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Skills available for Arkansas high school math standards

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Arkansas Mathematics Standards (adopted in 2023): Advanced Topics & Modeling Arkansas Mathematics Standards (adopted in 2023): Advanced Topics & Modeling
Arkansas Mathematics Standards (adopted in 2023): Algebra I Arkansas Mathematics Standards (adopted in 2023): Algebra I
Arkansas Mathematics Standards (adopted in 2023): Algebra II Arkansas Mathematics Standards (adopted in 2023): Algebra II
Arkansas Mathematics Standards (adopted in 2023): Algebra III Arkansas Mathematics Standards (adopted in 2023): Algebra III
Arkansas Mathematics Standards (adopted in 2023): Calculus Arkansas Mathematics Standards (adopted in 2023): Calculus
Arkansas Mathematics Standards (adopted in 2023): Critical Algebra I Arkansas Mathematics Standards (adopted in 2023): Critical Algebra I
Arkansas Mathematics Standards (adopted in 2023): Geometry Arkansas Mathematics Standards (adopted in 2023): Geometry
Arkansas Mathematics Standards (adopted in 2023): Mathematical Practices Arkansas Mathematics Standards (adopted in 2023): Mathematical Practices
Arkansas Mathematics Standards (adopted in 2023): Pre-Calculus Arkansas Mathematics Standards (adopted in 2023): Pre-Calculus
Arkansas Mathematics Standards (adopted in 2023): Statistics Arkansas Mathematics Standards (adopted in 2023): Statistics
Arkansas Mathematics Standards (adopted in 2023): Technical Math Arkansas Mathematics Standards (adopted in 2023): Technical Math
Arkansas Curriculum Framework: Advanced Topics and Modeling in Mathematics Arkansas Curriculum Framework: Advanced Topics and Modeling in Mathematics
Arkansas Curriculum Framework: Algebra I Arkansas Curriculum Framework: Algebra I
Arkansas Curriculum Framework: Algebra I Part A Arkansas Curriculum Framework: Algebra I Part A
Arkansas Curriculum Framework: Algebra I Part B Arkansas Curriculum Framework: Algebra I Part B
Arkansas Curriculum Framework: Algebra II/Advanced Algebra Arkansas Curriculum Framework: Algebra II/Advanced Algebra
Arkansas Curriculum Framework: Algebra III Arkansas Curriculum Framework: Algebra III
Arkansas Curriculum Framework: Bridge to Algebra II Arkansas Curriculum Framework: Bridge to Algebra II
Arkansas Curriculum Framework: Calculus Arkansas Curriculum Framework: Calculus
Arkansas Curriculum Framework: Computer Science and Mathematics Arkansas Curriculum Framework: Computer Science and Mathematics
Arkansas Curriculum Framework: Geometry Arkansas Curriculum Framework: Geometry
Arkansas Curriculum Framework: Geometry A Arkansas Curriculum Framework: Geometry A
Arkansas Curriculum Framework: Geometry B Arkansas Curriculum Framework: Geometry B
Arkansas Curriculum Framework: Mathematical Applications and Algorithms Arkansas Curriculum Framework: Mathematical Applications and Algorithms
Arkansas Curriculum Framework: Precalculus Arkansas Curriculum Framework: Precalculus
Arkansas Curriculum Framework: Statistics Arkansas Curriculum Framework: Statistics

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MI Making Inferences & Justifying Conclusion

Surveys, Experiments, & Observational Data: Students make inferences and justify conclusions from sample surveys, experiments, and observational studies.
- S.MI.1 Estimate a population mean or proportion from a sample survey; develop a margin of error through the use of simulation models for random sampling.
- S.MI.2 Calculate the standardized test statistic and p-value for a test about a population proportion and a population mean; determine if the sample data provides convincing evidence against a parameter claim.
- S.MI.3 Compare two treatment groups in an experiment and determine if the difference in parameters is significant by calculating the standardized test statistics and p-value.
  - Analyze the results of an experiment using simulations (PC-FF.9)

RP Conditional Probability & Rules of Probability

Compute Probability of Compound Events: Students use the rules of probability to compute probabilities of compound events.
- S.RP.1 Determine unions or intersections of events in a sample space; determine complements of events.
  - Sets, subsets, and complements (G)
  - Unions and intersections of sets (A2)
- S.RP.2 Identify the two components that make up a legitimate probability model/distribution.
  - Identify probability distributions (PC-DD.1)
Independence & Conditional Probability: Students understand and use independence and conditional probability to interpret data.
- S.RP.3 Determine if two events, A and B, are independent when given the probabilities of A and B.
  - Identify independent events (PC-CC.5)
- S.RP.4 Calculate and use conditional probabilities to determine if events are independent.
  - Find conditional probabilities (PC-CC.6)
  - Independence and conditional probability (PC-CC.7)
- S.RP.5 Create and analyze two-way frequency tables of data to calculate marginal, joint, and conditional probabilities.
  - Find probabilities using two-way frequency tables (PC-CC.4)
  - Find conditional probabilities using two-way frequency tables (PC-CC.8)
- S.RP.6 Using a two-way table, determine if two events are independent.
  - Identify independent events (PC-CC.5)
- S.RP.7 Explain conditional probability and independence using everyday language in a variety of real-world contexts.
- S.RP.8 Find the conditional probability of A given B, P(A|B), and interpret the answer in terms of the model, including two-way frequency tables and Venn diagrams.
  - Find conditional probabilities (PC-CC.6)
  - Find conditional probabilities using two-way frequency tables (PC-CC.8)
- S.RP.9 Apply the Addition Rule, P(A or B) = P(A) + P(B) – P(A and B) and interpret the answer.
  - Find probabilities using the addition rule (PC-CC.9)
- S.RP.10 Identify whether or not two events are mutually exclusive / disjoint.
- S.RP.11 Apply the general Multiplication Rule, P(A and B) = P(A)P(B|A) = P(B)P(A|B) and interpret the answer.
  - Find conditional probabilities (PC-CC.6)
- S.RP.12 Compute the probability of compound events and solve problems using combinations, permutations, Venn Diagrams, and Tree Diagrams.

PMD Use Probability to Make Decisions

Expected Values: Students calculate and use expected values of random variables to solve problems.
- S.PMD.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.PMD.2 Calculate the expected value for a discrete random variable; describe the expected value as the mean or typical value of the probability distribution in context.
  - Expected values of random variables (PC-DD.5)
- S.PMD.3 Create a probability distribution of a discrete random variable using theoretical probabilities and use the probability distribution to calculate the probability of an event.
  - Write the probability distribution for a game of chance (PC-DD.8)
  - Expected values for a game of chance (PC-DD.9)
- S.PMD.4 Create a probability distribution for a discrete random variable using experimental or observational data; calculate the expected value.
  - Write a discrete probability distribution (PC-DD.3)
  - Expected values of random variables (PC-DD.5)
- S.PMD.5 Use probability density models such as the normal curve and uniform density curve to model real-world data; calculate probabilities of continuous random variables using these models.
Outcomes of Decisions: Students evaluate outcomes of decisions using probability.
- S.PMD.6 Analyze the costs and benefits of possible outcomes of making a decision by assigning probabilities to particular payoff values of a discrete random variable and calculate expected values.
  - Expected values for a game of chance (PC-DD.9)
  - Choose the better bet (PC-DD.10)

CD Collecting Data & Data Bias

Data Collection: Students explore best practices of collecting data while identifying possible sources of bias in data collection methods.
- S.CD.1 Describe how to take a simple random sample using technology or a random number table.
- S.CD.2 Use randomization strategies to ensure random selection processes are fair.
  - Experiment design (PC-FF.8)
- S.CD.3 Understand that certain types of sampling methods may lead to bias, such as convenience and voluntary samples.
  - Identify biased samples (PC-FF.1)

DD Displaying & Describing Distributions of Data

Data Representation: Students represent raw data in tabular and graphical form to describe features of the data and summarize trends.
- S.DD.1 Distinguish between categorical and quantitative data.
- S.DD.2 Determine if there is an association between two quantitative variables using the correlation coefficient and scatter plots.
  - Match correlation coefficients to scatter plots (PC-GG.2)
  - Calculate correlation coefficients (PC-GG.3)
- S.DD.3 Model real-world data using least squares regression techniques.

Arkansas

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MI Making Inferences & Justifying Conclusion

Surveys, Experiments, & Observational Data: Students make inferences and justify conclusions from sample surveys, experiments, and observational studies.

S.MI.1 Estimate a population mean or proportion from a sample survey; develop a margin of error through the use of simulation models for random sampling.

S.MI.2 Calculate the standardized test statistic and p-value for a test about a population proportion and a population mean; determine if the sample data provides convincing evidence against a parameter claim.

S.MI.3 Compare two treatment groups in an experiment and determine if the difference in parameters is significant by calculating the standardized test statistics and p-value.

RP Conditional Probability & Rules of Probability

Compute Probability of Compound Events: Students use the rules of probability to compute probabilities of compound events.

S.RP.1 Determine unions or intersections of events in a sample space; determine complements of events.

S.RP.2 Identify the two components that make up a legitimate probability model/distribution.

Independence & Conditional Probability: Students understand and use independence and conditional probability to interpret data.

S.RP.3 Determine if two events, A and B, are independent when given the probabilities of A and B.

S.RP.4 Calculate and use conditional probabilities to determine if events are independent.

S.RP.5 Create and analyze two-way frequency tables of data to calculate marginal, joint, and conditional probabilities.

S.RP.6 Using a two-way table, determine if two events are independent.

S.RP.7 Explain conditional probability and independence using everyday language in a variety of real-world contexts.

S.RP.8 Find the conditional probability of A given B, P(A|B), and interpret the answer in terms of the model, including two-way frequency tables and Venn diagrams.

S.RP.9 Apply the Addition Rule, P(A or B) = P(A) + P(B) – P(A and B) and interpret the answer.

S.RP.10 Identify whether or not two events are mutually exclusive / disjoint.

S.RP.11 Apply the general Multiplication Rule, P(A and B) = P(A)P(B|A) = P(B)P(A|B) and interpret the answer.

S.RP.12 Compute the probability of compound events and solve problems using combinations, permutations, Venn Diagrams, and Tree Diagrams.

PMD Use Probability to Make Decisions

Expected Values: Students calculate and use expected values of random variables to solve problems.

S.PMD.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.PMD.2 Calculate the expected value for a discrete random variable; describe the expected value as the mean or typical value of the probability distribution in context.

S.PMD.3 Create a probability distribution of a discrete random variable using theoretical probabilities and use the probability distribution to calculate the probability of an event.

S.PMD.4 Create a probability distribution for a discrete random variable using experimental or observational data; calculate the expected value.

S.PMD.5 Use probability density models such as the normal curve and uniform density curve to model real-world data; calculate probabilities of continuous random variables using these models.

Outcomes of Decisions: Students evaluate outcomes of decisions using probability.

S.PMD.6 Analyze the costs and benefits of possible outcomes of making a decision by assigning probabilities to particular payoff values of a discrete random variable and calculate expected values.

CD Collecting Data & Data Bias

Data Collection: Students explore best practices of collecting data while identifying possible sources of bias in data collection methods.

S.CD.1 Describe how to take a simple random sample using technology or a random number table.

S.CD.2 Use randomization strategies to ensure random selection processes are fair.

S.CD.3 Understand that certain types of sampling methods may lead to bias, such as convenience and voluntary samples.

DD Displaying & Describing Distributions of Data

Data Representation: Students represent raw data in tabular and graphical form to describe features of the data and summarize trends.

S.DD.1 Distinguish between categorical and quantitative data.

S.DD.2 Determine if there is an association between two quantitative variables using the correlation coefficient and scatter plots.

S.DD.3 Model real-world data using least squares regression techniques.