Statistics and Probability

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1 Statistics and a single count or measurement variable. S.ID.1: Represent data with plots on the real number line (dot plots, histograms, and box plots). S.ID.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. S.ID.3: Interpret differences in shape, center and spread in the context of the data sets, accounting for possible effects of extreme data points (outliers). S.ID.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. S.ID.1 a single count or measurement variable. Demonstration: Represent data with plots on the real number line, using various display types by creating dot plots, histograms, and box plots S.ID.2 a single count or measurement variable. Reasoning: Choose the appropriate measure for center (mean, median) and spread (interquartile range, standard deviation) based on the shape of a data distribution Demonstration: Use appropriate statistics for center and spread to compare two or more data sets S.ID.3 a single count or measurement variable. Knowledge: Define the context of data sets as meaning the specific nature of the attributes under investigation Reasoning: Interpret differences in shape, center, and spread in the context of the data sets Reasoning: Describe the possible effects the presence of outliers in a set of data can have on shape, center, and spread in the context of the data sets 93

2 S.ID.4 a single count or measurement variable. Knowledge: Describe the characteristics of a normal distribution Reasoning: Recognize that there are data sets for which such a procedure is not appropriate Demonstration: Use the mean and standard deviation of a data set to fit it to a normal distribution Demonstration: Use a normal distribution to estimate population percentages Demonstration: Use a calculator, spreadsheet, and table to estimate areas under the normal curve two categorical and quantitative variables. S.ID.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. S.ID.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 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. S.ID.5 two categorical and quantitative variables. MP: Look for and make use of structure Knowledge: Recognize the differences between joint, marginal, and conditional relative frequencies Knowledge: Summarize categorical data for two categories in twoway frequency tables Reasoning: Interpret relative frequencies in the context of the data Reasoning: Recognize possible associations and trends in the data Demonstration: Calculate relative frequencies including joint, marginal, and conditional 94

3 S.ID.6 two categorical and quantitative variables. MP: Look for and express regularity in repeated reasoning Knowledge: Represent data on a scatter plot (2 quantitative variables) Knowledge: Fit a given function class (e.g., linear, exponential) to a data set Knowledge: Represent the residuals from a function and the data set it models, numerically and graphically Reasoning: Using given scatter plot data represented on the coordinate plane, informally describe how the two quantitative variables are related Reasoning: Determine which function best models scatter plot data represented on the coordinate plane, and describe how the two quantitative variables are related Reasoning: Use functions fitted to data to solve problems in the context of the data Reasoning: Informally assess the fit of a function by analyzing residuals from the residual plot Reasoning: Fit a linear function for a scatter plot that suggests a linear association Cluster: Interpret linear models. S.ID.7: Interpret the slope (rate of change) and the intercept (constant term) of a linear model in the context of the data. S.ID.8: Compute (using technology) and interpret the correlation coefficient of a linear fit. S.ID.9: Distinguish between correlation and causation. S.ID.7 Cluster: Interpret linear models. MP: Look for and express regularity in repeated reasoning Reasoning: Interpret the slope (rate of change) and the intercept (constant term) of a linear model in the context of the data 95

4 S.ID.8 Cluster: Interpret linear models. MP: Look for and express regularity in repeated reasoning Knowledge: Define the correlation coefficient Reasoning: Interpret the correlation coefficient of a linear fit as a measure of how well the data fit the relationship Demonstration: Using technology, compute the correlation coefficient of a linear fit S.ID.9 Cluster: Interpret linear models. Knowledge: Define positive, negative, and no correlation and explain why correlation does not imply causation Knowledge: Define causation Reasoning: Distinguish between correlation and causation Cluster: Understand and evaluate random processes underlying statistical experiments. S.IC.1: Understand statistics as a process for making inferences about population parameters based on a random sample from that population. S.IC2: Decide if a specified model is consistent with results from a given data-generating process, e.g., using simulation. For example, a model says a spinning coin falls heads up with probability 0.5. Would a result of 5 tails in a row cause you to question the model? S.IC.1 S.IC.2 Cluster: Understand and evaluate random processes underlying statistical experiments. Knowledge: Explain that statistics is a process for making inferences about population parameters, or characteristics Knowledge: Explain that statistical inferences about population characteristics are based on random samples from that population Cluster: Understand and evaluate random processes underlying statistical experiments. Knowledge: Use various, specified data-generating processes/ models Knowledge: Recognize data that various models produce Knowledge: Identify data or discrepancies that provide the basis for rejecting a statistical model Reasoning: Decide if a specified model is consistent with results from a given data-generating process, e.g., using simulation. 96

5 Cluster: Make inferences and justify conclusions from sample surveys, experiments, and observational studies. S.IC3: Recognize the purposes of and differences among sample surveys, experiments, and observational studies; explain how randomization relates to each. S.IC4: 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. S.IC5: Use data from a randomized experiment to compare two treatments; use simulations to decide if differences between two parameters are significant. S.IC6: Evaluate reports based on data. S.IC.3 Cluster: Make inferences and justify conclusions from sample surveys, experiments, and observational studies. Knowledge: Recognize the purpose of surveys, experiments, and observational studies in making statistical inferences and justifying conclusions and explain how randomization relates to each of these methods of data collection Knowledge: Recognize the differences among surveys, experiments, and observational studies in making statistical inferences and justifying conclusions and explain how randomization relates to each of these methods of data collection S.IC.4 Cluster: Make inferences and justify conclusions from sample surveys, experiments, and observational studies. MP: Look for and make use of structure Knowledge: Define margin of error Knowledge: Explain the connection of margin of error to variation within a data set or population Reasoning: Interpret the data generated by a simulation model for random sampling in terms of the context the simulation models Reasoning: Develop a margin of error, assuming certain population parameters/characteristics, through the use of simulation models for random sampling Demonstration: Use a simulation model to generate data for random sampling, assuming certain population parameters/characteristics Demonstration: Use data from a sample survey to estimate a population mean or proportion 97

6 S.IC.5 Cluster: Make inferences and justify conclusions from sample surveys, experiments, and observational studies. Knowledge: Using an established level of significance, determine if the difference between two parameters is significant Reasoning: Choose appropriate methods to simulate a randomized experiment Reasoning: Establish a reasonable level of significance Demonstration: Use data from a randomized experiment to compare two treatments S.IC.6 Cluster: Make inferences and justify conclusions from sample surveys, experiments, and observational studies. MP: Look for and express regularity in repeated reasoning Knowledge: Define the characteristics of experimental design (control, randomization, and replication) Reasoning: Evaluate experimental study design, how data was gathered, and what analysis (numerical or graphical) was used Reasoning: Draw conclusions based on graphical and numerical summaries Reasoning: Support with graphical and numerical summaries how appropriate the report of data was 98

7 Cluster: Understand independence and conditional probability and use them to S.CP.1: Describe events as subsets of a sample space (the set of outcomes) using characteristics (or categories) of the outcomes, or as unions, intersections, or complements of other events ( or, and, not ). S.CP.2: Understand that two events A and B are independent if the probability of A and B occurring together is the product of their probabilities, and use this characterization to determine if they are independent. S.CP.3: Understand the conditional probability of A given B as P(A and B)/P(B), and interpret independence of A and B as saying that the conditional probability of A given B is the same as the probability of A, and the conditional probability of B given A is the same as the probability of B. S.CP.4: Construct and interpret two-way frequency tables of data when two categories are associated with each object being classified. Use the two-way table as a sample space to decide if events are independent and to approximate conditional probabilities. For example, collect data from a random sample of students in your school on their favorite subject among math, science, and English. Estimate the probability that a randomly selected student from your school will favor science given that the student is in tenth grade. Do the same for other subjects and compare the results. S.CP.5: Recognize and explain the concepts of conditional probability and independence in everyday language and everyday situations. For example, compare the chance of having lung cancer if you are a smoker with the chance of being a smoker if you have lung cancer. S.CP.1 Cluster: Understand independence and conditional probability and use them to Knowledge: Define unions, intersections and complements of events Reasoning: Describe events as subsets of a sample space (the set of outcomes) using characteristics (or categories) of the outcomes, or as unions, intersections, or complements of other events S.CP.2 Cluster: Understand independence and conditional probability and use them to Knowledge: Categorize events as independent or not using the characterization that two events A and B are independent when the probability of A and B occurring together is the product of their probabilities S.CP.3 Cluster: Understand independence and conditional probability and use them to Knowledge: Know the conditional probability of A given B as P(A and B)/P(B) Knowledge: Interpret independence of A and B as saying that the conditional probability of A given B is the same as the probability of A, and the conditional probability of B given A is the same as the probability of B 99

8 S.CP.4 Cluster: Understand independence and conditional probability and use them to Knowledge: Use the two-way table as a sample space to decide if events are independent and to approximate conditional probabilities Knowledge: Build on work with two way tables from Algebra 1 Unit 3 S-ID.5 to develop understanding of conditional probability and independence Reasoning: Interpret two-way frequency tables of data when two categories are associated with each object being classified S.CP.5 Cluster: Understand independence and conditional probability and use them to Knowledge: Recognize the concepts of conditional probability and independence in everyday language and everyday situations. Reasoning: Explain the concepts of conditional probability and independence in everyday language and everyday situations. Cluster: Use the rules of probability to compute probabilities of compound events in a uniform probability model. S.CP.6: Find the conditional probability of A given B as the fraction of Bs outcomes that also belong to A, and interpret the answer in terms of the model. S.CP.7: Apply the Addition Rule, P(A or B) = P(A) + P(B) P(Aand B), and interpret the answer in terms of the model. S.CP.8: (+) Apply the general Multiplication Rule in a uniform probability model, P(A and B) = P(A)P(B A) = P(B)P(A B), and interpret the answer in terms of the model. S.CP.9: (+) Use permutations and combinations to compute probabilities of compound events and solve problems. S.CP.6 Cluster: Use the rules of probability to compute probabilities of compound events in a uniform probability model. Knowledge: Find the conditional probability of A given B as the fraction of B s outcomes that also belong to A Reasoning: Interpret the answer in terms of the model S.CP.7 Cluster: Use the rules of probability to compute probabilities of compound events in a uniform probability model. Knowledge: Use the Additional Rule, P(A or B) = P(A) + P(B) - P(A and B) Reasoning: Interpret the answer in terms of the model 100

9 S.CP.8 Cluster: Use the rules of probability to compute probabilities of compound events in a uniform probability model. Knowledge: Use the multiplication rule with correct notation Reasoning: Apply the general Multiplication Rule in a uniform probability model P(A and B) = P(A)P(B A) = P(B)P(A B) Reasoning: Interpret the answer in terms of the model S.CP.9 Cluster: Use the rules of probability to compute probabilities of compound events in a uniform probability model. Knowledge: Identify situations that are permutations and those that are combinations Demonstration: Use permutations and combinations to compute probabilities of compound events and solve problems Cluster: Calculate expected values and use them to solve problems. S.MD.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. S.MD.2: (+) Calculate the expected value of a random variable; interpret it as the mean of the probability distribution. S.MD.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. For example, find the theoretical probability distribution for the number of correct answers obtained by guessing on all five questions of a multiple-choice test where each question has four choices, and find the expected grade under various grading schemes. S.MD.4: (+) Develop a probability distribution for a random variable defined for a sample space in which probabilities are assigned empirically; find the expected value. For example, find a current data distribution on the number of TV sets per household in the United States, and calculate the expected number of sets per household. How many TV sets would you expect to find in 100 randomly selected households? S.MD.1 Cluster: Calculate expected values and use them to solve problems. Knowledge: Define a random variable for a quantity of interest by assigning a numerical value to each event in a sample space Demonstration: Graph the corresponding probability distribution using the same graphical displays used for data distributions 101

10 S.MD.2 Cluster: Calculate expected values and use them to solve problems. Reasoning: Interpret expected value as the mean of the probability distribution Demonstration: Calculate the expected value of a random variable S.MD.3 Cluster: Calculate expected values and use them to solve problems. Demonstration: Develop a probability distribution for a random variable defined for a sample space in which theoretical probabilities can be calculated Demonstration: Find the expected value of a random variable for a probability distribution S.MD.4 Cluster: Calculate expected values and use them to solve problems. Reasoning: Develop a probability distribution for a random variable defined for a sample space in which probabilities are assigned empirically Demonstration: Find the expected value of a random variable for a probability distribution Cluster: Use probability to evaluate outcomes of decisions. S.MD.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 high-deductible versus a low-deductible automobile insurance policy using various, but reasonable, chances of having a minor or a major accident. S.MD.6: (+) Use probabilities to make fair decisions (e.g., drawing by lots, using a random number generator). S.MD.7: (+) Analyze decisions and strategies using probability concepts (e.g., product testing, medical testing, pulling a hockey goalie at the end of a game). 102

11 S.MD.5 Cluster: Use probability to evaluate outcomes of decisions. MP: Look for and make use of structure Reasoning: Find the expected payoff for a game of chance Reasoning: Evaluate and compare strategies on the basis of expected values S.MD.6 Cluster: Use probability to evaluate outcomes of decisions. MP: Look for and express regularity in repeated reasoning Knowledge: Compute Theoretical and Experimental Probabilities Knowledge: Recall previous understandings of probability Reasoning: Use probabilities to make fair decisions Reasoning: Extend to more complex probability models. Include situations such as those involving quality control, or diagnostic tests that yield both false positive and false negative results S.MD.7 Cluster: Use probability to evaluate outcomes of decisions. Knowledge: Recall previous understandings of probability Reasoning: Analyze decisions and strategies using probability concepts Reasoning: Extend to more complex probability models. Include situations such as those involving quality control, or diagnostic tests that yield both false positive and false negative results 103

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