
D2: Pgs. 519-520 #8-12
even
D3: Pgs. 520-521 #16, 18
D4: Pg. 521 #20, 22
Unit 6: Learning About the World
In this unit we will continue to use the same inference procedures as in unit 5. However
now we will draw conclusions about the population mean from sample data by the use of
statistical estimates and tests of significance.
Essential Questions:
• Why is the Central Limit Theorem so important in our study about inference for the
mean?
• What distributions can the CLT be applied to, and how can this be accomplished?
• When confronted with incomplete (sample) information, how can a decision be made
whether to accept or reject a proposal?
• What procedure should be used to estimate a population mean?
• Describe the difference between a critical value and a test statistic?
• What is the significance of the standard error?
• What is the P-value of a statistical test? What does this measurement have to do with
performance reliability?
• How can a statistical test for µ be constructed? Does it make a difference whether σ
is known or unknown?
• What are the statistical advantages of paired data values? How can statistical tests be
constructed?
• How can means be compared from two independent populations when σ is unknown
for each population?
• Identify the conditions necessary to perform a confidence interval and hypothesis test
for a population mean.
Days
4
Chapter
23
4
24
3
25
Topic
Inference About Means
- TB Class Examples
23-4, 23-5, 23-7
- (SI) Sampling
Distributions
- (SI) Estimation
- (SI) Tests of
Significance
Comparing Means
- TB Class Examples
24-3, 24-4, 24-5
- (SI) Sampling
Distributions
- (SI) Estimation
- (SI) Tests of
Significance
Paired Samples and Blocks
- TB Class Examples
25-3, 25-4
- (SI) Sampling
Distributions
- (SI) Estimation
- (SI) Tests of
Significance
Concepts & Terms
T distribution, Degrees of freedom
(df), One-sample t-interval for the
mean, One-sample t-test for the
mean
Two-sample t methods, Twosample t-interval for the difference
between means, Two-sample t-test
for the difference between means,
Pooling, Pooled t-methods
Paired data, Paired t-test, Paired-t
confidence interval
Assignment
D1: Pg. 554 #2-8 even
D2: Pgs. 555-556 #10-16
even
D3: Pg. 556 #18, 20
D4: Pg. 557 #30, 32
TB Investigative Task
“SAT Performance”
D1: Pgs. 578-580 #2-8 even
D2: Pgs. 580-581 #10, 12,
14
D3: Pg. 582 #16, 18
D4:Pg. 585 #36
D1: Pgs. 602-603 #2-8 even
D2: Pgs. 603-604 #10-16
even
D3: Pgs. 605-606 #20, 24
TB Investigative Task
“SAT Performance Part II”
2
1
14
Review
Test
Total
Total Days: 112
Unit 7: Inference When Variables Are Related
This unit is about understanding and modeling relationships between variables. In
chapter 26, we look at categorical data with χ2 models. In chapter 27, the subject is
inference for regression models.
Essential Questions:
• How can it be determined whether the observed counts in a frequency distribution or
contingency table are consistent with a proposed model?
• How can it be determined whether two or more observed distributions could have
arisen from populations with the same model?
• How can it be decided if two distributions are independent?
• How can a correlation coefficient be tested?
• What is the standard error of estimate? How do you compute it, and where is it used?
• How can a confidence interval for a least-squares prediction be computed?
• The slope of the least-squares regression line represents rate of growth. How can it
be determined if the rate of growth is statistically significant?
Days
8
Chapter
26
2
27
2
1
13
Review
Test
Total
Total Days: 125
Topic
Comparing Counts
- TB Class
Examples 26-5,
26-6, 26-7
- (SI) Tests of
Significance
Inferences for Regression
- TB Class
Examples 27-4,
27-5, 27-6, 27-7
- (SI) Tests of
Significance
Concepts & Terms
Chi-square model, Cell, Chi-square
statistic, Chi-square test of
goodness-of-fit, Chi-square test of
homogeneity, Chi-square test of
independence, Chi-square
component, Standardized residual,
To-way table, Contingency table
Conditions for inference, in
regression , Residual standard
deviation, t-test for the regression
slope, Confidence interval for the
Regression slope (β)
Assignment
D1: Pgs. 642-643 #2-6
even
D2: Pg. 643 #8, 10
D3: Pgs. 644-645 #12-18
even
D4: Pg. 645 #20-24 even
D5: Pg. #26-30 even
D6: Pg. 647 #32-36 even
D7: Pg. 648 #38, 40
D8: Pg. 648 #42
TB Investigative Task
“97 AP Stat Scores”
D1: Pgs. 673-674 #2-8
even
D2: Pgs. 675-676 #12-18
even
SUPPLEMENTAL WORK: TEST PREPARATION AND
STRATEGIES
Days
10
Chapter
10
20
Total
Total Days: 145
Topic
Multiple Choice Questions
in a timed setting
- (SI) Multiple
Choice
Free Response questions in
a timed setting
- (SI) Free Response
Concepts & Terms
In-class practice
In-class practice
Assignment