## Description

- Overview:
- The intent of clarifying statements is to provide additional guidance for educators to communicate the intent of the standard to support the future development of curricular resources and assessments aligned to the 2021 math standards. Clarifying statements can be in the form of succinct sentences or paragraphs that attend to one of four types of clarifications: (1) Student Experiences; (2) Examples; (3) Boundaries; and (4) Connection to Math Practices.

- Remix of:
- OREGON MATH STANDARDS (2021): [TEMPLATE]
- Subject:
- Mathematics
- Level:
- High School
- Material Type:
- Teaching/Learning Strategy
- Author:
- Mark Freed
- Date Added:
- 07/11/2023

- License:
- Creative Commons Attribution
- Language:
- English

## Standards

Learning Domain: Statistics and Probability: Conditional Probability and the Rules of Probability

Standard: 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,"ť "and,"ť "not"ť).*

Degree of Alignment: Not Rated (0 users)

Learning Domain: Statistics and Probability: Conditional Probability and the Rules of Probability

Standard: 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.*

Degree of Alignment: Not Rated (0 users)

Learning Domain: Statistics and Probability: Making Inferences and Justifying Conclusions

Standard: Understand statistics as a process for making inferences about population parameters based on a random sample from that population.*

Degree of Alignment: Not Rated (0 users)

Learning Domain: Statistics and Probability: Making Inferences and Justifying Conclusions

Standard: 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.*

Degree of Alignment: Not Rated (0 users)

Learning Domain: Statistics and Probability: Making Inferences and Justifying Conclusions

Standard: Use data from a randomized experiment to compare two treatments; use simulations to decide if differences between parameters are significant.*

Degree of Alignment: Not Rated (0 users)

Learning Domain: Statistics and Probability: Making Inferences and Justifying Conclusions

Standard: Evaluate reports based on data.*

Degree of Alignment: Not Rated (0 users)

Learning Domain: Statistics and Probability: Interpreting Categorical and Quantitative Data

Standard: Represent data with plots on the real number line (dot plots, histograms, and box plots).*

Degree of Alignment: Not Rated (0 users)

Learning Domain: Statistics and Probability: Interpreting Categorical and Quantitative Data

Standard: 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.*

Degree of Alignment: Not Rated (0 users)

Learning Domain: Statistics and Probability: Interpreting Categorical and Quantitative Data

Standard: Interpret differences in shape, center, and spread in the context of the data sets, accounting for possible effects of extreme data points (outliers).*

Degree of Alignment: Not Rated (0 users)

Learning Domain: Statistics and Probability: Interpreting Categorical and Quantitative Data

Standard: 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.*

Degree of Alignment: Not Rated (0 users)

Learning Domain: Statistics and Probability: Interpreting Categorical and Quantitative Data

Standard: 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.*

Degree of Alignment: Not Rated (0 users)

Learning Domain: Statistics and Probability: Interpreting Categorical and Quantitative Data

Standard: Represent data on two quantitative variables on a scatter plot, and describe how the variables are related.*

Degree of Alignment: Not Rated (0 users)

Learning Domain: Statistics and Probability: Interpreting Categorical and Quantitative Data

Standard: Interpret the slope (rate of change) and the intercept (constant term) of a linear model in the context of the data.*

Degree of Alignment: Not Rated (0 users)

Learning Domain: Statistics and Probability: Interpreting Categorical and Quantitative Data

Standard: Compute (using technology) and interpret the correlation coefficient of a linear fit.*

Degree of Alignment: Not Rated (0 users)

Learning Domain: Statistics and Probability: Interpreting Categorical and Quantitative Data

Standard: Distinguish between correlation and causation.*

Degree of Alignment: Not Rated (0 users)

Cluster: Summarize, represent, and interpret data on a single count or measurement variable

Standard: Represent data with plots on the real number line (dot plots, histograms, and box plots).*

Degree of Alignment: Not Rated (0 users)

Cluster: Summarize, represent, and interpret data on a single count or measurement variable

Standard: 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.*

Degree of Alignment: Not Rated (0 users)

Cluster: Summarize, represent, and interpret data on a single count or measurement variable

Standard: Interpret differences in shape, center, and spread in the context of the data sets, accounting for possible effects of extreme data points (outliers).*

Degree of Alignment: Not Rated (0 users)

Cluster: Summarize, represent, and interpret data on a single count or measurement variable

Standard: 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.*

Degree of Alignment: Not Rated (0 users)

Cluster: Summarize, represent, and interpret data on two categorical and quantitative variables

Standard: 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.*

Degree of Alignment: Not Rated (0 users)

Cluster: Summarize, represent, and interpret data on two categorical and quantitative variables

Standard: Represent data on two quantitative variables on a scatter plot, and describe how the variables are related.*

Degree of Alignment: Not Rated (0 users)

Cluster: Interpret linear models

Standard: Interpret the slope (rate of change) and the intercept (constant term) of a linear model in the context of the data.*

Degree of Alignment: Not Rated (0 users)

Cluster: Interpret linear models

Standard: Compute (using technology) and interpret the correlation coefficient of a linear fit.*

Degree of Alignment: Not Rated (0 users)

Cluster: Interpret linear models

Standard: Distinguish between correlation and causation.*

Degree of Alignment: Not Rated (0 users)

Cluster: Understand and evaluate random processes underlying statistical experiments

Standard: Understand statistics as a process for making inferences about population parameters based on a random sample from that population.*

Degree of Alignment: Not Rated (0 users)

Cluster: Make inferences and justify conclusions from sample surveys, experiments, and observational studies

Standard: Recognize the purposes of and differences among sample surveys, experiments, and observational studies; explain how randomization relates to each.*

Degree of Alignment: Not Rated (0 users)

Cluster: Make inferences and justify conclusions from sample surveys, experiments, and observational studies

Standard: 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.*

Degree of Alignment: Not Rated (0 users)

Cluster: Make inferences and justify conclusions from sample surveys, experiments, and observational studies

Standard: Use data from a randomized experiment to compare two treatments; use simulations to decide if differences between parameters are significant.*

Degree of Alignment: Not Rated (0 users)

Standard: Evaluate reports based on data.*

Degree of Alignment: Not Rated (0 users)

Cluster: Understand independence and conditional probability and use them to interpret data

Standard: 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”).*

Degree of Alignment: Not Rated (0 users)

Cluster: Understand independence and conditional probability and use them to interpret data

Standard: 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.*

Degree of Alignment: Not Rated (0 users)

## Evaluations

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## Comments