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Jonna Spiller Bonham ISDBonham High School Bonham, Texas

AP Statistics Syllabus

Course Description:AP Statistics is the high school equivalent of a one semester, introductory college statistics course. In this course, students develop strategies for collecting, organizing, analyzing, and drawing conclusions from data. Students design, administer, and tabulate results from surveys and experiments. Probability and simulations aid students in constructing models for chance behavior. Sampling distributions provide the logical structure for confidence intervals and hypothesis tests. Students use a TI-84 graphing calculator, Fathom, statistical software, and Web-based java applets to investigate statistical concepts. To develop effective statistical communication skills, students are required to prepare frequent written and oral analyses of real data.

Course Goals: In AP Statistics, students are expected to learn

Skills• To produce convincing oral and written statistical arguments, using appropriate

terminology, in a variety of applied settings.• When and how to use technology to aid them in solving statistical problemsKnowledge• Essential techniques for producing data (surveys, experiments, observational studies),

analyzing data (graphical & numerical summaries), modeling data (probability, random variables, sampling distributions), and drawing conclusions from data (inference procedures – confidence intervals and significance tests)

Habits of mind• To become critical consumers of published statistical results by heightening their

awareness of ways in which statistics can be improperly used to mislead, confuse, or distort the truth.

Course Outline

This course will cover the AP Statistics course description primarily through the use the following resources:

The Practice of Statistics (5th edition), by Starnes, Tabor, Yates, and Moore, W. H. Freeman & Co., 2014.

College Board. AP Statistics Free Response Problems. New Jersey: College Board, 2014.

Timeline based on 90 minute block periods.Chapter 1 - Exploring Data

Day Topics Learning Objectives Students will be able to … Suggested assignment

1 Chapter 1 Introduction • Identify the individuals and variables in a set of data.• Classify variables as categorical or quantitative.

1, 3, 5, 7, 8

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

• Display categorical data with a bar graph. Decide if it would be appropriate to make a pie chart.

• Identify what makes some graphs of categorical data deceptive.

11, 13, 15, 17

3

1.1 Two-Way Tables and Marginal Distributions, Relationships between Categorical Variables: Conditional Distributions

• Calculate and display the marginal distribution of a categorical variable from a two-way table.

• Calculate and display the conditional distribution of a categorical variable for a particular value of the other categorical variable in a two-way table.

• Describe the association between two categorical variables by comparing appropriate conditional distributions.

19, 21, 23, 25, 27–32

41.2 Dotplots, Describing Shape, Comparing Distributions, Stemplots

• Make and interpret dotplots and stemplots of quantitative data.

• Describe the overall pattern (shape, center, and spread) of a distribution and identify any major departures from the pattern (outliers).

• Identify the shape of a distribution from a graph as roughly symmetric or skewed.

• Compare distributions of quantitative data using dotplots or stemplots.

37, 39, 41, 43, 45, 47

5 1.2 Histograms, Using Histograms Wisely

• Make and interpret histograms of quantitative data.• Compare distributions of quantitative data using

histograms.

53, 55, 59, 60, 65, 69–74

6

1.3 Measuring Center: Mean and Median, Comparing the Mean and Median, Measuring Spread: Range and IQR, Identifying Outliers, Five-Number Summary and Boxplots

• Calculate measures of center (mean, median).• Calculate and interpret measures of spread (range, IQR).• Choose the most appropriate measure of center and

spread in a given setting.• Identify outliers using the 1.5×IQR rule.• Make and interpret boxplots of quantitative data.

79, 81, 83, 87, 89, 91, 93

7

1.3 Measuring Spread: Standard Deviation, Choosing Measures of Center and Spread, Organizing a Statistics Problem

• Calculate and interpret measures of spread (standard deviation).

• Choose the most appropriate measure of center and spread in a given setting.

• Use appropriate graphs and numerical summaries to compare distributions of quantitative variables.

95, 97, 99, 103, 105, 107–110

8 Chapter 1 Review/FRAPPY! Chapter 1

Review Exercises

9 Chapter 1 Test

Activities and Projects:* M&M Simulation (see activities section) * Phone Contacts (see activities section) - * Card Matching (see activities section)

Chapter 2 - Modeling Distributions of Data

Day Topics Learning Objectives Students will be able to… Suggested assignment

1

2.1 Measuring Position: Percentiles; Cumulative Relative Frequency Graphs; Measuring Position: z-scores

• Find and interpret the percentile of an individual value within a distribution of data.

• Estimate percentiles and individual values using a cumulative relative frequency graph.

• Find and interpret the standardized score (z-score) of an individual value within a distribution of data.

1, 3, 5, 9, 11, 13, 15

2 2.1 Transforming Data• Describe the effect of adding, subtracting, multiplying

by, or dividing by a constant on the shape, center, and spread of a distribution of data.

17, 19, 21, 23, 25–30

32.2 Density Curves, The 68–95–99.7 Rule; The Standard Normal Distribution

• Estimate the relative locations of the median and mean on a density curve.

• Use the 68–95–99.7 rule to estimate areas (proportions of values) in a Normal distribution.

• Use Table A or technology to find (i) the proportion of z-values in a specified interval, or (ii) a z-score from a percentile in the standard Normal distribution.

33, 35, 39, 41, 43, 45, 47, 49,

51

4 2.2 Normal Distribution Calculations

• Use Table A or technology to find (i) the proportion of values in a specified interval, or (ii) the value that corresponds to a given percentile in any Normal distribution.

53, 55, 57, 59

5 2.2 Assessing Normality • Determine if a distribution of data is approximately Normal from graphical and numerical evidence.

54, 63, 65, 66, 67, 69–74

6 Chapter 2 Review/FRAPPY!Chapter 2 Review

Exercises

7 Chapter 2 Test

Activities and Projects:* Exploratory Data Analysis Project (see projects section)

Chapter 3 - Describing Relationships


1

Chapter 3 Introduction3.1 Explanatory and response variables, displaying relationships: scatterplots, describing scatterplots

• 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 a relationship displayed in a scatterplot and recognize outliers in a scatterplot.

1, 5, 7, 11, 13

23.1 Measuring linear association: correlation, facts about correlation

• Interpret the correlation.• Understand the basic properties of correlation,

including how the correlation is influenced by outliers.

• Use technology to calculate correlation.• Explain why association does not imply causation.

14–18, 21

33.2 Least-squares regression, interpreting a regression line, prediction, residuals

• Interpret the slope and y intercept of a least-squares regression line.

• Use the least-squares regression line to predict y for a given x. Explain the dangers of extrapolation.

• Calculate and interpret residuals.

27–32, 35, 37, 39, 41, 45

4

3.2 Calculating the equation of the least-squares regression line, determining whether a linear model is appropriate: residual plots

• Explain the concept of least squares.• Determine the equation of a least-squares regression

line using technology.• Construct and interpret residual plots to assess if a

linear model is appropriate.

43, 47, 49, 51

53.2 How well the line fits the data: the role of s and r2 in regression

• Interpret the standard deviation of the residuals and

and use these values to assess how well the least-squares regression line models the relationship between two variables.

48, 50, 55, 58

6

3.2 Interpreting computer regression output, regression to the mean, correlation and regression wisdom

• Determine the equation of a least-squares regression line using computer output.

• Describe how the slope, y intercept, standard

deviation of the residuals, and are influenced by outliers.

• Find the slope and y intercept of the least-squares regression line from the means and standard deviations of x and y and their correlation.

59, 61, 63, 65, 69, 71–78

7 Chapter 3 Review/FRAPPY!Chapter Review

Exercises

8 Chapter 3 Test

Activities and Projects:* Guess My Correlation (istics.net/correlation)* Correlation Stations (see activities section)* Phone Contacts (see activities section)

http://istics.net/correlation

Chapter 4 - Designing Studies


1

4.1 Introduction, The Idea of a Sample Survey, How to Sample Badly, How to Sample Well: Simple Random Sampling

• Identify the population and sample in a statistical study. • Identify voluntary response samples and convenience

samples. Explain how these sampling methods can lead to bias.

• Describe how to obtain a random sample using slips of paper, technology, or a table of random digits.

1, 3, 5, 7, 9, 11

2 4.1 Other Random Sampling Methods

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

13, 17, 19, 21, 23, 25

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

• Explain how undercoverage, nonresponse, question wording, and other aspects of a sample survey can lead to bias.

27, 29, 31, 33, 35

44.2 Observational Study versus Experiment, The Language of Experiments

• Distinguish between an observational study and an experiment.

• Explain the concept of confounding and how it limits the ability to make cause-and-effect conclusions.

37–42, 45, 47, 49, 51,

53, 55

5

4.2 How to Experiment Badly, How to Experiment Well, Completely Randomized Designs

• Identify the experimental units, explanatory and response variables, and treatments.

• Explain the purpose of comparison, random assignment, control, and replication in an experiment.

• Describe a completely randomized design for an experiment, including how to randomly assign treatments using slips of paper, technology, or a table of random digits.

57, 59, 61, 63, 65

64.2 Experiments: What Can Go Wrong? Inference for Experiments

• Describe the placebo effect and the purpose of blinding in an experiment.

• Interpret the meaning of statistically significant in the context of an experiment.

67, 69, 71, 73

7 4.2 Blocking• Explain the purpose of blocking in an experiment.• Describe a randomized block design or a matched pairs

design for an experiment.

75, 77, 79, 81, 85

84.3 Scope of Inference, The Challenges of Establishing Causation

• Describe the scope of inference that is appropriate in a statistical study.

83, 87–94, 97–104

9 4.3 Data Ethics (optional topic)

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

Chapter 4 Review

Exercises

10 Chapter 4 Review/FRAPPY!Chapter 4

AP® Practice Exam

11 Chapter 4 TestCumulative AP Practice

Test 1

Activities and Projects:* Show Me the Money (AP Statistics College Board Curriculum Module)* The River (see activities section)* Article Assignments (see activities section)* Bias Project (see projects section)

Chapter 5 - Probability: What are the Chances?


1 5.1 The Idea of Probability, Myths about Randomness • Interpret probability as a long-run relative frequency. 1, 3, 7, 9, 11

2 5.1 Simulation • Use simulation to model chance behavior.15, 17, 19, 23,

25

3 5.2 Probability Models, Basic Rules of Probability

• Determine 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, 39, 41, 43, 45, 47

4

5.2 Two-Way Tables, Probability, and the General Addition Rule, Venn Diagrams and Probability

• Use a two-way table or Venn diagram to model a chance process and calculate probabilities involving two events.

• Use the general addition rule to calculate probabilities.

29, 33–36, 49, 51, 53, 55

5

5.3 What Is Conditional Probability?, The General Multiplication Rule and Tree Diagrams,

• Calculate and interpret conditional probabilities.• Use the general multiplication rule to calculate

probabilities.• Use tree diagrams to model a chance process and

calculate probabilities involving two or more events.

57–60, 63, 65, 67, 71, 73, 77,

79

65.3 Conditional Probability and Independence: A Special Multiplication Rule

• Determine whether two events are independent.• When appropriate, use the multiplication rule for

independent events to compute probabilities.

81, 83, 85, 89, 91, 93, 95,

97–99


Exercises

8 Chapter 5 Test

Activities and Projects:* Egg Smashing (see activities section)

Chapter 6 - Random Variables


1

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

• Compute probabilities using the probability distribution of a discrete random variable.

• Calculate and interpret the mean (expected value) of a discrete random variable.

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

2

6.1 Standard Deviation (and Variance) of a Discrete Random Variable, Continuous Random Variables

• Calculate and interpret the standard deviation of a discrete random variable.

• Compute probabilities using the probability distribution of a continuous random variable.

14, 15, 17, 18, 21, 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.

27–30, 35, 37, 39–41, 43, 45

46.2 Combining Random Variables, Combining Normal Random Variables

• Find the mean and standard deviation of the sum or difference of independent random variables.

• Find probabilities involving the sum or difference of independent Normal random variables.

47, 49, 51, 53, 55, 57–59, 61

56.3 Binomial Settings and Binomial Random Variables, Binomial Probabilities

• Determine whether the conditions for using a binomial random variable are met.

• Compute and interpret probabilities involving binomial distributions.

63, 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

• Find probabilities involving geometric random variables.

93, 95, 97, 99, 101–104


Exercises

9 Chapter 6 Test

Activities and Projects:* Supercilious Test (see activities section)

EXAM REVIEW: 3 DAYS

SEMESTER 1 EXAM: Simulated AP format with Multiple Choice, Free Response

Chapter 7 - Sampling Distributions


1Introduction: German Tank Problem, 7.1 Parameters and Statistics

• Distinguish between a parameter and a statistic. 1, 3, 5

27.1 Sampling Variability, Describing Sampling Distributions

• Distinguish among the distribution of a population, the distribution of a sample, and the sampling distribution of a statistic.

• Use the sampling distribution of a statistic to evaluate a claim about a parameter.

• Determine whether or not a statistic is an unbiased estimator of a population parameter.

• Describe the relationship between sample size and the variability of a statistic.

7, 9, 11, 13, 15, 17, 19

3

7.2 The Sampling Distribution

of , Using the Normal

Approximation for .

• Find the mean and standard deviation of the sampling

distribution of a sample proportion . Check the

10% condition before calculating .

• Determine if the sampling distribution of is approximately Normal.

• If appropriate, use a Normal distribution to calculate

probabilities involving .

21–24, 27, 29, 33, 35, 37, 39

4

7.3 The Sampling Distribution of : Mean and Standard Deviation, Sampling from a Normal Population

• Find the mean and standard deviation of the sampling distribution of a sample mean . Check the 10%

condition before calculating .• If appropriate, use a Normal distribution to calculate


43–46, 49, 51, 53, 55

5 7.3 The Central Limit Theorem

• Explain how the shape of the sampling distribution of is affected by the shape of the population

distribution and the sample size. • If appropriate, use a Normal distribution to calculate


57, 59, 61, 63, 65–68


Exercises

7 Chapter 7 Test Cumulative AP®

Practice Exam 2

InferenceThroughout the rest of the course these ideas will be developed:

• Ideas of bias and variability will be discussed and explored.• Concepts of confidence intervals and confidence levels will be developed.• The logic of hypothesis testing will be developed. Also concepts regarding p-values, one- and two-sided tests, p-values, Type I and II errors, and power.• Graphing calculators will be used to create confidence intervals and calculate hypothesis tests.• Exercises in the textbook will provide computer output of both intervals and tests. These results will be read and interpreted.• Necessary conditions for inference will be discussed for each inference procedure.

Chapter 8 - Estimating With Confidence

Day Topics Learning objectives Students will be able to… Suggested assignment

1

Chapter 8 Introduction; 8.1 The Idea of a Confidence Interval, Interpreting Confidence Intervals and Confidence Levels

• Interpret a confidence interval in context.• Interpret a confidence level in context.

1, 3, 5, 7, 9

28.1 Constructing a Confidence Interval; Using Confidence Intervals Wisely

• Determine the point estimate and margin of error from a confidence interval.

• Describe how the sample size and confidence level affect the length of a confidence interval.

• Explain how practical issues like nonresponse, undercoverage, and response bias can affect the interpretation of a confidence interval.

10, 11, 13, 15, 17, 19

3

8.2 Conditions for Estimating p, Constructing a Confidence Interval for p, Putting It All Together: The Four-Step Process

• State and check the Random, 10%, and Large Counts conditions for constructing a confidence interval for a population proportion.

• Determine critical values for calculating a C% confidence interval for a population proportion using a table or technology.

• Construct and interpret a confidence interval for a population proportion.

20–24, 31, 33, 35, 37

4 8.2 Choosing the Sample Size

• Determine the sample size required to obtain a C% confidence interval for a population proportion with a specified margin of error.

39, 41, 43, 45, 47

5

8.3 The Problem of unknown , When Is Unknown:

The t Distributions, Conditions for Estimating

• Explain how the t distributions are different from the standard Normal distribution and why it is necessary to use a t distribution when calculating a confidence interval for a population mean.

• Determine critical values for calculating a C% confidence interval for a population mean using a table or technology.

• State and check the Random, 10%, and Normal/Large Sample conditions for constructing a confidence interval for a population mean.

49–52, 55, 57, 59

68.3 Constructing a Confidence Interval for , Choosing a Sample Size

• Construct and interpret a confidence interval for a population mean.

• Determine the sample size required to obtain a C% confidence interval for a population mean with a specified margin of error.

61, 65, 69, 71, 73, 75–78


Exercises

8 Chapter 8 Test

Activities and Projects:* Hershey Kisses (see activities section)* Inference Templates (see activities section)

Chapter 9 - Testing a Claim


1

9.1 Stating Hypotheses, The Reasoning of Significance Tests, Interpreting P-values, Statistical Significance

• State the null and alternative hypotheses for a significance test about a population parameter.

• Interpret a P-value in context.• Determine if the results of a study are statistically

significant and draw an appropriate conclusion using a significance level.

1, 3, 5, 7, 9, 11, 15

2 9.1 Type I and Type II Errors • Interpret a Type I and a Type II error in context, and give a consequence of each.

13, 17, 19, 21, 23

3

9.2 Carrying Out a Significance Test, The One-Sample z Test for a Proportion

• State and check the Random, 10%, and Large Counts conditions for performing a significance test about a population proportion.

• Perform a significance test about a population proportion.

25–28, 31, 35, 39, 41

4

9.2 Two-Sided Tests, Why Confidence Intervals Give More Information, Type II Error and the Power of a Test

• Use a confidence interval to draw a conclusion for a two-sided test about a population parameter.

• Interpret the power of a test and describe what factors affect the power of a test.

• Describe the relationship among the probability of a Type I error (significance level), the probability of a Type II error, and the power of a test.

43, 45, 47, 51, 53, 55, 57

5

9.3 Carrying Out a Significance Test for , The One Sample t Test, Two-Sided Tests and Confidence Intervals

• State and check the Random, 10%, and Normal/Large Sample conditions for performing a significance test about a population mean.

• Perform a significance test about a population mean.• Use a confidence interval to draw a conclusion for a

two-sided test about a population parameter.

59–62, 65, 69, 73, 77, 79

69.3 Inference for Means: Paired Data, Using Tests Wisely

• Perform a significance test about a mean difference using paired data.

83, 85, 87, 89–91, 93, 95–102


Exercises

8 Chapter 9 Test

Activities and Projects:* Power Dominoes (see activities section)* Buckets of Beads (see activities section)

Chapter 10 - Comparing Two Populations or Groups


1

“Is Yawning Contagious?” Activity, 10.1 The Sampling Distribution of a Difference between Two Proportions

• Describe the shape, center, and spread of the sampling

distribution of 1, 3

210.1 Confidence Intervals for

• Determine whether the conditions are met for doing

inference about • Construct and interpret a confidence interval to

compare two proportions.

5, 7, 9, 11

3

10.1 Significance Tests for

Inference for Experiments

• Perform a significance test to compare two proportions.

13, 15, 17, 21, 23

4

10.2 “Does Polyester Decay?” Activity, The Sampling Distribution of a Difference between Two Means

• Describe the shape, center, and spread of the sampling

distribution of • Determine whether the conditions are met for doing

inference about

31, 33, 35, 51

5

10.2 The Two-Sample t Statistic, Confidence Intervals

for

• Construct and interpret a confidence interval to compare two means.

25–28, 37, 39

6

10.2 Significance Tests for

, Using Two-Sample t Procedures Wisely

• Perform a significance test to compare two means.• Determine when it is appropriate to use two-sample t

procedures versus paired t procedures.

41, 43, 45, 47, 53, 57–60

7 Chapter 10 Review/ FRAPPY!

Chapter 10 Review

Exercises


Practice Exam 3

Activities and Projects:* Mythbusters Yawning (see activities section)

Chapter 11 - Inference for Distributions of Categorical Data

Day Topics Learning objectives Students will be able to… Suggested assignment

1

Activity: The Candy Man Can; 11.1 Comparing Observed and Expected Counts: The Chi-Square Statistic; The Chi-Square Distributions and P-values

• State appropriate hypotheses and compute expected counts for a chi-square test for goodness of fit.

• Calculate the chi-square statistic, degrees of freedom, and P-value for a chi-square test for goodness of fit.

1, 3, 5

2 11.1 Carrying Out a Test; Follow-Up Analysis

• Perform a chi-square test for goodness of fit.• Conduct a follow-up analysis when the results of a

chi-square test are statistically significant.7, 9, 11, 15, 17

3

11.2 Comparing Distributions of a Categorical Variable; Expected Counts and the Chi-Square Statistic; The Chi-Square Test for Homogeneity

• Compare conditional distributions for data in a two-way table.

• State appropriate hypotheses and compute expected counts for a chi-square test based on data in a two-way table.

• Calculate the chi-square statistic, degrees of freedom, and P-value for a chi-square test based on data in a two-way table.

• Perform a chi-square test for homogeneity.

19–22, 27, 29, 31, 33, 35, 37,

39

4

11.2 Relationships between Two Categorical Variables; the Chi-Square Test for Independence; Using Chi-Square Tests Wisely

• Perform a chi-square test for independence.• Choose the appropriate chi-square test.

41, 43, 45, 47, 49, 51–55


Chapter 11 Review

Exercises

6 Chapter 11 Test

Activities and Projects:* Fruit Loops Investigation (see activities section)

Chapter 12 - More About Regression


1

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

• Check the conditions for performing inference about the slope of the population (true) regression line.

1, 3

2

12.1 Estimating the Parameters; Constructing a Confidence Interval for the Slope

• Interpret the values of a, b, s, , and in context, and determine these values from computer output.

• Construct and interpret a confidence interval for the slope of the population (true) regression line.

5, 7, 9, 11

312.1 Performing a Significance Test for the Slope

• Perform a significance test about the slope of the population (true) regression line.

13, 15, 17

4 12.2 Transforming with Powers and Roots

• Use transformations involving powers and roots to find a power model that describes the relationship between two variables, and use the model to make predictions.

19–24, 31, 33

5

12.2 Transforming with Logarithms; Putting it all Together: Which Transformation Should We Choose?

• Use transformations involving logarithms to find a power model or an exponential model that describes the relationship between two variables, and use the model to make predictions.

• Determine which of several transformations does a better job of producing a linear relationship.

35, 37, 39, 41, 43, 45, 47–50


Chapter 12 Review

Exercises


Practice Test 4

AP EXAM REVIEW (10 days)• Practice AP Free Response Questions• Choosing the correct inference procedure• Flash cards• Mock grading sessions• Rubric development by student teams• Practice multiple choice questions• Practice AP Exam given as a cumulative assessment and graded by AP rubrics.• Test Prep Reflections• Super 6 AP free response problems• Stats4Stem problems

After the AP® Exam: Final Project - (See projects section)

Teaching Strategies• Data is frequently collected from the class. On the first day of class, students fill out a

short questionnaire. This data is used throughout the first unit. Many topics are introduced by asking students to provide simple data about themselves and this data is used to open the day’s lesson.

• Students use graphing calculators throughout the course. • Students are given a copy of the official formula sheet (in a sheet protector) when

probability begins and annotate on it throughout the rest of the course. A class set of formula sheets is used (un-annotated) for assessments.

• When learning inference, students do confidence intervals and hypothesis tests on a template sheet (see activity resources) that walks them through the steps for inference: name, hypotheses (tests only), check of conditions, formulas and math work, and conclusion. Students use this template throughout the inference units and even on many of assessments. During review time for the exam, students are weaned from the template, but by this time the steps for inference are automatic.

• Students are given a statistical article to read and evaluate monthly.

Homework policy Homework is a means to an end: learning Statistics. As such, some students may need to practice for many hours and some students very little. The only graded assignments in the course will be projects, quizzes and tests. Students are encouraged to do as much homework as they need to learn the material. Learning is the key. In fact, if you keep working and learning, your grade will be adjusted to reflect your growth. For example, a low quiz score can be replaced with a higher test score. Or, more dramatically, if you want to come work after school, I will offer reassessments that can raise your grade. It’s all about you learning to think statistically and showing me that you have accomplished this!

Web Sites Used:• Least squares regression demonstration:http://www.dynamicgeometry.com/javasketchpad/gallery/pages/least_squares.php• Linear regression influential point applethttp://www.math.csusb.edu/faculty/stanton/m262/regress/regress.html• Guess my correlation applet:http://www.stat.uiuc.edu/courses/stat100//java/GCApplet/GCAppletFrame.html• Type I and II and power applet:http://wise.cgu.edu/power/power_applet.html• StatCrunch applets and demonstrationshttps://media.pearsoncmg.com/aw/aw_bock_statsmodel_4/statcrunch_demos/StatCrunch_Demos_and_Activities.pdf

• CLT demonstration applethttp://onlinestatbook.com/stat_sim/sampling_dist/

• SMW4e applet pagehttps://media.pearsoncmg.com/aw/aw_bock_statsmodel_4/cw/stat4b_references.html

https://media.pearsoncmg.com/aw/aw_bock_statsmodel_4/cw/stat4b_references.html

http://onlinestatbook.com/stat_sim/sampling_dist/

https://media.pearsoncmg.com/aw/aw_bock_statsmodel_4/statcrunch_demos/StatCrunch_Demos_and_Activities.pdf

https://media.pearsoncmg.com/aw/aw_bock_statsmodel_4/statcrunch_demos/StatCrunch_Demos_and_Activities.pdf

http://wise.cgu.edu/power/power_applet.html

http://www.stat.uiuc.edu/courses/stat100//java/GCApplet/GCAppletFrame.html

http://www.math.csusb.edu/faculty/stanton/m262/regress/regress.html

http://www.dynamicgeometry.com/javasketchpad/gallery/pages/least_squares.php

Resource Books:

Bock, David E., Paul F. Velleman and Richard D. DeVeaux. Stats: Modeling the World. 4e, Boston: Pearson/Addison-Wesley, 2014.

Peck, Roxy, Chris Olsen and Jay Devore. Introduction to Statistics and Data Analysis. Belmont: Brooks/Cole—Thomson Learning, 2004.

Activities

M&M simulation

The story: My boyfriend fed me some M&M’s. 3 out of 5 were green (which means she got 3 kisses!). Could this happen by chance? Or did she trick me?

• Try with a bag of candy.

• Count the number of green out of 5.

• Make a dotplot.

• Simulate.

Phone Contacts and Quantitative Graphs

The idea: Students count the number of contacts in their phone. They graph these data using all the different quantitative displays. Strengths and weaknesses of the different displays can then be contrasted.

• Students open phones and class defines what constitutes “a contact”.

• Students count contacts and data is presented to the students.

• Discuss why we want to use graphical displays.

• In groups, students create graphs on butcher paper. (histogram, dotplot, stem and leaf, boxplot)

• Graphs are presented and discussed.

• This is a good time to use xkcd.com/833

Matching Boxplots, Histograms, & Summary Statistics

• Copy examples of boxplots, histograms, and summary statistics on cardstock. They can be laminated to preserve them for future use.

• Have students work with a partner to match the boxplot, histogram, & summary statistics

• Provide each pair with a page to record the matches

Correlation Stations

Station One

On Station One’s poster, with x values 0-31 and y values 0-31, x equals the day of the month of your birthday and y represents the number of days left after your birthday in your birth month.

Number of Days in each Month:January – 31 days February - 29 daysMarch – 31 days April – 30 daysMay – 31 days June – 30 daysJuly – 31 days August – 31 daysSeptember – 30 days October – 31 daysNovember – 30 days December – 31 days

For example, Mrs. Spiller’s birthday is on March 8th (FYI, I love cookies), so her point would be at:

X = L1 = Birthday = 8Y = L2 = Number of days left in month = 23

Station Two

Measure your forearm in millimeters.Measure your forearm + your hand in millimeters.

X = L1 = forearmY = L2 = forearm + hand

Station ThreeStation Three

Station Three

Make a circle of cheerios. Count the number it takes to make the circle. Count the number it takes to make the diameter.

X = L1 = diameterY = L2 = # around

Station Four

Make a circle FULL of cheerios. Count the number it takes to fill entire the circle. Count the number it takes to make the radius.

X = L1 = radiusY = L2 = total # to make the circle

Station Five

Roll the die. This is the number of knots you will tie in the wire. After tying the knots, measure the length of the wire in inches. Don’t forget to untie the knots before leaving this station!!

X = L1 = # of knotsY = L2 = length of wire

Station Six

Take a bite of a red vine. Measure the remaining piece in cm.

Weigh it. Repeat (two data points per team)

X = L1 = length (cm)Y = L2 = weight (g)

Station Seven

X = L1 = # of letters in your first nameY = L2 = length of the longest hair on your head (inches)

Station Eight

X = L1 = shoe sizeY = L2 = length of the longest hair on your head (inches)

Graph your data from each station on the posters provided.

The River

An Exercise in Sampling: Rolling Down the River

A farmer has just cleared a new field for corn. It is a unique plot of land in that a river runs along one side. The corn looks good in some areas of the field but not others. The farmer is not sure that harvesting the field is worth the expense. He has decided to harvest 10 plots and use this information to estimate the total yield. Based on this estimate, he will decide whether to harvest the remaining plots.

A. Method Number One: Convenience Sample

The farmer began by choosing 10 plots that would be easy to harvest. They are marked on the grid below:

Since then, the farmer has had second thoughts about this selection and has decided to come to you (knowing that you are an AP statistics student, somewhat knowledgeable, but far cheaper than a professional statistician) to determine the approximate yield of the field.

You will still be allowed to pick 10 plots to harvest early. Your job is to determine which of the following methods is the best one to use – and to decide if this is an improvement over the farmer’s original plan.

B. Method Number Two: Simple Random Sample

Use your calculator or a random number table to choose 10 plots to harvest. Mark them on the grid below, and describe your method of selection.

C. Method Number Three: Stratified Sample

Consider the field as grouped in vertical columns (called strata). Using your calculator or a random number table, randomly choose one plot from each vertical column and mark these plots on the grid.

D. Method Number Four: Stratified Sample

Consider the field as grouped in horizontal rows (also called strata). Using your calculator or a random number table, randomly choose one plot from each horizontal row and mark these plots on the grid.

OK, the crop is ready. Below is a grid with the yield per plot. Estimate the average yield per plot based on each of the four sampling techniques.

Observations:

1) You have looked at four different methods of choosing plots. Is there a reason, other than convenience, to choose one method over another?

2) How did your estimates vary according to the different sampling methods you used?

3) Which sampling method should you use? Why do you think this method is best?

4) How could the farmer take a cluster sample in this activity?

AP Summer Institute 2014 Page 5 of 14 Sampling and Design

Article Assignments

Read-Think-Write-DiscussI will send you an article about/using real world statistics. I will include a description of what you are to do with the article. Your work will be due the 2nd Thursday of the month, every month. As you will have a month to complete this assignment, I will accept no late work without penalty (including partial day absences and school activities).

Sample Article and Assignment

When people are serious about good data, their thoroughness may be surprising to you.1. Write a half page typed summary of this article (or hand written-1 page).2. Please describe:a) The populationb) The population parameter of interestc) The sampling framed) The samplee) The sampling method

Suffering Adds Up in a Hurry In Survey of Tsunami SurvivorsBy David BrownWashington Post Staff WriterSaturday, January 15, 2005; Page A01

CALANG, Indonesia -- For two days this week, an Australian doctor and his Acehnese assistant knocked on every 10th door in Calang, a seaside town on Sumatra island that was decimated by the Dec. 26 tsunami. At each house, they asked the same brief questions, thanked the residents and departed after about 10 minutes.What they learned in their bare-bones, random statistical survey, conducted for the International Rescue Committee, was chilling.Before the tsunami, 8,700 people lived in Calang; now, that number is 2,500, and a third of those are displaced from other towns. Sixty-five percent of households have had a death in the immediate family. Twenty-two percent have taken in orphans, usually more than one. Only 8 percent of the population is younger than 5, and 85 percent of those children have had diarrhea in the past two weeks.The survey, conducted by Richard Brennan and his assistant, Kamaruddin, has provided the most precise look to date at the tsunami's effects on the people living in the worst-hit part of the worst-hit country.

The survey produced more than numbers, however. It will help the International Rescue Committee, a humanitarian relief agency based in New York, plan how best to spend the $7 million it has budgeted to assist Indonesian survivors of the tsunami.Relief organizations often use such systematic assessments during man-made disasters involving war, famine and forced dislocation, in which people's needs may not be obvious.In natural disasters, relief experts said, the aid requirements are usually more clear-cut because the disruption tends to be short-lived and the sufferers start off healthy. But in the case of the recent tsunami, the magnitude of damage in Indonesia indicates that its effects will be severe and long-lasting.

Brennan's survey of Calang's households began Monday on one side of the tiny peninsula on the west coast of Sumatra. Two Indonesian navy ships were moored offshore.The flat land beyond the beach where the tsunami came ashore was covered with fallen palm trees and trash. There were a dozen green military tents, and an orange one containing an Indonesian Red Cross clinic. A steady stream of refugees came and went, with people picking up boxes of flavored noodles and sorting through sodden piles of donated clothes.The day before, Brennan had canvassed the residential area, built on slopes that drop toward the sea, and decided he needed to visit one in 10 homes to compile enough data to make the results of the survey valid.To make it truly random, however, his starting point couldn't be the first house he encountered, but rather a point that could not be predicted. So his first scientific move in Calang was to ask someone for some paper money and then to read off the last digit in its serial number, which was a 2.The scientific approach to disaster relief can be controversial. Some critics think it is foolish to prove the existence of "obvious" needs and cruel to ask for information before giving help. Brennan, 45, who heads the International Rescue Committee's health activities program, is not one of them."Okay," he said, "we start counting after the second house."This meant that the third house near the beach became Household No. 1, the first to be surveyed. It was a one-room shack built from scavenged wood, corrugated metal sheets and dried palm branches.

Brennan approached a woman squatting inside who was surrounded by children. His first question was: "Where are you from?" The second was: "How long have you been here?" Then: "How many people slept here last night?"The interpreter, whose only name is Kamaruddin and who uses the nickname Odon, took down the answers. At the third question, the woman seemed confused, and then Odon became engaged in an extended conversation with a man outside the shelter."Odon, don't get distracted, please," said Brennan, who hoped to complete the survey that day so he could move onto outlying villages."She can't count," the interpreter replied.It turned out the answer was 13 -- one of the larger households the team would encounter. Brennan still needed the age and sex of each person. And he had a dozen more questions after that.When the encounter was finally over, the visitors descended into a wide gully that had been made by the retreating water. It was full of tangled fronds, branches and uprooted trees. On the other side rose the hill where the next group of houses stood. Odon stopped in the middle of the gully."Rick, I think we need to ask fewer questions," he said. "They don't like just to give information and get nothing. If you go to someone's house three times and give them nothing, they will get angry."Brennan said he was acutely conscious of the problem.He told Odon he had used a satellite telephone that morning to try to reach the International Rescue Committee's emergency coordinator in Banda Aceh and request that a water and sanitation engineer be sent to Calang. Even without an assessment, he said, it was clear that people needed clean water and latrines. Unfortunately, he couldn't reach her."So what have you done?" demanded Odon, 29. Brennan said he had divided a large box of medicine and bandages between the Indonesian Red Cross clinic and a German clinic and promised that his office would send the engineer as soon as it could."Is that convincing?" Brennan asked with a doubtful smile. "I need to convince you before you can convince them."But nobody in Calang refused to talk to the surveyors. Everyone was gracious. Two people asked for cigarettes, and two asked for rice. The team had only thanks to offer, though at some stops they let children peep at a digital photograph of themselves.Up and down the hillsides the team went, stopping at every 10th house. If no one was home, they moved to the next house. They defined each "household" as a group of people who ate from one pot; some large structures contained more than one.Each dwelling was a testament to human resourcefulness. The humblest was a lean-to where five people appeared to sleep on the ground, except for an infant in a hammock. The most elaborate was a wood-frame house with rafters made of galvanized pipe, with woven mats covering the floors. With rain almost constant, the settlement was a cat's cradle of lines hung with drying clothes.Brennan's survey included questions about where each household obtained water and how much it used each day. The team inspected and estimated the capacity of buckets, jerrycans and former chemical containers used for storing water.The visitors also made one measurement of each young child they encountered. The "mid-upper-arm circumference," known to epidemiologists as MUAC, changes

little in children between 6 months and 5 years of age and is used as way of screening for malnutrition.Odon, although not completely convinced of the survey's usefulness, assisted willingly. He kept the multicolored MUAC tape around his shoulder bag, and he and Brennan exchanged jokes about the adventures of "Mr. MUAC."Over the course of two days, a solid statistical portrait emerged of the tsunami's effects on Calang.There were 316 households, with an average size of 7.6 people. Most had lost at least one member, but the precise mortality rate remained unknown because some families had been wiped out and some survivors had left.Children and the elderly seemed to have died at a slightly higher rate than adults -- although the town was filled with orphans. Nationwide, children younger than 5 constituted 10 percent of Indonesia's population in 2003; in Calang, the number is 8.2 percent. People older than 60 made up 7.9 percent of the nation's population before the tsunami; in Calang, the figure is 4.5 percent. The oldest survivor in the area was a woman of 80; a woman in the same household who was over 100 had died.Yet considering the enormity of the disaster, the survivors of Calang seemed to be in generally good condition. The orphans were all being cared for, and there were no cases of acute malnutrition. Almost everyone defecated outdoors, nobody was drinking from a "protected" water source, and childhood diarrhea -- spread by fecal contamination of food and water -- was common. Yet no cases suggested cholera or dysentery. Sick people were seeing doctors, and most reported being happy with the treatment they received.Brennan, who planned to share his findings with the World Health Organization's office in Banda Aceh, said the survey convinced him that the International Rescue Committee had made the right decision to concentrate on improving water quality and building latrines in Calang and to deploy its medical resources elsewhere.In one house, the surveyors met a 32-year-old carpenter they had seen earlier at the Indonesian Red Cross clinic, where he had brought his infant daughter. The baby had a large, tender swelling below her jaw. Brennan and another doctor had concluded that it might be an infection of a salivary gland, and they prescribed an antibiotic.When Odon reached the survey question about whether people were satisfied with medical treatment they had received in the last week, Brennan grinned at the carpenter and warned, "Careful what you say, mate."The man replied, "It's hard to say. We'll have to see how the baby does when she takes more medicine."Brennan rejoined: "Good answer."

Egg SmashingMaterials:

• YouTube of Jimmy Fallon egg smashing

• 12 eggs - 8 hard boiled or empty and 4 raw or filled with confetti

The Plan:Students watch Jimmy Fallon video. Ask students about probability of getting a raw egg. Students should note that choosing eggs without replacement results in changing probabilities. Display eggs and find a challenger! Play game, stopping to ask probabilities at each draw. Or have a recorder keep tallies on the game and find probabilities after game is concluded. Adventurous teachers can consider getting messy!

Tips:

Go outside to a grassy area so your janitor doesn’t hate you.Bring a trash bag to cover student clothingWear an old t-shirt.Alert school journalism for yearbook pictures!

Supercilious Test

The day before you start the binomial distribution offer students extra credit if they can get over 50% right on a simple 20 question test. Have them take the silly test. Use a random number generator to make your key. Distribute the graded quizzes the next day. Make sure you find the mean and standard deviation of the scores. Let the discussion begin.

Why was the mean around 4?What are the chances of missing them all?Build the binomial model.

Hershey Kisses

Gimme a Kiss!Hershey’s Kisses and Confidence Intervals

In this activity, we will estimate a confidence interval for the proportion of times a Hershey’s kiss lands on its base as opposed to its side. To do this, we will drop Hershey’s kisses, count how many land on their base, and calculate the confidence interval.

To take your sample, gather five Hershey’s kisses in your cup, shake them up, and drop them from about six inches above your desk. Count the number that land on their base.

Repeat ten times to get a sample of size 50, recording your results in the table below.

Toss Number

Number that land on base

Toss Number

Number that land on base

1 6

2 7

3 8

4 9

5 10

Total

What is the population of interest? _______________________________

What is the sample? ______________________________________

Your result (50 tosses combined): =

The results of the others in your group: ________ ________ ________

Compare your with the others in your group. Did you all get the same answer? ________

Follow these steps to make a 95% confidence interval based on your result.• Calculate the standard deviation of your sample proportion (standard error):

• Look up the critical value, z*, that corresponds to 95% confidence. Note, this is the z-score that corresponds to the middle 95% of the normal distribution. (Hint: what percentage would be left in each tail of the distribution? Use this & invNorm OR use this & the ∞ row of the t-table to find the z-score). The positive z-score is the critical value.

z*95% = ___________

• The confidence interval is where the critical value multiplied by the standard error is the margin of error for the interval. Adding and subtracting the margin of error from the sample statistic creates the lower and upper bounds for the confidence interval.

your lower bound = ___________ your upper bound = ____________

)(

Use the lines below to roughly draw the confidence intervals of each person in your group. Indicate the location of as a dot. (An example of a CI from 0.13 to 0.37 with = .25 is given.)

After comparing with your group, plot your dot sticker on your -value on the chart at the front of the room. Using a marker extend a line from your dot to your lower bound and upper bound (in similar fashion as the diagram above)

Interpret the 95% confidence interval in your own words. Write your interpretation below.

Compare your interpretation with your group members’ interpretations and come to an agreement on an appropriate interpretation. Write it below.

If you’ve completed these tasks and questions, eat a Hershey’s Kiss while you ponder the following: Which of the following do you think is true (more than one may be true)?

A. There is a 95% probability that the true proportion will fall in your interval.

B. There is a 95% probability that your interval will include the true proportion.

C. BEFORE we take the sample, there is a 95% probability that the confidence interval we will create WILL include the true proportion.

D. AFTER we take the sample, there is a 95% probability that the confidence interval we created DOES include the true proportion.

Modified by Doug Tyson, Lisa Brock, & Carol Sikes from Aaron Rendahl’s STAT 4102 activities from University of Minnesota

Inference Template

Name of Test/Interval:

Null and Alternative Hypotheses:(in words and symbols)

Check of Conditions:

Math Box: (standard deviation, test statistic, p-value, margin of error, sketch, etc…)

Decision and conclusion:

Problem Number: ______

Bead Activity

Preparation: Take paper bags and fill with some red beads and some blue. Label the bags A, B, C, etc… Make bag A 50% blue, bag B 53% blue, and the rest further and further away from 50%.

Activity: • Distribute the bags, one per group. Have students draw a small sample from their

“population.”• Instruct students to find the percentage of blue beads in their sample.• Students then need to:

o Find a 95% confidence interval for their proportion.o Run a 2-sided test comparing their proportion to 50%

p-hat (blue) p-value Decision 95% C. I. Correct?

A

B

etc…

Discussion points:• Who had enough power to make the right decision?• Why are the confidence intervals different sizes?• Group B will almost certainly make a Type II error; how could they fix this problem?

Discuss changing alpha and sample size.• Connect the decision with the confidence interval.

• You may have intervals that contain 50%, but rejected, or vice versa, because of the difference in standard deviation/standard error formulas. This is usually a good time for the class to realize why this can happen.

• Give the extreme groups memorable names: either let them pick a name or use a student’s name. Then when you talk about these issues later, you can refer to “Victoria’s group” that didn’t have enough power, or “Blake’s group” that was so far away from 50%, they would never make an error and had tons of power, etc…

• Change the sample size: Group B could draw again with twice the sample and see if they reject.

• Group A could draw a smaller sample size and see if they reject and make a Type I error.

Power Dominoes

We are testing a new headache medication to see if it is more effective than the current treatment (known to relieve pain within 15 minutes for 60% of headache suffers). We give the new medication to a bunch of volunteers and see what fraction of them report relief within 15 minutes.

The enclosed dominos need to be placed in order from START to FINISH. Each statement about the new headache medicine will be followed by a translation of that statement, until you reach the FINISH domino.

START

If the new medication is actually 90%

effective we’ll almost surely notice. If the

new med is only 65% effective we may miss

that.

TRANSLATION: The greater the effect

size, the higher the power

If we are willing to accept less evidence, we are more likely to

notice any effect

TRANSLATION: The higher the alpha level, the greater the

power.

If we are easier to convince, we are more

likely to mistakenly think the new

medication works when it really doesn’t.

TRANSLATION: The higher the alpha level, the greater the risk of a Type I error.

If we are easy to convince, we are less

likely to miss an effective medication.

TRANSLATION: The higher the alpha level, the lower the

risk of a Type II error.

If we demand stronger evidence, we are more likely to miss the fact that the medication is

more effective.

TRANSLATION: The lower the alpha level, the greater the risk of a Type II error.

If we require a higher standard of proof, we

are less likely to be fooled into thinking an ineffective medication

works.

TRANSLATION: The lower the alpha level, the lower the

risk of a Type I error.

If we are tougher to convince, the less

likely we are to notice that the medication

really is effective.

TRANSLATION: The lower the alpha level, the less power

we have.

If we increase the number of subjects in our experiment, we are more likely to

discover an effective medication.

TRANSLATION:Increasing your

sample size increases your power.

FINISH

Mythbusters Yawning

Mythbusters ran an experiment to see if yawning is contagious. They had a room with a “yawn seed” where a planted person would yawn and then the others were observed to see if they yawned. In other rooms, there was no “yawn seed.”

Here’s the data:

Treatment(Yawn seedPresent)

Control Total

Yes 10 4 14

No 24 12 36

Total 34 16 50

On the show, they concluded that the yawn seed was successful. But was the difference really that large?

To simulate:• Prepare decks of cards with 14 face cards and 36 non-face cards. • The face cards represent the people who yawned. • Students shuffle the cards.• Count out 16 cards (control group).• Count the number of face cards in the control.• Make a dotplot.• Online applet!

http://www.rossmanchance.com/applets/Yawning/Yawning.html

http://www.rossmanchance.com/applets/Yawning/Yawning.html

FruitLoops

Materials• 1 large box of Fruit Loops per class

• cups or bowls to distribute cereal

The PlanStudents are given a small bowlful of cereal. They tally the color distribution of their data.

Color distribution is entered into a list.

Students are asked if the color distribution is uniform. They enter the expected distribution of uniform color distribution in a second list.

Problems with running multiple proportion tests are discussed. (insert Green Jelly Beans xkcd)

Chi-square goodness of fit test explained, run and written on inference template.

Projects

Project 1 - Exploratory Data Analysis

(50 points)

1st paragraph: How and where you collected your data (collect at least 25 data points, data must be quantitative)

2nd paragraph: Do you think your data represents the population you were studying? Why or why not? What sources of bias do you think may have been present in your data collection?

3rd paragraph: By studying graphs of the data, what relationships can be observed? What do the graphs show? What conclusions can be drawn?

Page 1 must be typed.

Page 2 + (50 points)

Graph your data.

• By hand, neatly, is fine• Make TWO different types of graphs for the quantitative data—

histogram, boxplot, stemplot and/or dotplot

Excel?—Makes great pie graphs and lousy histograms—proceed at thy own peril…

Main Ideas: to practice describing data and graphs and to BEGIN to think about data collection and sources of bias.

Project 2 - Bias in Surveys

The Purpose: To investigate how much different forms of bias can affect the results of a survey.

The Project:*The project must be done in groups of one to four. You will turn in one poster per group.*A brief oral presentation and poster will be required for each group. NO REPORT NEEDED!

Topic: You will design a survey on an interesting topic of your choice, but you must design it so you can address ONE of the following questions:

• Is it possible to word a question in two different ways that are logically equivalent, but have much different responses?

• Do the characteristics of the interviewer affect responses?• Does anonymity change the responses to sensitive questions?• Does providing extra information affect the responses?NOTE: You may choose another form of bias if you get special approval from the teacher.

You should compare at least 2 different groups. Usually this is a “control” question that is “normal” and unbiased and then a second group that is biased in a certain direction. Depending on your idea, you may choose to have 2 different biases—one that will tilt in one direction and one that will tilt in another direction. You will not be penalized if you do not succeed in creating a large bias.

Proposal:1. -A definition of the population.2. -A copy of your survey questions.3. -A short description of how you will create bias and what direction you

think the bias will swing.4. -Where and how you will collect your data.

NOTE: Your sampling procedure should not be biased.

Poster: The poster should be completely summarize your project, yet be simple enough to be understood by a freshman. Remember the purpose of the project! It should be pleasing to the eye. It should include a one-page typed paper describing what you did. The colors on your graphs are crucial to communicating your bias: use a consistent color-key so the change can be easily spotted.Points: 60 points: 20 for appearance, 20 for clear communication on graphs, 20 for report

Oral Presentation: All group members need to participate equally. Your poster should be used as a visual aid. 5 minutes. To receive full credit for your presentation your group must speak clearly, with confidence and must do something to ENGAGE THE AUDIENCE. I leave it open-ended as to exactly what you tell us about what you did, but I absolutely insist that your presentation be clear, interesting and well-spoken (if you want full credit!). We do not have time for elaborate props/media set-up. Your task is to be engaging with your mouths, not by bringing in multi-media.

Points: 40 points: 20 for being engaging, 20 points for clear communication

Project 3 - Final Project

Purpose: The purpose of this project is for you to actually do statistics. You are to formulate a statistical question, design a study to answer the question, conduct the study, collect the data, analyze the data, and use statistical inference to answer the question. You are going to do it all!!

Topics: You may do your study on any topic, but you must be able to include 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 (25 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 statistical question, 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. The proposal should be typed. If your proposal isn’t approved, you must resubmit the proposal for partial credit until it is approved.

Poster/Powerpoint (55 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.

• Analysis. In this section, identify the inference procedure you used along with the test statistic and P-value and/or confidence interval. Also, discuss how you know that your inference procedure is valid.

• Conclusion. In this section, you will state your conclusion. You should also discuss any possible errors or limitations to your conclusion, 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 (20 points): You will be required to give a 5 minute oral presentation to the class.

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 (for tests)

• 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 parameter/ 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

• 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

• 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/P-value or confidence interval is calculated correctly

• P-value or confidence interval is interpreted correctly


• Lacks justification, lacks interpretation, or makes a calculation error


• Test statistic/P-value or confidence interval is calculated correctly

• Inference procedure is attempted

Conclusions

• Uses P-value/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, 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

Overall Presentation/

Communication

• Clear, holistic understanding of the project

• Poster is well organized, neat and easy to read

• Statistical vocabulary is used correctly

• Poster/ powerpoint is visually appealing

• Clear, holistic understanding of the project

• Statistical vocabulary is used correctly

• Poster/powerpoint is unorganized or isn’t visually appealing,

• Poster/powerpoint is not well done or communication is poor

• Communication and organization are very poor

s3. web viewjonna spillerbonham isd. bonham high schoolbonham, texas. ap statistics syllabus....

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