OREGON MATH STANDARDS (2021): [HS.DR]
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.
2021 Oregon Math Guidance: HS.DR.A.1
Cluster: HS.DR.A - Formulate Statistical Investigative Questions
STANDARD: HS.DR.A.1
Standards Statement (2021):
Formulate multivariable statistical investigative questions and determine how data from samples can be collected and analyzed to provide an answer.
Connections:
Preceding Pathway Content (2021) | Subsequent Pathway Content (2021) | Cross Domain Connections (2021) | Common Core (CCSS) (2010) |
6.DR.A.1, 7.DR.A.1, 8.DR.A.1 | N/A | N/A | HSS.IC.A.1 HS.DR.A Crosswalk |
Standards Guidance:
Clarifications
- Focus on supporting students to understand and ask questions about how data could be collected.
- As students engage in multivariable thinking, the types of statistical investigative questions should expand to include questions concerning association and prediction.
- Students pose statistical investigative questions for a particular sample to determine any association of the variables of interest for that sample.
Terminology
- A statistical investigative question is one that requires data that will vary.
- Statistical questions are set in a context where one wants to know something; are based in variability or uncertainty; are data based; and are approximations/estimates from data analysis.
- Deterministic questions are based upon exact calculations or theoretical deductions elicited from prior certain knowledge.
- A sample is a subset of a population.
- Samples are taken when examining the entire population is not possible or feasible.
Teaching Strategies
- This is an opportunity for students to create a survey, collect data, and use graphical displays, sample statistics or two way tables to help estimate population parameters which are unknown values.
- It is important to understand samples used on social media or in the news.
Progressions
- CCSS - (HSS.IC.A.1) Understand the process of statistical reasoning, formulate questions, collect, analyze, and interpret data to answer statistical investigative questions.
- GAISE II - (1.C.1) Formulate multivariable statistical investigative questions and determine how data can be collected and analyzed to provide an answer
Examples
- Students can distinguish between situations where a small group (e.g., a classroom) is the entire population (census) and when it is a sample from a larger population (e.g., the classroom is used to answer a question about an entire grade level in a school).
- “Given a list of the arm spans of 9th grade students, what can be predicted about the heights of those students?”
- Illustrative Mathematics:
2021 Oregon Math Guidance: HS.DR.A.2
Cluster: HS.DR.A - Formulate Statistical Investigative Questions
STANDARD: HS.DR.A.2
Standards Statement (2021):
Formulate summative, comparative, and associative statistical investigative questions for surveys, observational studies, and experiments using primary or secondary data.
Connections:
Preceding Pathway Content (2021) | Subsequent Pathway Content (2021) | Cross Domain Connections (2021) | Common Core (CCSS) (2010) |
6.DR.A.1, 7.DR.A.1, 8.DR.A.1 | N/A | N/A | HSS.IC.B.3 HS.DR.A Crosswalk |
Standards Guidance:
Clarifications
- Students will draft statistical questions for which appropriate data can be collected and analyzed to answer the statistical investigative question.
- Students will use appropriate sampling techniques, critique a poorly constructed survey, and make suggestions for good questions.
- Students should understand the advantages and disadvantages of each data collection method for specific statistical questions.
- For experimental studies, students are able to identify, discuss, and explain the aspects of best statistical practice for designing an experimental study, including: (1) the clear identification of the statistical question to be investigated; (2) the variables under investigation; and the random selection of experimental units and/or (3) random assignment of treatments for experimental studies.
Terminology
- Types of statistical investigative questions include:
- Summative questions can be answered using quantitative measures of center and variability for numerical data sets (6.DR.B.2).
- Comparative questions can be answered using numerical data from random samples to compare between two populations (7.DR.D.4).
- Associative questions can be answered using bivariate quantitative data to investigate patterns of association between two quantities (8.DR.D.4).
- Types of data collections could include:
- Surveys involve the collection of data from a pre-defined group to gain insight and information about the statistical investigative question.
- Observational studies measure a sample as it is without attempting to influence the results.
- Experiments involve the use of a treatment to explore the effects of the treatment on a sample.
- Types of data include:
- Primary data is collected through first-hand sources such as surveys, experiments, and other studies.
- Secondary data is obtained from previously conducted studies or research.
Progressions
- GAISE II - (1.C.2) Pose summary, comparative, and association statistical investigative questions for surveys, observational studies, and experiments using primary or secondary data
Examples
- Illustrative Mathematics:
2021 Oregon Math Guidance: HS.DR.A.3
Cluster: HS.DR.A - Formulate Statistical Investigative Questions
STANDARD: HS.DR.A.3
Standards Statement (2021):
Formulate inferential statistical investigative questions regarding causality and prediction from correlation.
Connections:
Preceding Pathway Content (2021) | Subsequent Pathway Content (2021) | Cross Domain Connections (2021) | Common Core (CCSS) (2010) |
6.DR.A.1, 7.DR.A.1, 8.DR.A.1 | N/A | N/A | HSS.ID.C.9 HS.DR.A Crosswalk |
Standards Guidance:
Clarifications
- Students pose statistical investigative questions for a particular sample to determine any association of the variables of interest for that sample.
- Students should be able to understand the magnitude of a correlation coefficient represents the strength of association; understand and able to calculate a residual; understand that any straight line other than the best fit line (by least squares) will have a larger sum of squared residuals than the best fit line.
Boundaries
- Understand and explain the difference between correlation and causation. It is important for students to discover and understand that strong correlation does not indicate causation.
Progressions
- CCSS - (HSS.ID.C.9) Distinguish between correlation and causation.
- GAISE II - (1.C.3) Pose inferential statistical investigative questions regarding causality and prediction.
Examples
- Determine if statements of causation seem reasonable or unreasonable and justify reasoning.
- Correlation coefficients of r =‐.65 and r = .65 indicate the same strength.
- Illustrative Mathematics:
2021 Oregon Math Guidance: HS.DR.A.4
Cluster: HS.DR.A - Formulate Statistical Investigative Questions
STANDARD: HS.DR.A.4
Standards Statement (2021):
Use mathematical and statistical reasoning to formulate questions about data to evaluate conclusions and assess risks.
Connections:
Preceding Pathway Content (2021) | Subsequent Pathway Content (2021) | Cross Domain Connections (2021) | Common Core (CCSS) (2010) |
6.DR.A.1, 7.DR.A.1, 8.DR.A.1 | N/A | N/A | HSS.IC.B.6 HS.DR.A Crosswalk |
Standards Guidance:
Clarifications
- Focus of standard is supporting students to evaluate data presented in reports to evaluate conclusions and/or assess risks.
- Understand different ways in which number appear in everyday discussions of government, business, scientific results, and personal activities.
- Apply mathematical and statistical knowledge to inform and make decisions students face or many need to evaluate in society.
Teaching Strategies
- Generate reasonable estimates and use scale to place quantities in context.
- Interpret visual representations of data to assess conclusions and risks
- Locate data to assess validly of claims and conclusions.
Progressions
- CCSS – (HSS.IC.B.6) Evaluate reports based on data.
- NCTM Essential Skills - Mathematical and statistical reasoning about data can be used to evaluate conclusions and assess risks.
Examples
- Illustrative Mathematics:
2021 Oregon Math Guidance: HS.DR.B.5
Cluster: HS.DR.B - Collect and Consider Data
STANDARD: HS.DR.B.5
Standards Statement (2021):
Articulate what constitutes good practice in designing a sample survey, an experiment, and an observational study. Understand issues of bias and confounding variables in a study and their implications for interpretation.
Connections:
Preceding Pathway Content (2021) | Subsequent Pathway Content (2021) | Cross Domain Connections (2021) | Common Core (CCSS) (2010) |
6.DR.B.2, 7.DR.B.2, 8.DR.B.2 | N/A | N/A | n/a HS.DR.B Crosswalk |
Standards Guidance:
Clarifications
- Students are able to identify, discuss, and explain the aspects of best statistical practice for designing an experimental study, including:
- the clear identification of the statistical question to be investigated;
- the variables under investigation; and
- the random selection of experimental units and/or the random assignment of treatments to the experimental units.
- Students should be able to describe the ethical consequences of their experiments and analyses.
- Practices for handling data that enhance reproducibility and ensure ethical use include providing descriptions of alterations to collected data, proper treatment of sensitive information, maintaining the confidentiality of data and experimental units, and using Institutional Review Boards to review study designs.
Teaching Strategies
- Students should be able to design and conduct comparative experiments using random assignment and demonstrate correct methods for planning data collection for comparison of treatments.
- Students should be able to randomly assign treatments to experimental units.
- Students provide or select appropriate interpretations of graphical displays and numerical summaries to compare two or more groups in the context of a study.
Progressions
- GAISE II - (2.C.3) Understand what constitutes good practice in designing a sample survey, an experiment, and an observational study
- NCTM Essential Skills –
- The role of randomization is different in randomly selecting samples and in randomly assigning subjects to experimental treatment groups.
- The larger the sample size, the less the expected variability in the sampling distribution of a sample statistic.
2021 Oregon Math Guidance: HS.DR.B.6
Cluster: HS.DR.B - Collect and Consider Data
STANDARD: HS.DR.B.6
Standards Statement (2021):
Distinguish and choose between surveys, observational studies, and experiments to design an appropriate data collection that answers an investigative question of interest.
Connections:
Preceding Pathway Content (2021) | Subsequent Pathway Content (2021) | Cross Domain Connections (2021) | Common Core (CCSS) (2010) |
6.DR.B.2, 7.DR.B.2, 8.DR.B.2 | N/A | N/A | HSS.IC.B.4 HS.DR.B Crosswalk |
Standards Guidance:
Clarifications
- Students should understand the advantages and disadvantages of each data collection method for specific statistical questions.
- Students should be able to design and conduct comparative experiments using random assignment, or non-experimental designs when random assignment is not possible, and demonstrate correct methods for planning data collection for comparison of treatments.
Terminology
- Surveys involve the collection of data from a pre-defined group to gain insight and information about the statistical investigative question.
- Observational studies measure a sample as it is without attempting to influence the results.
- Experiments involve the use of a treatment to explore the effects of the treatment on a sample.
- For experimental designs, students should be able to randomly assign treatments to experimental units.
- Nonexperimental research is research that lacks the manipulation of an independent variable, random assignment of participants to conditions or orders of conditions, or both.
- Examples of non-experimental research could include case studies, focus groups, interviews, correlational or quasi-experimental research, or qualitative studies.
Boundaries
- Limit to population proportion, graphical representations, and visual overlap.
Progressions
- CCSS - (HSS.IC.B.4) Use data from a randomized experiment to compare two treatments to decide if differences between parameters are significant based on the statistics.
- GAISE II - (2.C.2) Distinguish between surveys, observational studies, and experiments.
- NCTM Essential Skills - Study designs are of three main types: sample survey, experiment, and observational study.
Examples
2021 Oregon Math Guidance: HS.DR.B.7
Cluster: HS.DR.B - Collect and Consider Data
STANDARD: HS.DR.B.7
Standards Statement (2021):
Apply an appropriate data collection plan when collecting primary data or selecting secondary data for the statistical investigative question of interest.
Connections:
Preceding Pathway Content (2021) | Subsequent Pathway Content (2021) | Cross Domain Connections (2021) | Common Core (CCSS) (2010) |
6.DR.B.2, 7.DR.B.2, 8.DR.B.2 | N/A | N/A | HSS.ID.B.5 HS.DR.B Crosswalk |
Standards Guidance:
Clarifications
- Students will use appropriate sampling techniques, critique a poorly constructed survey, and make suggestions for good questions.
- Students should identify types of displays that are appropriate for categorical data versus quantitative (numerical) data.
- Students should have opportunities to analyze meaningful, real-life data and recognize possible associations and trends in the data.
- Students should understand and apply concepts of sample space to describe categorical data.
Terminology
- Primary data is collected through first-hand sources such as surveys, experiments, and other studies.
- Secondary data is obtained from previously conducted studies or research.
Boundaries
- Students should consider features such as whether the population is well-defined, whether the sampling procedure is random or non-random, and whether the objectivity or bias of questions will result in valid/invalid answers.
Teaching Strategies
- Students may use spreadsheets, graphing calculators, and statistical software to create frequency tables and determine associations or trends in the data.
- Recognize the association between two variables by comparing conditional and marginal percentages.
- Describe patterns observed in the data
Progressions
- GAISE II - (2.C.1) Apply an appropriate data collection plan when collecting primary data or selecting secondary data for the statistical investigative question of interest.
- NCTM Essential Skills - The scope and validity of statistical inferences are dependent on the role of randomization in the study design.
Examples
- Read, interpret and write clear summaries of data displayed in a two-way frequency table.
- Calculate joint, marginal, and conditional relative frequencies.
- Make appropriate displays of joint, marginal, and conditional distributions.
- Illustrative Mathematics:
2021 Oregon Math Guidance: HS.DR.C.8
Cluster: HS.DR.C - Analyze Data
STANDARD: HS.DR.C.8
Standards Statement (2021):
Identify appropriate ways to summarize and then represent the distribution of univariate and bivariate data multiple ways with graphs and/or tables. Use technology to present data that supports interpretation of tabular and graphical representations.
Connections:
Preceding Pathway Content (2021) | Subsequent Pathway Content (2021) | Cross Domain Connections (2021) | Common Core (CCSS) (2010) |
6.DR.C.3, 7.DR.C.3, 8.DR.C.3 | N/A | N/A | HSS.ID.A.1 HSS.ID.B.6 HS.DR.C Crosswalk |
Standards Guidance:
Clarifications
- Students should identify types of displays that are appropriate for categorical data versus quantitative (numerical) data.
- Students should be able to construct scatterplots, and describe positive, negative or no relationship.
- Strength of association is demonstrated by degree of spread about the line of best fit in a scatterplot.
- Numerical data can be displayed visually with graphs, such as using dot plots, histograms, and box plots, to discover patterns and deviations from patterns.
- Students should use spreadsheets, graphing calculators, or statistical software to analyze data.
Terminology
- Univariate data involves describing a single variable, such as student ages or student heights.
- Bivariate data involves relationships between two variables, such as comparing the age of a student and their height.
Teaching Strategies
- This is an extension of middle school expectations where students display data on dot and box plots.
- Opportunity for students to collect and graph their own data and use modeling to fit a function to the data; use a function fitted to data to solve problems in the context of the data. (Emphasize linear models.)
- Students should be able to fluently utilize dot plots, histograms, and box plots to represent data.
Progressions
- GAISE II – (3.C.2) Identify appropriate ways to summarize quantitative or categorical data using tables, graphical displays, and numerical summary statistics, which includes using standard deviation as a measure of variability and a modified boxplot for identifying outliers.
Examples
- Analyze the strengths and weakness inherent in different types of visual data representations.
- Describe and give simple conclusions and interpretations of a graphical representation of data.
- Fit a linear function for a scatter plot that suggests a linear association.
- Illustrative Mathematics:
2021 Oregon Math Guidance: HS.DR.C.9
Cluster: HS.DR.C - Analyze Data
STANDARD: HS.DR.C.9
Standards Statement (2021):
Use statistics appropriate to the shape of the data distribution to compare the center and spread of two or more different data sets.
Connections:
Preceding Pathway Content (2021) | Subsequent Pathway Content (2021) | Cross Domain Connections (2021) | Common Core (CCSS) (2010) |
6.DR.C.3, 7.DR.C.3, 8.DR.C.3 | N/A | N/A | HSS.ID.A.2 HSS.ID.A.4 HS.DR.C Crosswalk |
Standards Guidance:
Clarifications
- Students should have the opportunity to gain an understanding of this concept through the use of technology tools.
- Students should use the meaning of mean absolute deviation (MAD) learning in sixth grade to interpret the meaning of standard deviation.
- Students were first introduced to the concept of MAD as a tool for comparing variability of multiple data sets in sixth grade mathematics.
- Students should be able to construct scatterplots, and describe positive, negative or no relationship.
- Data may be displayed using histograms, dot plots, or smooth normal curves.
Boundaries
- Quantitative data can be described in terms of key characteristics: measures of shape, center, and spread.
- Measures of center include the mean, median, and mode.
- Measures of spread include the range, interquartile range, and standard deviation.
- The shape of a data distribution might be described as symmetric, skewed, uniform, or bell shaped, and it might be summarized by a statistic measuring center (such as mean or median) and a statistic measuring spread (such as standard deviation or interquartile range).
Progressions
- GAISE II –
- (3.C.6) Describe associations between two categorical variables using measures such as difference in proportions and relative risk
- (3.C.7) Describe the relationship between two quantitative variables by interpreting Pearson’s correlation coefficient and a least-squares regression line
- NCTM Essential Skills - Distributions of quantitative data (continuous or discrete) in one variable should be described in the context of the data with respect to what is typical (the shape, with appropriate measures of center and variability, including standard deviation) and what is not (outliers), and these characteristics can be used to compare two or more subgroups with respect to a variable.
Examples
2021 Oregon Math Guidance: HS.DR.C.10
Cluster: HS.DR.C - Analyze Data
STANDARD: HS.DR.C.10
Standards Statement (2021):
Use data to compare two groups, describe sample variability, and decide if differences between parameters are significant based on the statistics.
Connections:
Preceding Pathway Content (2021) | Subsequent Pathway Content (2021) | Cross Domain Connections (2021) | Common Core (CCSS) (2010) |
6.DR.C.3, 7.DR.C.3, 8.DR.C.3 | N/A | N/A | HSS.ID.A.3 HSS.IC.B.5 HS.DR.C Crosswalk |
Standards Guidance:
Clarifications
- Students should be able to describe how population estimates may be overstated or understated due to the presence of outliers.
- Students should be able to describe how missing or erroneous values can lead to biased or inaccurate estimations.
- Strength of association is demonstrated by degree of spread about the line of best fit in a scatterplot.
- Students should be able to recognize how sampling variability is influenced by sample size.
Teaching Strategies
- Use data from multiple sources to interpret differences in shape, center and spread
- Discuss the effect of outliers on measures of center and spread.
- Use the 1.5 IQR rule to determine the outliers and analyze their effects on the data set.
Progressions
- CCSS – (HSS.ID.A.3) Interpret differences in shape, center, and spread in the context of the data sets, accounting for possible effects of extreme data points (outliers).
- GAISE II – (3.C.4) Understand how sampling distributions (developed through simulation) are used to describe the sample-to-sample variability of sample statistics
- NCTM Essential Skills - Analyzing the association between two quantitative variables should involve statistical procedures, such as examining (with technology) the sum of squared deviations in fitting a linear model, analyzing residuals for patterns, generating a least-squares regression line and finding a correlation coefficient, and differentiating between correlation and causation.
Examples
- Students should use spreadsheets, graphing utilities and statistical software to identify outliers and analyze data sets with and without outliers as appropriate.
- Using the 1.5 IQR rule on data set {5,7,8,10,11,12,30}, 30 is determined to be an outlier since it is greater than 19.5, which is the 1.5*IQR +12 (the 3Q).
2021 Oregon Math Guidance: HS.DR.D.11
Cluster: HS.DR.D - Interpret data and answer investigative questions
STANDARD: HS.DR.D.11
Standards Statement (2021):
Use statistical evidence from analyses to answer statistical investigative questions, and communicate the findings in a variety of formats (verbal, written, visual) to support informed data-based decisions.
Connections:
Preceding Pathway Content (2021) | Subsequent Pathway Content (2021) | Cross Domain Connections (2021) | Common Core (CCSS) (2010) |
6.DR.D.4, 7.DR.D.4, 8.DR.D.4 | N/A | 8.AFN.B.4, HS.AFN.A.3 | HSS.ID.C.7 HS.DR.D Crosswalk |
Standards Guidance:
Clarification
- Identify when data can be generalized to a target population.
- Samples must be randomly selected from the appropriate population to allow for generalizations that extend beyond the sample from which the data were collected.
- Sampling procedures that are not random do not allow for generalizations to the sampled population because they may be biased.
- Evidence could be interpreted from data displays such as histograms, dot plots, or smooth normal curves.
Teaching Strategies
- Students should be able to recognize that sample statistics vary with repeated sampling.
- Students should be able to interpret the sampling variability in a summary statistic.
- Students should be able to interpret the sampling variability from simulation studies of statistics.
- Students should be able to recognize how sampling variability is influenced by sample size.
- Recognize that there are data sets for which the empirical rule is not appropriate.
Progressions
- GAISE II – (4.C.1) Use statistical evidence from analyses to answer the statistical investigative questions and communicate results through more formal reports and presentations
Examples
- Illustrative Mathematics:
2021 Oregon Math Guidance: HS.DR.D.12
Cluster: HS.DR.D - Interpret data and answer investigative questions
STANDARD: HS.DR.D.12
Standards Statement (2021):
Articulate what it means for an outcome or an estimate of a population characteristic to be plausible or not plausible compared to chance variation.
Connections:
Preceding Pathway Content (2021) | Subsequent Pathway Content (2021) | Cross Domain Connections (2021) | Common Core (CCSS) (2010) |
6.DR.D.4, 7.DR.D.4, 8.DR.D.4 | N/A | N/A | HSS.ID.C.8 HS.DR.D Crosswalk |
Standards Guidance:
Clarifications
- Students should be able to decide whether an observed difference is something that would be likely to be observed by chance and whether this difference has any practical meaning.
- Students recognize that significance is demonstrated by a result that is unlikely to occur by chance
- Students recognize that statistical, but not practical, significance is influenced by sample size.
Teaching Strategies
- Students should use spreadsheets, graphing calculators and statistical software to represent data, describe how the variables are related, fit functions to data, perform regressions, and calculate residuals and correlation coefficients.
- Students should be given the opportunity to utilize interactive graphing technologies to interpret the correlation coefficient, r.
- Students should be able to use the correlation coefficient, r, to make predictions and describe the reasonableness of the prediction in the context of a practical, real-life situation.
- Explain that the correlation coefficient must be between −1 and 1 inclusive and explain what each of these values means.
- Determine whether the correlation coefficient shows a weak positive, strong positive, weak negative, strong negative, or no linear correlation. Interpret what the correlation coefficient is telling about the data.
Progressions
- GAISE II – (4.C.3) Understand what it means for an outcome or an estimate of a population characteristic to be plausible or not plausible compared to chance variation
- NCTM Essential Skills - Data-analysis techniques can be used to develop models of contextual situations and to generate and evaluate possible solutions to real problems involving those contexts.
Examples
- Illustrative Mathematics:
2021 Oregon Math Guidance: HS.DR.D.13
Cluster: HS.DR.D - Interpret data and answer investigative questions
STANDARD: HS.DR.D.13
Standards Statement (2021):
Use multivariate thinking to articulate how variables impact one another, and measure the strength of association using correlation coefficients for regression curves.
Connections:
Preceding Pathway Content (2021) | Subsequent Pathway Content (2021) | Cross Domain Connections (2021) | Common Core (CCSS) (2010) |
6.DR.D.4, 7.DR.D.4, 8.DR.D.4 | N/A | N/A | HSS.ID.C.9 HS.DR.D Crosswalk |
Standards Guidance:
Clarifications
- As students engage in multivariable thinking, the types of statistical investigative questions should expand to include questions concerning association and prediction.
- Students should be able to identify contexts where a change in one attribute may be related to a change in another attribute.
- Students should be able to describe how population estimates may be overstated or understated due to the presence of outliers.
- Students should be able to describe how missing or erroneous values can lead to biased or inaccurate estimations.
Boundaries
- Students should be able to provide a reasonable estimate of the Pearson's correlation coefficient (r) for a scatterplot; identify linear and non‐linear relationships in scatterplots; correctly interpret the strength of a linear relationship based on r.
- Students should be able to understand the magnitude of a correlation coefficient represents the strength of association; understand and able to calculate a residual; understand that any straight line other than the best fit line (by least squares) will have a larger sum of squared residuals than the best fit line.
Teaching Strategies
- Opportunity to connect the concept of distinguishing between correlation and causation as students interpret data.
- Understand and explain the difference between correlation and causation. It is important for students to discover and understand that strong correlation does not indicate causation.
Progressions
- GAISE II – (4.C.6) Use multivariate thinking to understand how variables impact one another.
- NCTM Essential Skills - Making and defending informed data-based decisions is a characteristic of a quantitatively literate person.
Examples
- Determine if statements of causation seem reasonable or unreasonable and justify reasoning.
- Correlation coefficients of r =‐.65 and r = .65 indicate the same strength.
- Illustrative Mathematics:
2021 Oregon Math Guidance: HS.DR.E.14
Cluster: HS.DR.E - Understand independence and conditional probability and use them to interpret data
STANDARD: HS.DR.E.14
Standards Statement (2021):
Describe the possible outcomes for a situation as subsets of a sample space.
Connections:
Preceding Pathway Content (2021) | Subsequent Pathway Content (2021) | Cross Domain Connections (2021) | Common Core (CCSS) (2010) |
7.RP.B.4, 7.RP.B.5, 7.RP.B.6, 7.RP.B.7 | N/A | N/A | HSS.CP.A.1 HS.DR.E Crosswalk |
Standards Guidance:
Progressions
- This provides an opportunity for students to engage with finding the outcomes of situations which include words such as and, or, not, if, and all, and to grammatical constructions that reflect logical connections.
Examples
- Illustrative Mathematics:
2021 Oregon Math Guidance: HS.DR.E.15
Cluster: HS.DR.E - Understand independence and conditional probability and use them to interpret data
STANDARD: HS.DR.E.15
Standards Statement (2021):
Recognize and explain the concepts of conditional probability and independence in everyday language and everyday situations.
Connections:
Preceding Pathway Content (2021) | Subsequent Pathway Content (2021) | Cross Domain Connections (2021) | Common Core (CCSS) (2010) |
7.RP.B.6, 7.RP.B.7 | N/A | N/A | HSS.CP.A.5 HS.DR.E Crosswalk |
Standards Guidance:
Examples
- Compare the chance of having lung cancer if you are a smoker with the chance of being a smoker if you have lung cancer.
- Illustrative Mathematics: