AP Statistics
Kellie Lee
Glasgow High School
Primary Textbook
Starnes Daren S., Yates, Daniel, Moore, David. The Practice of Statistics, Fourth edition. W.H. Freeman and Company, New York, 2010.
Technology
 All students are expected to have a TI84, 84+ for their use in class and for homework assignments. For students that cannot afford a calculator our school will allow the students to check one out from the library. The students also have access to graphing calculators in the classroom during school.
 The graphing calculator is used everyday in class and students are instructed daily on how to use this technology to help them understand statistical concepts.
 I will use TI Smartview in the classroom to give instructions and class demonstrations.
 Students are exposed to numerous applets during the course and I have an interactive board in my classroom.
Websites
Website

Content


· This website contains many datasets for use in statistics as well and useful tools for analyzing population data.

 http://exploringdata.cqu.edu.au/

· Webbased supplement to the AP Stats textbook.

 http://lib.stat.cmu.edu/DASL/

· Data sets and statistical real world examples for student work.

 http://bcs.whfreeman.com/yates2e/

· Website for TPS (AP Statistics Book)



Course Topic Overview
This course is an introduction to the basic topics of statistics. The related topics include the following:
 Organizing Data: Looking for Patterns and Departures from Patterns
 Producing Data: Samples, Experiments, and Simulations
 Probability: Foundations of Inference
 Inference: Conclusions with Confidence
Course Objectives
By the end of this class students should be able to analyze data to make relevant, intelligent, real world decisions. The students will be able to selfevaluate their content knowledge in this course by completing the following I can statements.
 I can analyze data using numerous graphical/tabular types
 I can analyze data using density/normal curves
 I can understand correlation/causation with data samples
 I can use the LSRL to describe a data set
 I can model and describe nonlinear relationships
 I can design an appropriate experiment that is valid and reliable
 I can understand randomness as it is related to probability/data sampling
 I can solve probability problems using both a binomial and geometric distribution
 I can understand sampling distributions as a basis for statistical inference
 I can use confidence intervals and tests of significance to analyze data
 I an understand the difference between a Type I and II error
 I can compare two sample means for statistical inference
 I can use population proportions for statistical inference
 I can use inference/twoway tables to test for goodness of fit
 I can use inference for the regression line and slope of the LSRL
 I can communicate concepts and ideas in a clear manner, using correct statistical vocabulary and notation.
 I can use technology to help solve problems, experiments, interpret results, and verify conclusions.
 I can recognize and develop a thorough plan for collecting data in order to make a valid conjecture.
AP Exam
All our students are strongly encouraged to take the AP exam in May. If a student is in need of financial aide our school will pay for this exam if they cannot afford the exam fee.
GRADING
The overall grade will be determined (approximately) as follows:
· Homework/Class participation. 10%
· Quizzes, including Online quizzes, Release FreeResponse 35%
· Tests/Projects. 55%
Grading scale:
10090% A
80  89% B
70  79% C
60  69% D
Below 60% F
Chapter 1
Day

Topics

Objectives: Students will be able to…

Homework

1

Chapter 1 Introduction; Activity: Hiring discrimination: This activity models the components of the statistical problem solving process: research question, data analysis, probability model, and inference

· Identify the individuals and variables in a set of data.
· Classify variables as categorical or quantitative. Identify units of measurement for a quantitative variable.

1, 3, 5, 7, 8

2

1.1 Bar Graphs and Pie Charts, Graphs: Good and Bad

· Make a bar graph of the distribution of a categorical variable or, in general, to compare related quantities.
· Recognize when a pie chart can and cannot be used.
· Identify what makes some graphs deceptive.

11, 13, 15, 17

3

1.1 TwoWay Tables and Marginal Distributions, Relationships Between Categorical Variables: Conditional Distributions, Organizing a Statistical Problem, Technology: Analyzing TwoWay Tables with Minitab

· From a twoway table of counts, answer questions involving marginal and conditional distributions.
· Describe the relationship between two categorical variables in context by comparing the appropriate conditional distributions.
· Construct bar graphs to display the relationship between two categorical variables.

19, 21, 23, 25, 2732

4

1.2 Dotplots, Describing Shape, Comparing Distributions, Stemplots

· Make a dotplot or stemplot to display small sets of data.
· Describe the overall pattern (shape, center, spread) of a distribution and identifyany major departures from the pattern (like outliers).
· Identify the shape of a distribution from a dotplot, stemplot, or histogram as roughly symmetric or skewed. Identify the number of modes.

37, 39, 41, 43, 45, 47

5

1.2 Histograms, Using Histograms Wisely, Technology: Making Histograms on the Calculator

· Make a histogram with a reasonable choice of classes.
· Identify the shape of a distribution from a dotplot, stemplot, or histogram as roughly symmetric or skewed. Identify the number of modes.
· Interpret histograms.

53, 55, 57, 59, 60, 6974

6

1.3 Measuring Center: Mean and Median, Comparing Mean and Median, Measuring Spread: IQR, Identifying Outliers

· Calculate and interpret measures of center (mean, median) in context
· Calculate and interpret measures of spread (IQR) in context
· Identify outliers using the 1.5 ´ IQR rule.

79, 81, 83, 87, 89

7

1.3 Five Number Summary and Boxplots, Measuring Spread: Standard Deviation, Choosing Measures of Center and Spread, Technology: Making Boxplots on the Calculator, Computing Numerical Summaries with Minitab and the Calculator

· Make a boxplot.
· Calculate and interpret measures of spread (standard deviation)
· Select appropriate measures of center and spread
· Use appropriate graphs and numerical summaries to compare distributions of quantitative variables.

91, 93, 95, 97, 103, 105, 107110

8

Chapter 1 Review


Chapter 1 Review Exercises

9

Chapter 1 Test



Chapter 1 Project: Critical statistical analysis – each student collects data and analyzes it using the techniques learned in this unit and prepares a written analysis. Evaluation using a fourpoint rubric like the AP Free Response questions.
Chapter 2
Day

Topics

Objectives: Students will be able to…

Homework

1

2.1 Introduction, Measuring Position: Percentiles, Cumulative Relative Frequency Graphs, Measuring Position: zscores

· Use percentiles to locate individual values within distributions of data.
· Interpret a cumulative relative frequency graph.
· Find the standardized value (zscore) of an observation. Interpret zscores in context.

5, 7, 9, 11, 13, 15

2

2.1 Transforming Data, Density Curves

· Describe the effect of adding, subtracting, multiplying by, or dividing by a constant on the shape, center, and spread of a distribution of data.
· Approximately locate the median (equalareas point) and the mean (balance point) on a density curve.

19, 21, 23, 31, 3338

3

2.2 Normal Distributions, The 689599.7 Rule, The Standard Normal Distribution, Technology: Standard Normal Curve Calculations with the Calculator and with an Applet

· Use the 68–95–99.7 rule to estimate the percent of observations from a Normal distribution that fall in an interval involving points one, two, or three standard deviations on either side of the mean.
· Use the standard Normal distribution to calculate the proportion of values in a specified interval.
· Use the standard Normal distribution to determine a zscore from a percentile.

41, 43, 45, 47, 49, 51

4

2.2 Normal Distribution Calculations, Technology: Normal Curve Calculations with the Calculator and with an Applet

· Use Table A to find the percentile of a value from any Normal distribution and the value that corresponds to a given percentile.

53, 55, 57, 59

5

2.2 Assessing Normality, Normal Probability Plots on the Calculator

· Make an appropriate graph to determine if a distribution is bellshaped.
· Use the 689599.7 rule to assess Normality of a data set.
· Interpret a Normal probability plot

63, 65, 66, 68, 6974

6

Chapter 2 Review


Chapter 2 Review Exercises

7

Chapter 2 Test


39^{R}, 40^{R}, 75^{R}, 76^{R}

Chapter 3
Day

Topics

Objectives: Students will be able to …

Homework

1

Chapter 3 Introduction, Activity: CSI Stats, 3.1 Explanatory and response variables, 3.1 Displaying relationships: scatterplots, 3.1 Interpreting scatterplots, Technology: Scatterplots on the Calculator

· Describe why it is important to investigate relationships between variables.
· Identify explanatory and response variables in situations where one variable helps to explain or influences the other.
· Make a scatterplot to display the relationship between two quantitative variables.
· Describe the direction, form, and strength of the overall pattern of a scatterplot.
· Recognize outliers in a scatterplot.

1, 5, 7, 11, 13

2

3.1 Measuring linear association: correlation, 3.1 Facts about correlation, Technology: Correlation and Regression Applet

· Know the basic properties of correlation.
· Calculate and interpret correlation in context.
· Explain how the correlation r is influenced by extreme observations.

14–18, 21, 26

3

3.2 Leastsquares regression, 3.2 Interpreting a regression line, 3.2 Prediction, Technology: LeastSquares Regression Lines on the Calculator

· Interpret the slope and y intercept of a leastsquares regression line in context.
· Use the leastsquares regression line to predict y for a given x.
· Explain the dangers of extrapolation.

27–32,
35, 37, 39, 41

4

3.2 Residuals and the leastsquares regression line, 3.2 Calculating the equation of the leastsquares regression line, Technology: Residual Plots and s on the Calculator

· Calculate and interpret residuals in context.
· Explain the concept of least squares.
· Use technology to find a leastsquares regression line.
· Find the slope and intercept of the leastsquares regression line from the means and standard deviations of x and y and their correlation.

43, 45, 47, 53

5

3.2 How well the line fits the data: residual plots, 3.2 How well the line fits the data: the role of r^{2} in regression

· Construct and interpret residual plots to assess if a linear model is appropriate.
· Use the standard deviation of the residuals to assess how well the line fits the data.
· Use r^{2} to assess how well the line fits the data.
· Interpret the standard deviation of the residuals and r^{2} in context.

49, 54, 56, 58–61

6

3.2 Interpreting computer regression output, 3.2 Correlation and regression wisdom, Technology: LeastSquares Regression using Minitab and JMP

· Identify the equation of a leastsquares regression line from computer output.
· Explain why association doesn’t imply causation.
· Recognize how the slope, y intercept, standard deviation of the residuals, and r^{2} are influenced by extreme observations.

63, 65, 68, 69, 71–78

7

Chapter 3 Review


Chapter Review Exercises

8

Chapter 3 Test


33^{R}, 34^{R}, 79^{R}, 80^{R}, 81^{R}

Chapter 4
Day

Topics

Objectives: Students will be able to…

Homework

1

4.1 Introduction, Sampling and Surveys, How to Sample Badly, How to Sample Well: Random Samples, Technology: Choosing an SRS using an Applet or Calculator

· Identify the population and sample in a sample survey.
· Identify voluntary response samples and convenience samples. Explain how these bad sampling methods can lead to bias.
· Describe how to use Table D to select a simple random sample (SRS).

1, 3, 5, 7, 9, 11

2

4.1 Other Sampling Methods

· Distinguish a simple random sample from a stratified random sample or cluster sample. Give advantages and disadvantages of each sampling method.

17, 19, 21, 23, 25

3

4.1 Inference for Sampling, Sample Surveys: What Can Go Wrong?

· Explain how undercoverage, nonresponse, and question wording can lead to bias in a sample survey.

27, 28, 29, 31, 33, 35

4

4.2 Observational Studies vs. Experiments, The Language of Experiments, How to Experiment Badly

· Distinguish between an observational study and an experiment.
· Explain how a lurking variable in an observational study can lead to confounding.
· Identify the experimental units or subjects, explanatory variables (factors), treatments, and response variables in an experiment.

3742, 45, 47, 49, 51, 53

5

4.2 How to Experiment Well, Three Principles of Experimental Design

· Describe a completely randomized design for an experiment.
· Explain why random assignment is an important experimental design principle.

57, 63, 65, 67

6

4.2 Experiments: What Can Go Wrong? Inference for Experiments

· Describe how to avoid the placebo effect in an experiment.
· Explain the meaning and the purpose of blinding in an experiment.
· Explain in context what “statistically significant” means.

69, 71, 73, 75*
(*We will analyze this data again in an Activity in chapter 10)

7

4.2 Blocking, Matched Pairs Design

· Distinguish between a completely randomized design and a randomized block design.
· Know when a matched pairs experimental design is appropriate and how to implement such a design.

77, 79, 81, 85,

8

4.3 Scope of Inference, the Challenges of Establishing Causation

· Determine the scope of inference for a statistical study.

9198, 102108

9

4.2 Class Experiments
or
4.3 Data Ethics* (*optional topic)

· Evaluate whether a statistical study has been carried out in an ethical manner.

55, 83, 87, 89

10

Chapter 4 Review


Chapter 4 Review Exercises

11

Chapter 4 Test


Part 1: Cumulative AP Review Exercises

Chapter 4 Project: Students work in teams of 2 to design and carry out an experiment to investigate response bias, write a summary report, and give a 10 minute oral synopsis to their classmates. See rubric on page 15.
Chapter 5
Day

Topics

Objectives: Students will be able to…

Homework

1

5.1 Introduction, The Idea of Probability, Myths about Randomness

· Interpret probability as a longrun relative frequency in context.

1, 3, 7, 9, 11

2

5.1 Simulation, Technology: Random Numbers with Calculators

· Use simulation to model chance behavior.

15, 17, 19, 23, 25

3

5.2 Probability Models, Basic Rules of Probability

· Describe a probability model for a chance process.
· Use basic probability rules, including the complement rule and the addition rule for mutually exclusive events.

27, 31, 32, 43, 45, 47

4

5.2 TwoWay Tables and Probability, Venn Diagrams and Probability

· Use a Venn diagram to model a chance process involving two events.
· Use the general addition rule to calculate P(AB)

29, 3336, 49, 51, 53, 55

5

5.3 What is Conditional Probability?, Conditional Probability and Independence, Tree Diagrams and the General Multiplication Rule

· When appropriate, use a tree diagram to describe chance behavior.
· Use the general multiplication rule to solve probability questions.
· Determine whether two events are independent.
· Find the probability that an event occurs using a twoway table.

5760, 63, 65, 67, 69, 73, 77, 79

6

5.3 Independence: A Special Multiplication Rule, Calculating Conditional Probabilities

· When appropriate, use the multiplication rule for independent events to compute probabilities.
· Compute conditional probabilities.

83, 85, 87, 91, 93, 95, 97, 99

7

Review


Chapter 5 Review Problems

8

Chapter 5 Test


61^{R}, 62^{R}, 107^{R}, 108^{R}, 109^{R}

Chapter 6
Day

Topics

Objectives: Students will be able to…

Homework

1

Chapter 6 Introduction, 6.1 Discrete random Variables, Mean (Expected Value) of a Discrete Random Variable

· Use a probability distribution to answer questions about possible values of a random variable.
· Calculate the mean of a discrete random variable.
· Interpret the mean of a random variable in context.

1, 5, 7, 9, 13

2

6.1 Standard Deviation (and Variance) of a Discrete Random Variable, Continuous Random Variables, Technology: Analyzing Random Variables on the Calculator

· Calculate the standard deviation of a discrete random variable.
· Interpret the standard deviation of a random variable in context.

14, 18, 19, 23, 25

3

6.2 Linear Transformations

· Describe the effects of transforming a random variable by adding or subtracting a constant and multiplying or dividing by a constant.

2730, 37, 3941, 43, 45

4

6.2 Combining Random Variables, Combining Normal Random Variables

· Find the mean and standard deviation of the sum or difference of independent random variables.
· Determine whether two random variables are independent.
· Find probabilities involving the sum or difference of independent Normal random variables.

49, 51, 5759, 63

5

6.3 Binomial Settings and Binomial Random Variables, Binomial Probabilities, Technology: Binomial Probabilities on the Calculator

· Determine whether the conditions for a binomial random variable are met.
· Compute and interpret probabilities involving binomial distributions.

61, 65, 66, 69, 71, 73, 75, 77

6

6.3 Mean and Standard Deviation of a Binomial Distribution, Binomial Distributions in Statistical Sampling

· Calculate the mean and standard deviation of a binomial random variable. Interpret these values in context.

79, 81, 83, 85, 87, 89

7

6.3 Geometric Random Variables, Technology: Geometric Probabilities on the Calculator

· Find probabilities involving geometric random variables.

93, 95, 97, 99, 101103

8

Chapter 6 Review


Chapter 6 Review Exercises

9

Chapter 6 Test


31^{R}34^{R}

EXAM REVIEW: 3 DAYS
SEMESTER 1 EXAM: Simulated AP format with Multiple Choice, Free Response
Chapter 7
Day

Topics

Objectives: Students will be able to…

Homework

1

Introduction: German Tank Problem, 7.1 Parameters and Statistics, Technology: Using Fathom to Simulate Sampling Distributions

· Distinguish between a parameter and a statistic.

1, 3, 5, 7

2

7.1 Sampling Variability, Describing Sampling Distributions

· Understand the definition of a sampling distribution.
· Distinguish between population distribution, sampling distribution, and the distribution of sample data.
· Determine whether a statistic is an unbiased estimator of a population parameter.
· Understand the relationship between sample size and the variability of an estimator.

9, 11, 13, 1720

3

7.2 The Sampling Distribution of , Using the Normal Approximation for , Technology: Using an Applet to Simulate the distribution of .

· Find the mean and standard deviation of the sampling distribution of a sample proportion for an SRS of size n from a population having proportion p of successes.
· Check whether the 10% and Normal conditions are met in a given setting.
· Use Normal approximation to calculate probabilities involving .
· Use the sampling distribution of to evaluate a claim about a population proportion.

2124, 27, 29, 33, 35, 37, 41

4

7.3 The Sampling Distribution of : Mean and Standard Deviation, Sampling from a Normal Population, Technology: Using an Applet to Simulate the distribution of .

· Find the mean and standard deviation of the sampling distribution of a sample mean from an SRS of size n.
· Calculate probabilities involving a sample mean when the population distribution is Normal.

4346, 49, 51, 53, 55

5

7.3 The Central Limit Theorem

· Explain how the shape of the sampling distribution of is related to the shape of the population distribution.
· Use the central limit theorem to help find probabilities involving a sample mean .

57, 59, 61, 63, 6568

6

Chapter 7 Review


Chapter 7 Review Exercises

7

Chapter 7 Test


69^{R}72^{R}

Chapter 8
Day

Topics

Objectives: Students will be able to:

Homework

1

8.1 The Idea of a Confidence Interval, Interpreting Confidence Levels and Confidence Intervals, Constructing a Confidence Interval, Technology: Simulating Confidence Intervals with the Confidence Interval Applet

· Interpret a confidence level in context.
· Interpret a confidence interval in context.
· Understand that a confidence interval gives a range of plausible values for the parameter.

5, 7, 9, 11, 13

2

8.1 Using Confidence Intervals Wisely, 8.2 Conditions for Estimating p, Constructing a Confidence Interval for p

· Understand why each of the three inference conditions—Random, Normal, and Independent—is important.
· Explain how practical issues like nonresponse, undercoverage, and response bias can affect the interpretation of a confidence interval.
· Construct and interpret a confidence interval for a population proportion.
· Determine critical values for calculating a confidence interval using a table or your calculator.

17, 19–24, 27, 31, 33

3

8.2 Putting It All Together: The FourStep Process, Choosing the Sample Size, Technology: Confidence Intervals for p on the Calculator

· Carry out the steps in constructing a confidence interval for a population proportion: define the parameter; check conditions; perform calculations; interpret results in context.
· Determine the sample size required to obtain a level C confidence interval for a population proportion with a specified margin of error.
· Understand how the margin of error of a confidence interval changes with the sample size and the level of confidence C.
· Understand why each of the three inference conditions—Random, Normal, and Independent—is important.

35, 37, 41, 43, 47

4

8.3 When Is Known: The OneSample z Interval for a Population Mean, When Is Unknown: The t Distributions, Constructing a Confidence Interval for , Technology: Inverse t on the Calculator

· Construct and interpret a confidence interval for a population mean.
· Determine the sample size required to obtain a level C confidence interval for a population mean with a specified margin of error.
· Carry out the steps in constructing a confidence interval for a population mean: define the parameter; check conditions; perform calculations; interpret results in context.

49–52, 55, 57, 59, 63

5

8.3 Using t Procedures Wisely, Technology: Confidence Intervals for on the Calculator

· Understand why each of the three inference conditions—Random, Normal, and Independent—is important.

65, 67, 71, 73, 75–78

6

Chapter 8 Review

· Determine sample statistics from a confidence interval.

Chapter 8 Review Exercises

7

Chapter 8 Test



Chapter 9
Day

Topics

Objectives: Students will be able to:

Homework

1

9.1 The Reasoning of Significance Tests, Stating Hypotheses, Interpreting Pvalues, Statistical Significance

· State correct hypotheses for a significance test about a population proportion or mean.
· Interpret Pvalues in context.

1, 3, 5, 7, 9, 11, 13

2

9.1 Type I and Type II Errors, Planning Studies: The Power of a Statistical Test, Technology: Investigating Power with an Applet

· Interpret a Type I error and a Type II error in context, and give the consequences of each.
· Understand the relationship between the significance level of a test, P(Type II error), and power.

15, 19, 21, 23, 25

3

9.2 Carrying Out a Significance Test, The OneSample z Test for a Proportion, Technology: OneProportion z Test on the Calculator

· Check conditions for carrying out a test about a population proportion.
· If conditions are met, conduct a significance test about a population proportion.

27–30, 41, 43, 45

4

9.2 TwoSided Tests, Why Confidence Intervals Give More Information, Technology: Tests and Confidence Intervals using Minitab

· Use a confidence interval to draw a conclusion for a twosided test about a population proportion.

47, 49, 51, 53, 55

5

9.3 Carrying Out a Significance Test for , The One Sample t Test, TwoSided Tests and Confidence Intervals, Technology: Computing Pvalues from t Distributions on the Calculator, One Sample t Test on the Calculator

· Check conditions for carrying out a test about a population mean.
· If conditions are met, conduct a onesample t test about a population mean .
· Use a confidence interval to draw a conclusion for a twosided test about a population mean.

57–60, 71, 73

6

9.3 Inference for Means: Paired Data, Using Tests Wisely

· Recognize paired data and use onesample t procedures to perform significance tests for such data.

75, 77, 89, 94–97, 99–104

7

Chapter 9 Review


Chapter 9 Review Exercises

8

Chapter 9 Test



Chapter 10
Day

Topics

Objectives: Students will be able to…

Homework

1

Activity: Is Yawning Contagious?, 10.1 The Sampling Distribution of a Difference Between Two Proportions

· Describe the characteristics of the sampling distribution of
· Calculate probabilities using the sampling distribution of

1, 3, 5

2

10.1 Confidence Intervals for p_{1} – p_{2} , Technology: Confidence Intervals for a Difference in Proportions on the Calculator

· Determine whether the conditions for performing inference are met.
· Construct and interpret a confidence interval to compare two proportions.

7, 9, 11, 13

3

10.1 Significance Tests for p_{1} – p_{2}, Inference for Experiments, Technology: Significance Tests for a Difference in Proportions on the Calculator

· Perform a significance test to compare two proportions.
· Interpret the results of inference procedures in a randomized experiment.

15, 17, 21, 23

4

10.2 Activity: Does Polyester Decay?, The Sampling Distribution of a Difference Between Two Means

· Describe the characteristics of the sampling distribution of
· Calculate probabilities using the sampling distribution of

2932, 35, 37, 57

5

10.2 The TwoSample tStatistic, Confidence Intervals for , Technology: Confidence Intervals for a Difference in Means on the Calculator

· Determine whether the conditions for performing inference are met.
· Use twosample t procedures to compare two means based on summary statistics.
· Use twosample t procedures to compare two means from raw data.
· Interpret standard computer output for twosample t procedures.

39, 41, 43, 45

6

10.2 Significance Tests for , Using TwoSample t Procedures Wisely, Technology: Two Sample t Tests with Computer Software and Calculators

· Perform a significance test to compare two means.
· Check conditions for using twosample t procedures in a randomized experiment.
· Interpret the results of inference procedures in a randomized experiment.

51, 53, 59, 65, 6770

7

Chapter 10 Review

· Determine the proper inference procedure to use in a given setting.

Chapter 10 Review Exercises

8

Chapter 10 Test


33^{R}, 34^{ R}, 75^{ R}, 76^{ R}

Chapter 11
Day

Topics

Objectives: Students will be able to…

Homework

1

Activity: The Candy Man Can, 11.1 Comparing Observed and Expected Counts: The ChiSquare Statistic, The ChiSquare Distributions and Pvalues, Technology: Finding Pvalues for ChiSquare Tests on the Calculator

· Know how to compute expected counts, conditional distributions, and contributions to the chisquare statistic.

1, 3, 5

2

11.1 The ChiSquare GoodnessofFit Test, FollowUp Analysis, Technology: ChiSquare GoodnessofFit Tests on the Calculator

· Check the Random, Large sample size, and Independent conditions before performing a chisquare test.
· Use a chisquare goodnessoffit test to determine whether sample data are consistent with a specified distribution of a categorical variable.
· Examine individual components of the chisquare statistic as part of a followup analysis.

7, 9, 11, 17

3

11.2 Comparing Distributions of a Categorical Variable, Expected Counts and the ChiSquare Statistic, The ChiSquare Test for Homogeneity, FollowUp Analysis, Comparing Several Proportions, Technology: ChiSquare Tests for TwoWay Tables with Computer Software and Calculators

· Check the Random, Large sample size, and Independent conditions before performing a chisquare test.
· Use a chisquare test for homogeneity to determine whether the distribution of a categorical variable differs for several populations or treatments.
· Interpret computer output for a chisquare test based on a twoway table.
· Examine individual components of the chisquare statistic as part of a followup analysis.
· Show that the twosample z test for comparing two proportions and the chisquare test for a 2by2 twoway table give equivalent results.

1922, 27, 29, 31, 33, 35, 43

4

11.2 The ChiSquare Test of Association/Independence, Using ChiSquare Tests Wisely

· Check the Random, Large sample size, and Independent conditions before performing a chisquare test.
· Use a chisquare test of association/independence to determine whether there is convincing evidence of an association between two categorical variables.
· Interpret computer output for a chisquare test based on a twoway table.
· Examine individual components of the chisquare statistic as part of a followup analysis.

45, 49, 51, 5358

5

Chapter 11 Review

· Distinguish between the three types of chisquare tests.

Chapter 11 Review Exercises

6

Chapter 11 Test


59^{R}, 60^{R}

Chapter 12
Day

Topics

Objectives: Students will be able to…

Homework

1

Activity: The Helicopter Experiment, 12.1 The Sampling Distribution of b, Conditions for Regression Inference

· Check conditions for performing inference about the slope of the population regression line.

1, 3

2

12.1 Estimating Parameters, Constructing a Confidence Interval for the Slope, Technology: Regression Inference using Computer Software and Calculators

· Interpret computer output from a leastsquares regression analysis.
· Construct and interpret a confidence interval for the slope of the population regression line.

5, 7, 9, 11

3

12.1 Performing a Significance Test for the Slope

· Perform a significance test about the slope of a population regression line.

13, 15, 17, 19

4

12.2 Transforming with Powers and Roots, Technology: Transforming to Achieve Linearity on the Calculator

· Use transformations involving powers and roots to achieve linearity for a relationship between two variables.
· Make predictions from a leastsquares regression line involving transformed data.

2126, 33, 35

5

12.2 Transforming with Logarithms

· Use transformations involving logarithms to achieve linearity for a relationship between two variables.
· Make predictions from a leastsquares regression line involving transformed data.
· Determine which of several transformations does a better job of producing a linear relationship.

37, 39, 41, 4548

6

Chapter 12 Review


Chapter 12 Review Exercises

7

Chapter 12 Test


Cumulative AP Practice Test 4

AP EXAM REVIEW (10 days)
 Practice AP Free Response Questions
 Choosing the Correct Inference Procedure
 Mock Grading Sessions
 Rubric development by student teams
 Practice Multiple Choice Questions
AP STATISTICS EXAM (1 DAY)
AFTER THE AP EXAM: FINAL PROJECT (See rubric on page 16)
Purpose: The purpose of this project is for you to actually do statistics. You are to form a hypothesis, design a study, conduct the study, collect the data, describe the data, and make conclusions using the data. You are going to do it all!!
Topics: You may do your study on any topic, but you must be able to do all 6 steps listed above. Make it interesting and note that degree of difficulty is part of the grade.
Group Size: You may work alone or with a partner for this project.
Proposal (20 points): To get your project approved, you must be able to demonstrate how your study will meet the requirements of the project. In other words, you need to clearly and completely communicate your hypotheses, your explanatory and response variables, the test/interval you will use to analyze the results, and how you will collect the data so the conditions for inference will be satisfied. You must also make sure that your study will be safe and ethical if you are using human subjects. This should be typed. If your proposal isn’t approved, you must resubmit the proposal for partial credit until it is approved.
Poster (80 points):
The key to a good statistical poster is communication and organization. Make sure all components of the poster are focused on answering the question of interest and that statistical vocabulary is used correctly. The poster should include:
 Title (in the form of a question).
 Introduction. In the introduction you should discuss what question you are trying to answer, why you chose this topic, what your hypotheses are, and how you will analyze your data.
 Data Collection. In this section you will describe how you obtained your data. Be specific.
 Graphs, Summary Statistics and the Raw Data (if numerical). Make sure the graphs are well labeled, easy to compare, and help answer the question of interest. You should include a brief discussion of the graphs and interpretations of the summary statistics.
 Discussion and Conclusions. In this section, you will state your conclusion (with the name of the test, test statistic and Pvalue) and you should discuss why your inference procedure is valid. You should also discuss any errors you made, what you could do to improve the study next time, and any other critical reflections
 Live action pictures of your data collection in progress.
Presentation: Each individual will be required to give a 5 minute oral presentation to the class.
RUBRIC FOR CHAPTER 4 PROJECT:
Chapter 4 Project

4 = Complete

3 = Substantial

2 = Developing

1 = Minimal

Introduction

· Describes the context of the research
· Has a clearly stated question of interest
· Provides a hypothesis about the answer to the question of interest
· Question of interest is of appropriate difficulty

· Introduces the context of the research and has a specific question of interest
· Suggests hypothesis OR has appropriate difficulty

· Introduces the context of the research and has a specific question of interest OR has question of interest and a hypothesis

· Briefly describes the context of the research

Data Collection

· Method of data collection is clearly described
· Includes appropriate randomization
· Describes efforts to reduce bias, variability, confounding
· Quantity of data collected is appropriate

· Method of data collection is clearly described
· Some effort is made to incorporate principles of good data collection
· Quantity of data is appropriate

· Method of data collection is described
· Some effort is made to incorporate principles of good data collection

· Some evidence of data collection

Graphs and Summary Statistics

· Appropriate graphs are included (to help answer the question of interest)
· Graphs are neat, clearly labeled, and easy to compare
· Appropriate summary statistics are included
· Summary statistics are discussed and correctly interpreted

· Appropriate graphs are included (to help answer the question of interest)
· Graphs are neat, clearly labeled, and easy to compare
· Appropriate summary statistics are included

· Graphs and summary statistics are included

· Graphs or summary statistics are included

Conclusions

· Uses the results of the study to correctly answer question of interest
· Discusses what inferences are appropriate based on study design
· Shows good evidence of critical reflection (discusses possible errors, shortcomings, limitations, alternate explanations, etc.)

· Makes a correct conclusion
· Discusses what inferences are appropriate
· Shows some evidence of critical reflection

· Makes a partially correct conclusion
· Shows some evidence of critical reflection

· Makes a conclusion

Overall Presentation/ Communication

· Clear, holistic understanding of the project
· Poster is well organized, neat and easy to read
· Statistical vocabulary is used correctly
· Poster is visually appealing

· Clear, holistic understanding of the project
· Statistical vocabulary is used correctly
· Poster is unorganized or isn’t visually appealing,

· Poster is not well done or communication is poor

· Communication and organization are very poor

RUBRIC FOR FINAL PROJECT:
Final Project

4 = Complete

3 = Substantial

2 = Developing

1 = Minimal

Introduction

· Describes the context of the research
· Has a clearly stated question of interest
· Clearly defines the parameter of interest and states correct hypotheses
· Question of interest is of appropriate difficulty

· Introduces the context of the research and has a specific question of interest
· Has correct parameter/ hypotheses OR has appropriate difficulty

· Introduces the context of the research and has a specific question of interest OR has question of interest and hypotheses

· Briefly describes the context of the research

Data Collection

· Method of data collection is clearly described
· Includes appropriate randomization
· Describes efforts to reduce bias, variability, confounding
· Quantity of data collected is appropriate

· Method of data collection is clearly described
· Some effort is made to incorporate principles of good data collection
· Quantity of data is appropriate

· Method of data collection is described
· Some effort is made to incorporate principles of good data collection

· Some evidence of data collection

Graphs and Summary Statistics

· Appropriate graphs are included (to help answer the question of interest)
· Graphs are neat, clearly labeled, and easy to compare
· Appropriate summary statistics are included
· Summary statistics are discussed and correctly interpreted

· Appropriate graphs are included (to help answer the question of interest)
· Graphs are neat, clearly labeled, and easy to compare
· Appropriate summary statistics are included

· Graphs and summary statistics are included

· Graphs or summary statistics are included

Analysis

· Correct inference procedure is chosen
· Use of inference procedure is justified
· Test statistic/pvalue or confidence interval is calculated correctly
· pvalue or confidence interval is interpreted correctly

· Correct inference procedure is chosen
· Lacks justification, lacks interpretation, or makes a calculation error

· Correct inference procedure is chosen
· Test statistic/pvalue or confidence interval is calculated correctly

· Inference procedure is attempted

Conclusions

· Uses pvalue/confidence interval to correctly answer question of interest
· Discusses what inferences are appropriate based on study design
· Shows good evidence of critical reflection (discusses possible errors, shortcomings, limitations, alternate explanations, etc.)

· Makes a correct conclusion
· Discusses what inferences are appropriate
· Shows some evidence of critical reflection

· Makes a partially correct conclusion (such as accepting null).
· Shows some evidence of critical reflection

· Makes a conclusion


· Clear, holistic understanding of the project
· Poster is well organized, neat and easy to read
· Statistical vocabulary is used correctly
· Poster is visually appealing

· Clear, holistic understanding of the project
· Statistical vocabulary is used correctly
· Poster is unorganized or isn’t visually appealing,

· Poster is not well done or communication is poor

· Communication and organization are very poor
