Probability and Statistics CURRICULUM

VALLEY CENTRAL SCHOOL DISTRICT 944 STATE ROUTE 17K MONTGOMERY, NY 12549 Telephone Number: (845) 457-2400 ext. 8121 Fax Number: (845) 457-4254 Probability and Statistics CURRICULUM June 2011 Approved by the Board of Education August 15, 2011

Table of Contents Introduction Unit I. Data Unit II. Modeling Distributions of Data Unit III. Describing Relationships Unit IV. Designing Studies Unit V. Probability Unit VI. Random Variables Unit VII. Sampling Distributions Unit VIII. Confidence Intervals Unit IX. Significance Tests Unit X. Comparing Two Populations Unit XI. Inference for Categorical Data Unit XII. Regression in Depth

Introduction Valley Central s Probability and Statistics Curriculum is based on New York State Common Core Standards for Mathematics. In addition several units of study are above and beyond the scope of the Common Core. They are based on standards as outlined in the Advanced Placement Statistics Curriculum. The course is designed for the student who has successfully completely Regents Geometry or Algebra II and Trigonometry. The textbook for this course is The Practice of Statistics, by Starnes, Yates and Moore, 4 th edition. The use of technology will be incorporated through use of TI-83 Plus graphing calculators, the internet, Smartboards and Microsoft Excel software. Students will work in the computer lab on a regular basis. The following Mathematical Practices as defined in the Common Core Curriculum will be incorporated in daily instruction and reinforced with outside assignments and assessments: 1. Make sense of problems and persevere in solving them. 2. Reason abstractly and quantitatively. 3. Construct viable arguments and critique the reasoning of others. 4. Model with mathematics. 5. Use appropriate tools strategically. 6. Attend to precision. 7. Look for and make use of structure. 8. Look for and express regularity in repeated reasoning.

Probability and Statistics Curriculum Valley Central High School June 2011 Semester I Common Core Reference Days Resources Unit I Data 18 Days Categorical Data Individuals and Variables Data Analysis and Inference Bar Graphs and Pie Charts Two Way Tables and Distributions Categorical Variables &, Conditional Distributions Simpson s Paradox Quantitative Data Dotplots Skewness Stemplots Histograms Measures of Central Tendency Measures of Dispersion Outliers Boxplots Choosing Appropriate Measures of Central Tendency and Spread Summarize, represent, and interpret data on a single count or measurement variable 1. Represent data with plots on the real number line (dot plots, histograms, and box plots). 2. Use statistics appropriate to the shape of the data distribution to compare center (median, mean) and spread (interquartile range, standard deviation) of two or more different data sets. 3. Interpret differences in shape, center, and spread in the context of the data sets, accounting for possible effects of extreme data points (outliers). p. 2-25 p. 27-68

Unit II Modeling Distributions of Data 13 Days Location and Position Percentiles Cumulative Frequency Graphs z-scores Transforming Data Density Curves Normal Distributions Summarize, represent, and interpret data on a single count or measurement variable 2. Use statistics appropriate to the shape of the data distribution to compare center (median, mean) and spread (interquartile range, standard deviation) of two or more different data sets. 3. Interpret differences in shape, center, and spread in the context of the data sets, accounting for possible effects of extreme data points (outliers). 4. Use the mean and standard deviation of a data set to fit it to a normal distribution and to estimate population percentages. Recognize that there are data sets for which such a procedure is not appropriate. Use calculators, spreadsheets, and tables to estimate areas under the normal curve.. p. 85-103 p. 115-129

Unit III Describing Relationships Scatterplots and Correlation Explanatory and Response Variables Scatterplots Linear Correlation Least-Squares Regression Interpreting a Regression Line Prediction (Extrapolation and Interpolation) Calculating the Equation of the Least- Squares Line The Role of r 2 in Regression Interpret linear models 7. Interpret the slope (rate of change) and the intercept (constant term) of a linear model in the context of the data. 8. Compute (using technology) and interpret the correlation coefficient of a linear fit. 9. Distinguish between correlation and causation Summarize, represent, and interpret data on two categorical and quantitative variables 5. Summarize categorical data for two categories in two-way frequency tables. Interpret relative frequencies in the context of the data (including joint, marginal, and conditional relative frequencies). Recognize possible associations and trends in the data. 15 Days p. 143-156 p. 168-189 6. Represent data on two quantitative variables on a scatter plot, and describe how the variables are related. a. Fit a function to the data; use functions fitted to data to solve problems in the context of the data. Use given functions or choose a function suggested by the context. Emphasize linear, quadratic, and exponential models. b. Informally assess the fit of a function by plotting and analyzing residuals. c. Fit a linear function for a scatter plot that suggests a linear association.

Unit IV Designing Studies 22 Days Sampling and Surveys Sample Survey Random Sampling Inference for Sampling Experiments Observational Studies versus Experiments Designing Studies Randomized Comparative Experiment Principles of Experimental Design Blocking Make inferences and justify conclusions from sample surveys, experiments, and observational studies. 3. Recognize the purposes of and differences among sample surveys, experiments, and observational studies; explainhow randomization relates to each. 4. Use data from a sample survey to estimate a population mean or proportion; develop a margin of error through the use of simulation models for random sampling. 5. Use data from a randomized experiment to compare two treatments; use simulations to decide if differences between parameters are significant. 6. Evaluate reports based on data. p.206-224 p. 231-251 Unit V Probability 17 Days Randomness and Simulation Probability Models Two-way Tables Venn Diagrams and Probability Conditional Probability and Independence Tree Diagrams and the Counting Principle Calculating Conditional Probabilities Understand and evaluate random processes underlying statistical experiments 1. Understand statistics as a process for making inferences about population parameters based on a random sample from that population. 2. Decide if a specified model is consistent with results from a given data-generating process, e.g., using simulation. p.282-293 p. 299-308 p. 313-328

Make inferences and justify conclusions from sample surveys, experiments, and observational studies. 3. Recognize the purposes of and differences among sample surveys, experiments, and observational studies; explain how randomization relates to each. 4. Use data from a sample survey to estimate a population mean or proportion; develop a margin of error through the use of simulation models for random sampling. 5. Use data from a randomized experiment to compare two treatments; use simulations to decide if differences between parameters are significant. 6. Evaluate reports based on data. Calculate expected values and use them to solve problems 1. (+) Define a random variable for a quantity of interest by assigning a numerical value to each event in a sample space; graph the corresponding probability distribution using the same graphical displays as for data distributions. 2. (+) Calculate the expected value of a random variable; interpret it as the mean of the probability distribution. 3. (+) Develop a probability distribution for a random variable defined for a sample space in which theoretical probabilities can be calculated; find the expected value. 4. (+) Develop a probability distribution for a random variable defined for a sample space in which probabilities are assigned empirically; find the expected value.

Use probability to evaluate outcomes of decisions 5. (+) Weigh the possible outcomes of a decision by assigning probabilities to payoff values and finding expected values. a. Find the expected payoff for a game of chance. For example, find the expected winnings from a state lottery ticket or a game at a fast-food restaurant. b. Evaluate and compare strategies on the basis of expected values. For example, compare a highdeductible versus a low-deductible automobile insurance policy using various, but reasonable, chances of having a minor or a major accident. 6. (+) Use probabilities to make fair decisions (e.g., drawing by lots, using a random number generator). 7. (+) Analyze decisions and strategies using probability concepts (e.g., product testing, medical testing, pulling a hockey goalie at the end of a game). Understand and evaluate random processes underlying statistical experiments 1. Understand statistics as a process for making inferences about population parameters based on a random sample from that population. 2. Decide if a specified model is consistent with results from a given data-generating process, e.g., using simulation. Midterm Exam Review and Exam Total Semester I 5 Days 90 Days

Semester II Common Core Reference Days Unit VI Random 17 Days Variables Discrete Random Variables Expected Value Standard Deviation and Variance Continuous Random Variables Transforming and Combining Random Variables Binomial Random Variables Binomial Settings Binomial Probabilities Mean and Standard Deviation of a Binomial Distribution Geometric Random Variables Understand and evaluate random processes underlying statistical experiments 1. Understand statistics as a process for making inferences about population parameters based on a random sample from that population. 2. Decide if a specified model is consistent with results from a given data-generating process, e.g., using simulation. p. 343-352 p. 358-376 p. 382-401 Unit VII Sampling Distributions 10 Days Parameters Variability Describing Sampling Distributions Sample Proportions The Sampling Distribution of p Understand and evaluate random processes underlying statistical experiments 1. Understand statistics as a process for making inferences about population parameters based on a random sample from that population. 2. Decide if a specified model is consistent with results from a given data-generating process, e.g., using simulation. p. 416-427 p. 432-437 p. 444-453

Unit VIII Confidence Intervals 11 Days Estimating a Population Proportion Conditions for Estimating p Constructing a Confidence Interval for p Choosing the sample size Estimating a Population Mean The One-Sample z Interval for a Population Mean Choosing the Sample Size The t Distributions Constructing a Confidence Interval for μ Understand and evaluate random processes underlying statistical experiments 1. Understand statistics as a process for making inferences about population parameters based on a random sample from that population. 2. Decide if a specified model is consistent with results from a given data-generating process, e.g., using simulation. p. 469-480 p. 485-494 p. 499-516

Unit IX Significance Tests Significance Tests Hypotheses P-values Statistical Significance Type I and Type II Errors One Sample Z Test Significance test for mu One sample t test Two sided Tests and Confidence Intervals Beyond the scope of the Common Core Standards 15 Days p. 528-545 p. 549-561 p. 565-586 Unit X Comparing Two Populations Comparing Two Proportions Sampling Distribution Confidence Intervals Significance Tests Comparing Two Means Sampling Distribution Confidence Intervals Significance Tests Understand and evaluate random processes underlying statistical experiments 1. Understand statistics as a process for making inferences about population parameters based on a random sample from that population. 2. Decide if a specified model is consistent with results from a given data-generating process, e.g., using simulation. 10 Days p.602-619 p. 627-649

Unit XI Inference for Categorical Data Chi-Square Goodness of Fit Tests Comparing Observed and Expected Counts The Chi-Square Statistic The Chi-Square Distributions and p-values Tests and Analysis 6 Days Beyond the scope of the Common Core Standards p. 679-690 p. 696-721 Inference for Relationships Comparing Distributions Expected Counts Test for Homgeneity Relationships between two variables Chi-Square test for Association and Independence

Unit XII Regression In Depth Inference for Linear Regression Sampling Distribution of b Conditions for Regression Inference Estimating the Parameters Confidence Interval for Slope Significance Test for Slope Linear Tranformation of Data Interpret linear models 7. Interpret the slope (rate of change) and the intercept (constant term) of a linear model in the context of the data. 8. Compute (using technology) and interpret the correlation coefficient of a linear fit. 9. Distinguish between correlation and causation 11 Days p.738-757 p. 765-785 Final Exam Review and Exam Total Semester II Total Semesters I and II 10 Days 90 Days 180 Days