A.P. Statistics 2015-2016
Advanced Placement Statistics
Instructor: Mrs. Soto
Room: 224 (South)
 Phone/ Voice Mail: (616) 738-6946
 School E-mail Address: sotol@westottawa.net
 Website: wosotomath.weebly.com
Twitter: @wosotomath
Availability outside of class: seminar, before school, after school most days
Course Description:
The purpose of AP 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 (Ch. 1-3): Describing patterns and departures from patterns
2. Sampling and Experimentation (Ch. 4): Planning and conducting a study
3. Anticipating Patterns (Ch. 5-7): Exploring random phenomena using probability and simulation
4. Statistical Inference (Ch. 8-12): Estimating population parameters and testing hypotheses
Materials Needed Daily:
 Graphing Calculator (recommended models: TI-83, TI-83 Plus, TI-84, TI-84 Plus, TI-84 Silver ed.)
 Textbook
 Writing utensil – pencil preferred
 Lined paper and 3-ring binder
Textbook:
Starnes, Tabor, Yates, and Moore. The Practice of Statistics, 5th ed.
Classroom Expectations:
The following is a list of simple behavior guidelines to which we will all abide.
1. Respect other students, the teacher, and the classroom.
2. Be on time to class. We need every minute we can get.
3. All electronic devices should be used appropriately, in a way that does not distract from teaching or
learning.
4. Be an active learner. Participate in discussions. Ask questions. Help others. You will get more
out of the class this way.
Assignments:
 Practicing mathematical concepts is essential to successfully understanding the material.
Assignments will be given daily, with few exceptions.
 Students typically are given 1-2 days to complete each assignment.
 Solutions to most of the assignments will be posted in the Google classroom. They are available for
you to monitor your progress. If you need help on homework assignments, please see me outside of
class time.
 Most homework will be scored as complete/incomplete. Some assignments will be collected and
graded in detail.
 There is no penalty for late work due to excused absences. If special circumstances exist and you
feel you need more time for homework, please come see me.
A.P. Statistics 2015-2016
Absences:
 It is the student’s responsibility to see me about any material missed due to an absence.
 Any assignment, test, or quiz that was due or took place on the day of an absence, is due or will take
place the day the student returns, unless prior arrangements have been made.
 Students are notified well in advance of any quizzes or tests.
 If a student is absent on the day before a quiz or test, he or she will be expected to take the
assessment upon return during the class period it is administered to the entire class (unless PRIOR
arrangements have been discussed with me.)
 If two or more days are missed before a test or quiz, it is the responsibility of the student to
reschedule the test/quiz promptly with me upon return.
Test Retakes:
 There are no retests in this college-level course.
Grading:
The math department grading scale will be used.
18-week grades will be calculated as follows:
Tests: 70 %
Quizzes: 25 %
Assignments/Homework: 5 %
Final semester grades will be calculated one of two ways, and the higher of the two grades will be
recorded.
18-week cumulative grade: 80 %
or
18-week cumulative grade: 66.6%
Semester Exam:
20 %
Semester Exam:
33.3%
Resources to Help You be Successful:
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Subscribe to class announcements via Remind101: text “@sotostats” to 81010
Visit my website: wosotomath.weebly.com. Use the “AP Statistics” tab to find PowerPoint notes, or
the “More Study Tools” tab to find flash cards, videos, and other resources.
Join our Google Classroom: Use class code “7jkxf7” to join
A.P. Statistics 2015-2016
Course Topics:
UNIT I: EXPLORATORY DATA ANALYSIS: Chapters 1–3: Observing Patterns and Departures from
Patterns
Enduring Understandings:
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Interpretation of graphical displays of distributions of univariate data is essential in understanding data patterns.
Graphical skills are created for the purpose of analysis and communication.
Calculating descriptive statistics is essential in summarizing distributions of univariate data.
Comparison of distributions of univariate data is important in discovering data patterns.
Exploring relationships between bivariate data is necessary in order to fit an appropriate model to the data.
Frequency tables are used to explore relationships between categorical variables.
Exploratory analysis of data makes use of graphical and numerical techniques to study patterns and departures from
patterns.
Regression is an effective model for prediction.
There is a difference between causation and correlation.
Knowledge and Skills:
Dot Plots
Stem and Leaf Plot
Scatterplots
Box &Whisker Plot
Mode
Range
IQR
Quartiles
Clusters
Gaps
Correlation Coefficient r Normal Distribution
Simpson’s Paradox
Qualitative Variables
Standard Normal Calculations
Marginal Frequencies
Relative Frequencies
Write regression equations given summary statistics
Histograms
Cumulative Frequency plots
Mean
Median
Variance
Standard Deviation
Percentiles
Z-scores
Outliers
Influential Points
Coefficient of Determination r2
Quantitative Variables
Bar Charts
Two-way Tables
Read/interpret computer printouts
UNIT II: EXPERIMENTAL DESIGN: Chapter 4: Producing Data
Enduring Understandings:
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There are many different methods of data collection, depending on the goal(s) of the study.
It is important to use appropriate techniques when planning and conducting surveys.
It is important to use appropriate techniques when planning and conducting experiments.
Data must be collected according to a well-developed plan if valid information is to be obtained.
Appropriate generalizations can be made from properly conducted observational studies, experimental studies, and
surveys.
Clarifying the question leads to appropriate methodology.
Knowledge and Skills:
Census
Population
Bias
Experimental Units
Blinding
Replication
Randomized Design
Sample Survey
Sample
Treatment
Random Assignment
Placebo Effect
Blocking
Experiment
Random Selection
Control Group
Random Digit Table
Lurking Variable
Cluster Sample
Observational Study
Simple Random Sample
Stratified Random Sample
Confounding Variable
Double Blind
Multi-Stage Sample
A.P. Statistics 2015-2016
UNIT III: ANTICIPATING PATTERNS: Chapters 5–7: Exploring random phenomena using
probability and simulation
Enduring Understandings:
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Probability is the tool for anticipating what a distribution of data should look like under a given model.
Probability is the basis for statistical inference.
Independent random variables can be combined to produce new models.
A binomial model is very useful in many discrete real-life situations.
A geometric model may be appropriate for some discrete real-life situations.
The normal distribution and central limit theorem are essential to analyzing samples of data.
Sampling distributions are necessary in producing models when population data is not feasible.
Knowledge and Skills:
Law of Large Numbers
Multiplication Rule for Probability
Independent Events
Mutually Exclusive Events
Tree Diagrams
Geometric Distributions
Standard Deviation
Linear Transformations
Properties of a Normal Distribution
Sampling Distribution of a Proportion
Sampling Dist. Of a Diff of Proportions
Addition Rule for Probability
Union
Conditional Probability
Intersection
Discrete Random Variables
Events
Continuous Random Variables
Outcomes
Binomial Distributions
Central Limit Theorem
Expected Value (mean)
Simulation
Independent Random Variables
TI-83 use
Dependent Random Variables
Venn Diagrams
Rules for Sums/Differences of Random Variables
Sampling Distribution of a Mean
Z-Scores
Sampling Dist of a Diff of Means
Table Use
UNIT IV: STATISTICAL INFERENCE: Chapters 8–12: Estimating population parameters and testing
hypotheses
Enduring Understandings:
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Statistical inference guides the selection of appropriate models.
Confidence intervals can be used to make decisions regarding the appropriateness of a statistical model.
Confidence intervals are effective tools for estimating the mean of a population.
Confidence intervals are effective tools for estimating the proportion of a population.
Tests of Significance can be used to make decisions regarding the appropriates of a statistical model.
Normally distributed data lends itself to special situations when defining statistical models.
The appropriate communication and interpretation of statistics is essential to avoiding statistical abuse and/or
misunderstanding.
Students will understand that statistics can be used to make valuable, reliable inferences from empirical information.
Knowledge and Skills:
Estimate
Margin of Error
Standard Error
Confidence Level
Confidence Interval
Std Dev of the Estimate 1-Prop Z Confidence Interval
2-Prop Z Confidence Interval
1-Sample Z interval
2-Sample Z interval
Null Hypothesis
Alternative Hypothesis (1- and 2-sided)
P-value
Interpretation of p
Statistically Significant Alpha level
Type I error
Type II error
Power of a Test
1-Prop Z Test
2-Prop Z Test
1-Sample Z Test
2-Sample Z Test
1-Sample T Interval
2-Sample T Interval
1-Sample T Test
2-Sample T Test
Chi-Square Test of Independence
Chi-Square GOF Test
Degrees of Freedom
Regression Test for Slope
Critical Value
Test Statistic
Check appropriate Assumptions/Conditions for Confidence Intervals and Tests of Significance
Determine Sample Size for desired margin of error