3. Data Analysis, Statistics, and Probability

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1 3. Data Analysis, Statistics, and Probability Data and probability sense provides students with tools to understand information and uncertainty. Students ask questions and gather and use data to answer them. Students use a variety of data analysis and statistics strategies to analyze, develop and evaluate inferences based on data. Probability provides the foundation for collecting, describing, and interpreting data. Prepared Graduates The prepared graduate competencies are the preschool through twelfth-grade concepts and skills that all students who complete the Colorado education system must master to ensure their success in a postsecondary and workforce setting. Prepared Graduate Competencies in the 3. Data Analysis, Statistics, and Probability Standard are: Recognize and make sense of the many ways that variability, chance, and randomness appear in a variety of contexts Solve problems and make decisions that depend on understanding, explaining, and quantifying the variability in data Communicate effective logical arguments using mathematical justification and proof. Mathematical argumentation involves making and testing conjectures, drawing valid conclusions, and justifying thinking Use critical thinking to recognize problematic aspects of situations, create mathematical models, and present and defend solutions Colorado Department of Education: High School Mathematics Revised: December 2010 Page 1 of 5

2 Solve problems and make decisions that depend on understanding, explaining, and quantifying the variability in data 1. Visual displays and summary statistics condense the information in data sets into usable knowledge a. Summarize, represent, and interpret data on a single count or measurement variable. (CCSS: S-ID) i. Represent data with plots on the real number line (dot plots, histograms, and box plots). (CCSS: S-ID.1) ii. 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. (CCSS: S-ID.2) iii. Interpret differences in shape, center, and spread in the context of the data sets, accounting for possible effects of extreme data points (outliers). (CCSS: S-ID.3) iv. Use the mean and standard deviation of a data set to fit it to a normal distribution and to estimate population percentages and identify data sets for which such a procedure is not appropriate. (CCSS: S-ID.4) v. Use calculators, spreadsheets, and tables to estimate areas under the normal curve. (CCSS: S-ID.4) b. Summarize, represent, and interpret data on two categorical and quantitative variables. (CCSS: S-ID) i. Summarize categorical data for two categories in two-way frequency tables. Interpret relative frequencies in the context of the data 1 (including joint, marginal, and conditional relative frequencies). Recognize possible associations and trends in the data. (CCSS: S- ID.5) ii. Represent data on two quantitative variables on a scatter plot, and describe how the variables are related. (CCSS: S-ID.6) 1. 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. (CCSS: S-ID.6a) 2. Informally assess the fit of a function by plotting and analyzing residuals. (CCSS: S- ID.6b) 3. Fit a linear function for a scatter plot that suggests a linear association. (CCSS: S- ID.6c) c. Interpret linear models. (CCSS: S-ID) i. Interpret the slope 2 and the intercept 3 of a linear model in the context of the data. (CCSS: S-ID.7) ii. Using technology, compute and interpret the correlation coefficient of a linear fit. (CCSS: S-ID.8) iii. Distinguish between correlation and causation. (CCSS: S-ID.9) 1. What makes data meaningful or actionable? 2. Why should attention be paid to an unexpected outcome? 3. How can summary statistics or data displays be accurate but misleading? 1. Facility with data organization, summary, and display allows the sharing of data efficiently and collaboratively to answer important questions such as is the climate changing, how do people think about ballot initiatives in the next election, or is there a connection between cancers in a community? 1. Mathematicians create visual and numerical representations of data to reveal relationships and meaning hidden in the raw data. 2. Mathematicians reason abstractly and quantitatively. (MP) 3. Mathematicians model with mathematics. (MP) 4. Mathematicians use appropriate tools strategically. (MP) Colorado Department of Education: High School Mathematics Revised: December 2010 Page 2 of 5

3 Communicate effective logical arguments using mathematical justification and proof. Mathematical argumentation involves making and testing conjectures, drawing valid conclusions, and justifying thinking 2. Statistical methods take variability into account supporting informed decisions making through quantitative studies designed to answer specific questions a. Understand and evaluate random processes underlying statistical experiments. (CCSS: S-IC) i. Describe statistics as a process for making inferences about population parameters based on a random sample from that population. (CCSS: S-IC.1) ii. Decide if a specified model is consistent with results from a given data-generating process. 4 (CCSS: S-IC.2) b. Make inferences and justify conclusions from sample surveys, experiments, and observational studies. (CCSS: S-IC) i. Identify the purposes of and differences among sample surveys, experiments, and observational studies; explain how randomization relates to each. (CCSS: S-IC.3) ii. Use data from a sample survey to estimate a population mean or proportion. (CCSS: S-IC.4) iii. Develop a margin of error through the use of simulation models for random sampling. (CCSS: S-IC.4) iv. Use data from a randomized experiment to compare two treatments; use simulations to decide if differences between parameters are significant. (CCSS: S-IC.5) v. Define and explain the meaning of significance, both statistical (using p-values) and practical (using effect size). vi. Evaluate reports based on data. (CCSS: S-IC.6) 1. How can the results of a statistical investigation be used to support an argument? 2. What happens to sample-to-sample variability when you increase the sample size? 3. When should sampling be used? When is sampling better than using a census? 4. Can the practical significance of a given study matter more than statistical significance? Why is it important to know the difference? 5. Why is the margin of error in a study important? 6. How is it known that the results of a study are not simply due to chance? 1. Inference and prediction skills enable informed decision-making based on data such as whether to stop using a product based on safety concerns, or whether a political poll is pointing to a trend. 1. Mathematics involves making conjectures, gathering data, recording results, and making multiple tests. 2. Mathematicians are skeptical of apparent trends. They use their understanding of randomness to distinguish meaningful trends from random occurrences. 3. Mathematicians construct viable arguments and critique the reasoning of others. (MP) 4. Mathematicians model with mathematics. (MP) 5. Mathematicians attend to precision. (MP) Colorado Department of Education: High School Mathematics Revised: December 2010 Page 3 of 5

4 Recognize and make sense of the many ways that variability, chance, and randomness appear in a variety of contexts 3. Probability models outcomes for situations in which there is inherent randomness a. Understand independence and conditional probability and use them to interpret data. (CCSS: S-CP) i. Describe events as subsets of a sample space 5 using characteristics (or categories) of the outcomes, or as unions, intersections, or complements of other events. 6 (CCSS: S-CP.1) ii. Explain 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. (CCSS: S-CP.2) iii. Using the conditional probability of A given B as P(A and B)/P(B), interpret the 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. (CCSS: S-CP.3) iv. Construct and interpret two-way frequency tables of data when two categories are associated with each object being classified. Use the twoway table as a sample space to decide if events are independent and to approximate conditional probabilities. 7 (CCSS: S-CP.4) v. Recognize and explain the concepts of conditional probability and independence in everyday language and everyday situations. 8 (CCSS: S- CP.5) b. Use the rules of probability to compute probabilities of compound events in a uniform probability model. (CCSS: S-CP) i. Find the conditional probability of A given B as the fraction of B s outcomes that also belong to A, and interpret the answer in terms of the model. (CCSS: S-CP.6) ii. Apply the Addition Rule, P(A or B) = P(A) + P(B) P(A and B), and interpret the answer in terms of the model. (CCSS: S-CP.7) c. Analyze * the cost of insurance as a method to offset the risk of a situation (PFL) *Indicates a part of the standard connected to the mathematical practice of Modeling. 1. Can probability be used to model all types of uncertain situations? For example, can the probability that the 50 th president of the United States will be female be determined? 2. How and why are simulations used to determine probability when the theoretical probability is unknown? 3. How does probability relate to obtaining insurance? (PFL) 1. Comprehension of probability allows informed decision-making, such as whether the cost of insurance is less than the expected cost of illness, when the deductible on car insurance is optimal, whether gambling pays in the long run, or whether an extended warranty justifies the cost. (PFL) 2. Probability is used in a wide variety of disciplines including physics, biology, engineering, finance, and law. For example, employment discrimination cases often present probability calculations to support a claim. 1. Some work in mathematics is much like a game. Mathematicians choose an interesting set of rules and then play according to those rules to see what can happen. 2. Mathematicians explore randomness and chance through probability. 3. Mathematicians construct viable arguments and critique the reasoning of others. (MP) 4. Mathematicians model with mathematics. (MP) Colorado Department of Education: High School Mathematics Revised: December 2010 Page 4 of 5

5 High School 1 including joint, marginal, and conditional relative frequencies. 2 rate of change. (CCSS: S-ID.7) 3 constant term. (CCSS: S-ID.7) 4 e.g., using simulation. (CCSS: S-IC.2) 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? (CCSS: S-IC.2) 5 the set of outcomes. (CCSS: S-CP.1) 6 or, and, not. (CCSS: S-CP.1) 7 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. (CCSS: S-CP.4) 8 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. (CCSS: S-CP.5) Colorado Department of Education: High School Mathematics Revised: December 2010 Page 5 of 5

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