Common Core Math Standards
 6.EE.B.6

Use variables to represent numbers and write expressions when solving a realworld or mathematical problem; understand that a variable can represent an unknown number, or, depending on the purpose at hand, any number in a specified set. [See: Functions Make Life Easier!; Functions: Contracts, Examples & Definitions; Solving Word Problems with the Design Recipe; Defining Table Functions; Grouped Samples; Linear Regression.]
 6.RP.A

Understand ratio concepts and use ratio reasoning to solve problems. [See: Bar and Pie Charts.]
 6.SP.A

Develop understanding of statistical variability. [See: Visualizing the “Shape” of Data; Measures of Center; Box Plots; Standard Deviation; Scatter Plots; Linear Regression.]
 6.SP.A.1

Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers. [See: Introduction to Data Science; Choosing Your Dataset.]
 6.SP.A.2

Understand that a set of data collected to answer a statistical question has a distribution which can be described by its center, spread, and overall shape. [See: Visualizing the “Shape” of Data; Measures of Center; Box Plots; Standard Deviation.]
 6.SP.B.4

Display numerical data in plots on a number line, including dot plots, histograms, and box plots. [See: Histograms; Visualizing the “Shape” of Data; Box Plots; Standard Deviation.]
 6.SP.B.5

Summarize numerical data sets in relation to their context. [See: Measures of Center; Box Plots; Standard Deviation.]
 6.SP.B.5.C

Summarize numerical data sets in relation to their context by giving quantitative measures of center (median and/or mean) and variability (interquartile range and/or mean absolute deviation), as well as describing any overall pattern and any striking deviations from the overall pattern with reference to the context in which the data were gathered. [See: Measures of Center; Box Plots; Standard Deviation.]
 6.SP.B.5.D

Summarize numerical data sets in relation to their context by relating the choice of measures of center and variability to the shape of the data distribution and the context in which the data were gathered. [See: Measures of Center.]
 7.EE.B

Solve reallife and mathematical problems using numerical and algebraic expressions and equations. [See: Solving Word Problems with the Design Recipe.]
 7.EE.B.4

Use variables to represent quantities in a realworld or mathematical problem, and construct simple equations and inequalities to solve problems by reasoning about the quantities. [See: Functions Make Life Easier!; Solving Word Problems with the Design Recipe.]
 7.RP.A

Analyze proportional relationships and use them to solve realworld and mathematical problems. [See: Bar and Pie Charts.]
 7.RP.A.3

Use proportional relationships to solve multistep ratio and percent problems. [See: Bar and Pie Charts.]
 7.SP.A

Use random sampling to draw inferences about a population. [See: Probability, Inference, and Sample Size.]
 7.SP.A.1

Understand that statistics can be used to gain information about a population by examining a sample of the population; generalizations about a population from a sample are valid only if the sample is representative of that population. Understand that random sampling tends to produce representative samples and support valid inferences. [See: Probability, Inference, and Sample Size.]
 7.SP.B

Draw informal comparative inferences about two populations. [See: Bar and Pie Charts.]
 8.F.A.1

Understand that a function is a rule that assigns to each input exactly one output. The graph of a function is the set of ordered pairs consisting of an input and the corresponding output. [See: Contracts.]
 8.F.B

Use functions to model relationships between quantities. [See: Functions Make Life Easier!; Solving Word Problems with the Design Recipe.]
 8.SP.A.1

Construct and interpret scatter plots for bivariate measurement data to investigate patterns of association between two quantities. Describe patterns such as clustering, outliers, positive or negative association, linear association, and nonlinear association. [See: Scatter Plots; Grouped Samples; Correlations; Linear Regression.]
 8.SP.A.2

Know that straight lines are widely used to model relationships between two quantitative variables. For scatter plots that suggest a linear association, informally fit a straight line, and informally assess the model fit by judging the closeness of the data points to the line. [See: Scatter Plots; Correlations; Linear Regression.]
 8.SP.A.3

Use the equation of a linear model to solve problems in the context of bivariate measurement data, interpreting the slope and intercept. [See: Linear Regression.]
 HSA.SSE.A.1

Interpret expressions that represent a quantity in terms of its context. [See: Functions Make Life Easier!.]
 HSA.SSE.A.1.B

Interpret complicated expressions by viewing one or more of their parts as a single entity. [See: Custom Scatter Plots.]
 HSF.BF.A.1

Write a function that describes a relationship between two quantities. [See: Solving Word Problems with the Design Recipe.]
 HSF.IF.A.1

Understand that a function from one set (called the domain) to another set (called the range) assigns to each element of the domain exactly one element of the range. If f is a function and x is an element of its domain, then f(x) denotes the output of f corresponding to the input x. The graph of f is the graph of the equation y = f(x). [See: Contracts.]
 HSF.IF.A.2

Use function notation, evaluate functions for inputs in their domains, and interpret statements that use function notation in terms of a context. [See: Contracts.]
 HSF.IF.B

Interpret functions that arise in applications in terms of the context. [See: Functions Make Life Easier!.]
 HSF.IF.C

Analyze functions using different representations. [See: Functions: Contracts, Examples & Definitions; Solving Word Problems with the Design Recipe.]
 HSS.IC.B.3

Recognize the purposes of and differences among sample surveys, experiments, and observational studies; explain how randomization relates to each. [See: Probability, Inference, and Sample Size.]
 HSS.IC.B.6

Evaluate reports based on data. [See: Threats to Validity.]
 HSS.ID.A

Summarize, represent, and interpret data on a single count or measurement variable. [See: Bar and Pie Charts.]
 HSS.ID.A.1

Represent data with plots on the real number line (dot plots, histograms, and box plots). [See: Histograms; Visualizing the “Shape” of Data; Box Plots; Standard Deviation.]
 HSS.ID.A.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. [See: Histograms; Measures of Center; Box Plots; Standard Deviation.]
 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). [See: Histograms; Visualizing the “Shape” of Data; Measures of Center; Box Plots; Standard Deviation.]
 HSS.ID.B.6

Represent data on two quantitative variables on a scatter plot, and describe how the variables are related. [See: Scatter Plots; Correlations.]
 HSS.ID.B.6.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, quadratic, and exponential models. [See: Linear Regression.]
 HSS.ID.B.6.C

Fit a linear function for a scatter plot that suggests a linear association. [See: Linear Regression.]
 HSS.ID.C.7

Interpret the slope (rate of change) and the intercept (constant term) of a linear model in the context of the data. [See: Linear Regression.]
 HSS.ID.C.8

Compute (using technology) and interpret the correlation coefficient of a linear fit. [See: Scatter Plots; Correlations; Linear Regression.]
 HSS.ID.C.9

Distinguish between correlation and causation. [See: Correlations; Linear Regression.]
Common Core ELA Standards
 SL.910.1

Initiate and participate effectively in a range of collaborative discussions (oneonone, in groups, and teacherled) with diverse partners on grades 910 topics, texts, and issues, building on others' ideas and expressing their own clearly and persuasively. [See: Introduction to Data Science.]
CSTA Standards
 1BAP10

Create programs that include sequences, events, loops, and conditionals. [See: Custom Scatter Plots; Method Chaining.]
 1BAP11

Decompose (break down) problems into smaller, manageable subproblems to facilitate the program development process. [See: Choosing Your Dataset.]
 1BAP12

Modify, remix, or incorporate portions of an existing program into one’s own work, to develop something new or add more advanced features. [See: Custom Scatter Plots.]
 1BAP15

Test and debug (identify and fix errors) a program or algorithm to ensure it runs as intended. [See: Functions Make Life Easier!; Checking Your Work.]
 1BDA06

Organize and present collected data visually to highlight relationships and support a claim. [See: Visualizing the “Shape” of Data; Box Plots; Standard Deviation; Scatter Plots; Correlations; Linear Regression.]
 1BDA07

Use data to highlight or propose causeandeffect relationships, predict outcomes, or communicate an idea. [See: Scatter Plots; Linear Regression.]
 2AP11

Create clearly named variables that represent different data types and perform operations on their values. [See: Simple Data Types; Functions Make Life Easier!; Custom Scatter Plots; Grouped Samples.]
 2AP13

Decompose problems and subproblems into parts to facilitate the design, implementation, and review of programs [See: Method Chaining; Defining Table Functions.]
 2AP14

Create procedures with parameters to organize code and make it easier to reuse. [See: Functions Make Life Easier!; Defining Table Functions.]
 2AP17

Systematically test and refine programs using a range of test cases [See: Functions Make Life Easier!; Custom Scatter Plots; Method Chaining; Defining Table Functions; Checking Your Work.]
 2AP19

Document programs in order to make them easier to follow, test, and debug. [See: Functions Make Life Easier!; Custom Scatter Plots.]
 2DA08

Collect data using computational tools and transform the data to make it more useful and reliable. [See: Bar and Pie Charts; Probability, Inference, and Sample Size; Custom Scatter Plots; Table Methods; Grouped Samples.]
 2DA09

Refine computational models based on the data they have generated. [See: Probability, Inference, and Sample Size; Scatter Plots; Grouped Samples; Correlations.]
 2IC21

Discuss issues of bias and accessibility in the design of existing technologies [See: Threats to Validity.]
 2IC23

Describe tradeoffs between allowing information to be public and keeping information private and secure. [See: Ethics, Privacy, and Bias.]
 3AAP16

Design and iteratively develop computational artifacts for practical intent, personal expression, or to address a societal issue by using events to initiate instructions. [See: Choosing Your Dataset; Ethics, Privacy, and Bias.]
 3AAP17

Decompose problems into smaller components through systematic analysis, using constructs such as procedures, modules, and/or objects. [See: Choosing Your Dataset; Method Chaining; Defining Table Functions.]
 3AAP18

Create artifacts by using procedures within a program, combinations of data and procedures, or independent but interrelated programs. [See: Method Chaining; Defining Table Functions.]
 3AAP23

Document design decisions using text, graphics, presentations, and/or demonstrations in the development of complex programs. [See: Choosing Your Dataset.]
 3ADA11

Create interactive data visualizations using software tools to help others better understand realworld phenomena. [See: Bar and Pie Charts; Choosing Your Dataset; Histograms; Visualizing the “Shape” of Data; Box Plots; Standard Deviation; Scatter Plots; Linear Regression.]
 3ADA12

Create computational models that represent the relationships among different elements of data collected from a phenomenon or process. [See: Scatter Plots; Linear Regression.]
 3AIC24

Evaluate the ways computing impacts personal, ethical, social, economic, and cultural practices [See: Ethics, Privacy, and Bias.]
 3AIC29

Explain the privacy concerns related to the collection and generation of data through automated processes that may not be evident to users. [See: Ethics, Privacy, and Bias.]
 3AIC30

Evaluate the social and economic implications of privacy in the context of safety, law, or ethics. [See: Ethics, Privacy, and Bias.]
 3BAP14

Construct solutions to problems using studentcreated components, such as procedures, modules and/or objects. [See: Choosing Your Dataset; Histograms; Visualizing the “Shape” of Data; Functions Make Life Easier!; Custom Scatter Plots.]
 3BAP21

Develop and use a series of test cases to verify that a program performs according to its design specifications. [See: Functions Make Life Easier!; Checking Your Work.]
 3BNI05

Use data analysis tools and techniques to identify patterns in data representing complex systems [See: Scatter Plots; Custom Scatter Plots; Correlations; Linear Regression.]
 3BNI07

Evaluate the ability of models and simulations to test and support the refinement of hypotheses. [See: Correlations; Threats to Validity.]
K12CS Standards
 68.Algorithms and Programming.Control

Programmers select and combine control structures, such as loops, event handlers, and conditionals, to create more complex program behavior. [See: Method Chaining.]
 68.Algorithms and Programming.Modularity

Programs use procedures to organize code, hide implementation details, and make code easier to reuse. Procedures can be repurposed in new programs. Defining parameters for procedures can generalize behavior and increase reusability. [See: Functions Make Life Easier!; Defining Table Functions.]
 68.Algorithms and Programming.Variables

Programmers create variables to store data values of selected types. A meaningful identifier is assigned to each variable to access and perform operations on the value by name. Variables enable the flexibility to represent different situations, process different sets of data, and produce varying outputs. [See: Functions Make Life Easier!.]
 68.Computing Systems.Troubleshooting

Comprehensive troubleshooting requires knowledge of how computing devices and components work and interact. A systematic process will identify the source of a problem, whether within a device or in a larger system of connected devices. [See: Checking Your Work.]
 68.Data and Analysis.Collection

People design algorithms and tools to automate the collection of data by computers. When data collection is automated, data is sampled and converted into a form that a computer can process. For example, data from an analog sensor must be converted into a digital form. The method used to automate data collection is influenced by the availability of tools and the intended use of the data. [See: Collecting Data; Threats to Validity.]
 68.Data and Analysis.Inference and Models

People transform, generalize, simplify, and present large data sets in different ways to influence how other people interpret and understand the underlying information. Examples include visualization, aggregation, rearrangement, and application of mathematical operations. [See: Choosing Your Dataset; Measures of Center; Box Plots; Standard Deviation; Custom Scatter Plots.]
 68.Data and Analysis.Visualization and Transformation

Computer models can be used to simulate events, examine theories and inferences, or make predictions with either few or millions of data points. Computer models are abstractions that represent phenomena and use data and algorithms to emphasize key features and relationships within a system. As more data is automatically collected, models can be refined. [See: Scatter Plots; Correlations.]
 912.Algorithms and Programming.Control

Programmers consider tradeoffs related to implementation, readability, and program performance when selecting and combining control structures. [See: Custom Scatter Plots; Method Chaining.]
 912.Algorithms and Programming.Modularity

Complex programs are designed as systems of interacting modules, each with a specific role, coordinating for a common overall purpose. These modules can be procedures within a program; combinations of data and procedures; or independent, but interrelated, programs. Modules allow for better management of complex tasks. [See: Functions Make Life Easier!; Method Chaining; Defining Table Functions.]
 912.Computing Systems.Troubleshooting

Troubleshooting complex problems involves the use of multiple sources when researching, evaluating, and implementing potential solutions. Troubleshooting also relies on experience, such as when people recognize that a problem is similar to one they have seen before or adapt solutions that have worked in the past. [See: Checking Your Work.]
 912.Data and Analysis.Collection

Data is constantly collected or generated through automated processes that are not always evident, raising privacy concerns. The different collection methods and tools that are used influence the amount and quality of the data that is observed and recorded. [See: Ethics, Privacy, and Bias; Collecting Data.]
 912.Data and Analysis.Inference and Models

The accuracy of predictions or inferences depends upon the limitations of the computer model and the data the model is built upon. The amount, quality, and diversity of data and the features chosen can affect the quality of a model and ability to understand a system. Predictions or inferences are tested to validate models. [See: Linear Regression; Threats to Validity.]
 912.Data and Analysis.Visualization and Transformation

Data can be transformed to remove errors, highlight or expose relationships, and/or make it easier for computers to process. [See: Choosing Your Dataset; Visualizing the “Shape” of Data; Box Plots; Standard Deviation; Scatter Plots.]
 912.Impacts of Computing.Culture

The design and use of computing technologies and artifacts can improve, worsen, or maintain inequitable access to information and opportunities. [See: Ethics, Privacy, and Bias.]
 912.Impacts of Computing.Safety, Law, and Ethics

Laws govern many aspects of computing, such as privacy, data, property, information, and identity. These laws can have beneficial and harmful effects, such as expediting or delaying advancements in computing and protecting or infringing upon people’s rights. International differences in laws and ethics have implications for computing. [See: Ethics, Privacy, and Bias.]
 912.Impacts of Computing.Social Interactions

Many aspects of society, especially careers, have been affected by the degree of communication afforded by computing. The increased connectivity between people in different cultures and in different career fields has changed the nature and content of many careers. [See: Computing Needs All Voices.]
Oklahoma Standards
 OK.3.AP.C.01

Create programs using a programming language that utilize sequencing, repetition, conditionals, and variables to solve a problem or express ideas both independently and collaboratively. [See: Method Chaining; Grouped Samples.]
 OK.3.AP.PD.01

Use an iterative process to plan the development of a program while solving simple problems. [See: Functions Make Life Easier!; Solving Word Problems with the Design Recipe; Defining Table Functions.]
 OK.3.AP.PD.04

Communicate and explain your program development using comments, presentations and demonstrations. [See: Solving Word Problems with the Design Recipe.]
 OK.3.DA.CVT.01

Collect and organize data in various visual formats. [See: Collecting Data.]
 OK.3.DA.IM.01

With guidance, utilize data to make predictions and discuss whether there is adequate data to make reliable predictions. [See: Linear Regression; Threats to Validity.]
 OK.4.AP.A.01

Compare and refine multiple algorithms for the same task. [See: Method Chaining.]
 OK.4.AP.M.01

Decompose large problems into smaller, manageable subproblems to facilitate the program development process. [See: Method Chaining.]
 OK.4.AP.PD.03

Analyze, create, and debug a program that includes sequencing, repetition, conditionals and variables in a programming language. [See: Method Chaining.]
 OK.4.AP.PD.04

Communicate and explain your program development using comments, presentations and demonstrations. [See: Solving Word Problems with the Design Recipe.]
 OK.4.DA.CVT.01

Organize and present collected data visually to highlight comparisons. [See: Collecting Data.]
 OK.4.DA.IM.01

Determine how the accuracy of conclusions are influenced by the amount of data collected. [See: Linear Regression.]
 OK.5.AP.M.01

Decompose large problems into smaller, manageable subproblems and then into a precise sequence of instructions. [See: Method Chaining.]
 OK.5.AP.PD.03

Analyze, create, and debug a program that includes sequencing, repetition, conditionals and variables in a programming language. [See: Method Chaining.]
 OK.5.AP.PD.04

Communicate and explain your program development using comments, presentations and demonstrations. [See: Solving Word Problems with the Design Recipe.]
 OK.5.DA.CVT.01

Organize and present collected data to highlight comparisons and support a claim. [See: Collecting Data.]
 OK.5.DA.IM.01

Use data to highlight or propose cause and effect relationships, predict outcomes, or communicate an idea. [See: Introduction to Data Science.]
 OK.5.GM.1.1

Describe, classify and construct triangles, including equilateral, right, scalene, and isosceles triangles. Recognize triangles in various contexts. [See: Contracts.]
 OK.6.A.1.3

Use and evaluate variables in expressions, equations, and inequalities that arise from various contexts, including determining when or if, for a given value of the variable, an equation or inequality involving a variable is true or false. [See: Simple Data Types.]
 OK.6.AP.A.01

Use an existing algorithm in natural language or pseudocode to solve complex problems. [See: Solving Word Problems with the Design Recipe.]
 OK.6.AP.PD.04

Break down tasks and follow an individual timeline when developing a computational artifact. [See: Probability, Inference, and Sample Size.]
 OK.6.AP.PD.05

Document textbased programs in order to make them easier to follow, test, and debug. [See: Solving Word Problems with the Design Recipe.]
 OK.6.D.1.3

Create and analyze box and whisker plots observing how each segment contains one quarter of the data. [See: Bar and Pie Charts; Choosing Your Dataset; Histograms; Visualizing the “Shape” of Data; Box Plots; Standard Deviation; Grouped Samples.]
 OK.6.DA.CVT.01

Collect data using computational tools and transform the data to make it more useful. [See: Box Plots; Standard Deviation.]
 OK.6.DA.S.01

Identify how the same data can be represented in multiple ways. [See: Bar and Pie Charts.]
 OK.6.GM.2.2

Develop and use the fact that the sum of the interior angles of a triangle is 180° to determine missing angle measures in a triangle. [See: Contracts.]
 OK.6.IC.C.01

Explain how computing impacts people’s everyday activities. [See: Computing Needs All Voices.]
 OK.7.AP.A.01

Select and modify an existing algorithm in natural language or pseudocode to solve complex problems. [See: Simple Data Types; Table Methods; Solving Word Problems with the Design Recipe.]
 OK.7.AP.M.01

Decompose problems into parts to facilitate the design, implementation, and review of increasingly complex programs. [See: Method Chaining.]
 OK.7.AP.PD.04

Distribute tasks and maintain a project timeline when collaboratively developing computational artifacts. [See: Probability, Inference, and Sample Size.]
 OK.7.AP.PD.05

Document textbased programs of increasing complexity in order to make them easier to follow, test, and debug. [See: Solving Word Problems with the Design Recipe.]
 OK.7.D.1.2

Use reasoning with proportions to display and interpret data in circle graphs (pie charts) and histograms. Choose the appropriate data display and know how to create the display using a spreadsheet or other graphing technology. [See: Bar and Pie Charts; Choosing Your Dataset; Histograms; Visualizing the “Shape” of Data; Box Plots; Standard Deviation; Grouped Samples.]
 OK.7.DA.CVT.01

Collect data using computational tools and transform the data to make it more useful and reliable. [See: Box Plots; Standard Deviation.]
 OK.7.DA.S.01

Create multiple representations of data. [See: Bar and Pie Charts.]
 OK.7.N.1.1

Know that every rational number can be written as the ratio of two integers or as a terminating or repeating decimal. [See: Simple Data Types.]
 OK.7.N.1.2

Compare and order rational numbers expressed in various forms using the symbols <, >, and =. [See: Simple Data Types.]
 OK.7.N.1.3

Recognize and generate equivalent representations of rational numbers, including equivalent fractions. [See: Simple Data Types.]
 OK.8.AP.A.01

Design algorithms in natural language, flow and control diagrams, comments within code, and/or pseudocode to solve complex problems. [See: Solving Word Problems with the Design Recipe.]
 OK.8.AP.C.01

Develop programs that utilize combinations of nested repetition, compound conditionals, procedures without parameters, and the manipulation of variables representing different data types. [See: Simple Data Types.]
 OK.8.AP.M.01

Decompose problems and subproblems into parts to facilitate the design, implementation, and review of complex programs. [See: Method Chaining.]
 OK.8.AP.PD.02

Incorporate existing code, media, and libraries into original programs of increasing complexity and give attribution. [See: Functions Make Life Easier!.]
 OK.8.AP.PD.04

Explain how effective communication between participants is required for successful collaboration when developing computational artifacts. [See: Solving Word Problems with the Design Recipe.]
 OK.8.AP.PD.05

Document textbased programs of increasing complexity in order to make them easier to follow, test, and debug. [See: Solving Word Problems with the Design Recipe.]
 OK.8.DA.CVT.01

Develop, implement, and refine a process that utilizes computational tools to collect and transform data to make it more useful and reliable. [See: Introduction to Data Science; Grouped Samples.]
 OK.8.DA.S.01

Analyze multiple methods of representing data and choose the most appropriate method for representing data. [See: Bar and Pie Charts; Choosing Your Dataset; Histograms; Visualizing the “Shape” of Data; Box Plots; Standard Deviation; Grouped Samples.]
 OK.8.IC.SI.01

Communicate and publish key ideas and details individually or collaboratively in a way that informs, persuades, and/or entertains using a variety of digital tools and mediarich resources. Describe and use safe, appropriate, and responsible practices (netiquette) when participating in online communities (e.g., discussion groups, blogs, social networking sites). [See: Choosing Your Dataset; Threats to Validity.]
 OK.A1.A.1.1

Use knowledge of solving equations with rational values to represent and solve mathematical and realworld problems (e.g., angle measures, geometric formulas, science, or statistics) and interpret the solutions in the original context. [See: Functions Make Life Easier!.]
 OK.A1.D.1.1

Describe a data set using data displays, describe and compare data sets using summary statistics, including measures of central tendency, location, and spread. Know how to use calculators, spreadsheets, or other appropriate technology to display data and calculate summary statistics. [See: Choosing Your Dataset; Histograms; Visualizing the “Shape” of Data; Grouped Samples.]
 OK.A1.D.1.2

Collect data and use scatterplots to analyze patterns and describe linear relationships between two variables. Using graphing technology, determine regression lines and correlation coefficients; use regression lines to make predictions and correlation coefficients to assess the reliability of those predictions. [See: Collecting Data.]
 OK.A1.D.2.1

Select and apply counting procedures, such as the multiplication and addition principles and tree diagrams, to determine the size of a sample space (the number of possible outcomes) and to calculate probabilities. [See: Table Methods; Method Chaining; Defining Table Functions.]
 OK.A1.F.1.2

Identify the dependent and independent variables as well as the domain and range given a function, equation, or graph. Identify restrictions on the domain and range in realworld contexts. [See: Contracts.]
 OK.A1.F.1.3

Write linear functions, using function notation, to model realworld and mathematical situations. [See: Contracts; Functions Make Life Easier!.]
 OK.A1.F.1.4

Given a graph modeling a realworld situation, read and interpret the linear piecewise function (excluding step functions). [See: Contracts; Custom Scatter Plots.]
 OK.A2.F.1.8

Graph piecewise functions with no more than three branches (including linear, quadratic, or exponential branches) and analyze the function by identifying the domain, range, intercepts, and intervals for which it is increasing, decreasing, and constant. [See: Custom Scatter Plots.]
 OK.G.2D.1.8

Construct logical arguments to prove triangle congruence (SSS, SAS, ASA, AAS and HL) and triangle similarity (AA, SSS, SAS). [See: Contracts.]
 OK.L1.AP.A.01

Create a prototype that uses algorithms (e.g., searching, sorting, finding shortest distance) to provide a possible solution for a realworld problem. [See: Grouped Samples.]
 OK.L1.AP.M.01

Break down a solution into procedures using systematic analysis and design. [See: Custom Scatter Plots; Method Chaining; Defining Table Functions.]
 OK.L1.AP.M.02

Create computational artifacts by systematically organizing, manipulating and/or processing data. [See: Custom Scatter Plots; Table Methods; Method Chaining; Defining Table Functions.]
 OK.L1.AP.PD.05

Evaluate and refine computational artifacts to make them more userfriendly, efficient and/or accessible. [See: Histograms; Visualizing the “Shape” of Data.]
 OK.L1.DA.CVT.01

Use tools and techniques to locate, collect, and create visualizations of small and largescale data sets (e.g., paper surveys and online data sets). [See: Choosing Your Dataset.]
 OK.L1.DA.IM.01

Show the relationships between collected data elements using computational models. [See: Scatter Plots; Correlations; Linear Regression.]
 OK.L1.IC.C.01

Evaluate the ways computing impacts personal, ethical, social, economic, and cultural practices. [See: Ethics, Privacy, and Bias.]
 OK.L1.IC.C.02

Test and refine computational artifacts to reduce bias and equity deficits. [See: Probability, Inference, and Sample Size; Choosing Your Dataset; Grouped Samples; Checking Your Work; Threats to Validity.]
 OK.L1.IC.SLE.02

Explain the privacy concerns related to the largescale collection and analysis of information about individuals (e.g., how businesses, social media, and the government collects and uses data) that may not be evident to users. [See: Ethics, Privacy, and Bias.]
 OK.L1.IC.SLE.03

Evaluate the social and economic consequences of how law and ethics interact with digital aspects of privacy, data, property, information, and identity. [See: Ethics, Privacy, and Bias.]
 OK.L2.AP.M.03

Create programming solutions by reusing existing code (e.g., libraries, Application Programming Interface (APIs), code repositories). [See: Table Methods.]
 OK.L2.AP.PD.03

Develop programs for multiple computing platforms. [See: Functions Make Life Easier!.]
 OK.L2.AP.PD.05

Develop and use a series of test cases to verify that a program performs according to its design specifications. [See: Solving Word Problems with the Design Recipe.]
 OK.L2.DA.CVT.01

Use data analysis tools and techniques to identify patterns from complex realworld data. [See: Linear Regression.]
 OK.L2.DA.CVT.02

Generate data sets that use a variety of data collection tools and analysis techniques to support a claim and/or communicate information. [See: Box Plots; Standard Deviation.]
 OK.L2.IC.C.01

Evaluate the beneficial and harmful effects that computational artifacts and innovations have on society. [See: Ethics, Privacy, and Bias.]
 OK.L2.IC.SLE.01

Debate laws and regulations that impact the development and use of software. [See: Ethics, Privacy, and Bias.]
 OK.MAP.5

Develop a productive mathematical disposition. [See: Functions Make Life Easier!.]
 OK.MAP.6

Develop the ability to make conjectures, model, and generalize. [See: Functions Make Life Easier!.]
 OK.MAP.7

Develop the ability to communicate mathematically. [See: Solving Word Problems with the Design Recipe; Linear Regression.]
 OK.PA.A.1.1

Recognize that a function is a relationship between an independent variable and a dependent variable in which the value of the independent variable determines the value of the dependent variable. [See: Contracts; Functions Make Life Easier!.]
 OK.PA.A.1.2

Use linear functions to represent and explain realworld and mathematical situations. [See: Functions Make Life Easier!; Solving Word Problems with the Design Recipe.]
 OK.PA.A.1.3

Identify a function as linear if it can be expressed in the form y = mx + b or if its graph is a straight line. [See: Solving Word Problems with the Design Recipe.]
 OK.PA.A.2

Recognize linear functions in realworld and mathematical situations; represent linear functions and other functions with tables, verbal descriptions, symbols, and graphs; solve problems involving linear functions and interpret results in the original context. [See: Solving Word Problems with the Design Recipe.]
 OK.PA.A.2.1

Represent linear functions with tables, verbal descriptions, symbols, and graphs; translate from one representation to another. [See: Solving Word Problems with the Design Recipe.]
 OK.PA.A.2.2

Identify, describe, and analyze linear relationships between two variables. [See: Probability, Inference, and Sample Size; Choosing Your Dataset; Histograms; Visualizing the “Shape” of Data; Grouped Samples.]
 OK.PA.D.1

Display and interpret data in a variety of ways, including using scatterplots and approximate lines of best fit. Use line of best fit and average rate of change to make predictions and draw conclusions about data. [See: Choosing Your Dataset.]
 OK.PA.D.1.1

Describe the impact that inserting or deleting a data point has on the mean and the median of a data set. Know how to create data displays using a spreadsheet and use a calculator to examine this impact. [See: Choosing Your Dataset; Histograms; Visualizing the “Shape” of Data; Grouped Samples.]
 OK.PA.D.1.2

Explain how outliers affect measures of central tendency. [See: Measures of Center.]
 OK.PA.D.1.3

Collect, display and interpret data using scatterplots. Use the shape of the scatterplot to informally estimate a line of best fit, make statements about average rate of change, and make predictions about values not in the original data set. Use appropriate titles, labels and units. [See: Scatter Plots; Correlations; Linear Regression.]
 OK.PA.D.2.2

Determine how samples are chosen (random, limited, biased) to draw and support conclusions about generalizing a sample to a population. [See: Probability, Inference, and Sample Size.]
Iowa Standards
 IA.HSF.IF.A.1

Understand that a function from one set (called the domain) to another set (called the range) assigns to each element of the domain exactly one element of the range. If f is a function and x is an element of its domain, then f(x) denotes the output of f corresponding to the input x. The graph of f is the graph of the equation y = f(x). [See: Contracts.]
Connected Math
 CMP.8.1

Thinking with Mathematical Models: Linear and Inverse Variations. [See: Measures of Center; Scatter Plots; Correlations; Linear Regression.]
 CMP.6.7

Data About Us: Statistics and Data Analysis. [See: Measures of Center; Box Plots.]
 CMP.7.8

Samples and Populations: Making Comparisons and Predictions. [See: Probability, Inference, and Sample Size.]
IM 8 Math™
 IM.8.6.6

The Slope of a Fitted Line. [See: Linear Regression.]
 IM.8.6.7

Observing More Patterns in Scatter Plots. [See: Linear Regression.]
 IM.8.6.8

Analyzing Bivariate Data. [See: Linear Regression.]
 IM.8.6.4

Fitting a Lin to Data. [See: Correlations.]
 IM.8.6.5

Describing Trends in Scatter Plots. [See: Correlations.]
 IM.8.5.3

Equations for Functions. [See: Defining Table Functions.]
 IM.8.6.3

What a Point in a Scatter Plots Means. [See: Scatter Plots.]
 IM.8.6.2

Plotting Data. [See: Bar and Pie Charts; Histograms; Scatter Plots.]
 IM.8.6.1

Organizing Data. [See: Bar and Pie Charts; Visualizing the “Shape” of Data.]
 IM.8.5.1

Inputs and Outputs. [See: Contracts.]
 IM.8.5.2

Introduction to Functions. [See: Contracts.]
IM 7 Math™
 IM.7.8.19

Comparing Populations with Friends. [See: Threats to Validity.]
 IM.7.8.18

Comparing Populations Using Samples. [See: Grouped Samples.]
 IM.7.8.15

Estimating Populations Measures of Center. [See: Box Plots.]
 IM.7.8.11

Comparing Groups. [See: Measures of Center; Grouped Samples.]
 IM.7.8.13

What Makes a Good Sample?. [See: Measures of Center.]
 IM.7.8.12

Larger Populations. [See: Probability, Inference, and Sample Size.]
 IM.7.8.14

Sampling in a Fair Way. [See: Probability, Inference, and Sample Size; Threats to Validity.]
 IM.7.8.17

More about Sampling Variability. [See: Probability, Inference, and Sample Size; Threats to Validity.]
 IM.7.2.4

Proportional Relationships and Equations. [See: Contracts.]
 IM.7.2.5

Two Equations for Each Relationship. [See: Contracts.]
 IM.7.2.6

Using Equations to Solve Problems. [See: Contracts.]
 IM.7.6.13

Reintroducing Inequalities. [See: Simple Data Types.]
IM Algebra 1
 IM.Alg1.3.9

Causal Relationships. [See: Linear Regression; Threats to Validity.]
 IM.Alg1.3.5

Fitting Lines. [See: Correlations.]
 IM.Alg1.3.7

The Correlation Coefficient. [See: Correlations; Linear Regression.]
 IM.Alg1.3.8

Using the Correlation Coefficient. [See: Correlations; Linear Regression.]
 IM.Alg1.4.10

Domain and Range (Part 1). [See: Defining Table Functions.]
 IM.Alg1.3.4

Linear Models. [See: Scatter Plots.]
 IM.Alg1.1.15

Comparing Data Sets. [See: Box Plots; Checking Your Work.]
 IM.Alg1.1.5

Calculating Measures of Center and Variability. [See: Measures of Center.]
 IM.Alg1.1.11

Comparing and Contrasting Data Distributions. [See: Measures of Center; Box Plots.]
 IM.Alg1.1.9

Technological Graphing. [See: Choosing Your Dataset.]
 IM.Alg1.3.10

Fossils and Flags. [See: Choosing Your Dataset.]
 IM.Alg1.1.2

Data Representations. [See: Bar and Pie Charts; Histograms.]
 IM.Alg1.1.3

A Gallery of Data. [See: Bar and Pie Charts; Histograms.]
 IM.Alg1.4.2

Function Notation. [See: Contracts.]
 IM.Alg1.4.3

Interpreting & Using Function Notation. [See: Contracts; Method Chaining.]
 IM.Alg1.1.6

Mystery Computations. [See: Simple Data Types.]
 IM.Alg1.1.1

Getting to Know You. [See: Introduction to Data Science.]
IM 6 Math™
 IM.6.8.15

Quartiles and Interquartile Range. [See: Box Plots.]
 IM.6.8.16

Box Plots. [See: Box Plots.]
 IM.6.8.17

Using Box Plots. [See: Box Plots.]
 IM.6.8.9

Interpreting the Mean as Fair Share. [See: Measures of Center.]
 IM.6.8.10

Interpreting the Mean as the Balance Point. [See: Measures of Center.]
 IM.6.8.13

The Median of a Data Set. [See: Measures of Center.]
 IM.6.8.14

Comparing Mean and Median. [See: Measures of Center.]
 IM.6.8.6

Histograms. [See: Histograms; Visualizing the “Shape” of Data.]
 IM.6.8.7

Using Histograms to Answer Statistical Questions. [See: Histograms; Visualizing the “Shape” of Data.]
 IM.6.8.8

Describing Distributions on Histograms. [See: Histograms; Visualizing the “Shape” of Data.]
 IM.6.6.16

Two Related Quantities, Part 1. [See: Contracts.]
 IM.6.6.17

Two Related Quantities, Part 2. [See: Contracts.]
 IM.6.6.18

More Relationships. [See: Contracts.]
 IM.6.7.9

Solutions of Inequalities. [See: Simple Data Types.]
 IM.6.7.10

Interpreting Inequalities. [See: Simple Data Types.]
 IM.6.8.1

Got Data?. [See: Introduction to Data Science.]
 IM.6.8.2

Statistical Questions. [See: Introduction to Data Science.]
Science and Engineering
 SEP.4

Analyzing and Interpreting Data. [See: The Data Cycle.]
 SEP.7

Engaging in Argument from Evidence. [See: The Data Cycle.]
 SEP.3

Planning and Carrying Out Investigations. [See: Bar and Pie Charts; Probability, Inference, and Sample Size; Choosing Your Dataset; Histograms; Visualizing the “Shape” of Data; Measures of Center; Box Plots; Scatter Plots; Grouped Samples; Correlations; Linear Regression; Checking Your Work; Threats to Validity.]
 SEP.8

Obtaining, Evaluating, and Communicating Information. [See: Contracts; The Data Cycle; Collecting Data.]
Math Lang. Routines
 MLR.6

Three Reads. [See: Solving Word Problems with the Design Recipe.]
 MLR.3

Clarify, Critique and Correct. [See: Collecting Data.]
 MLR.5

CoCraft Questions and Problems. [See: Collecting Data.]
 MLR.7

Compare and Connect. [See: Box Plots; Solving Word Problems with the Design Recipe; Correlations.]
 MLR.1

Stronger and Clearer Each Time. [See: Probability, Inference, and Sample Size; Solving Word Problems with the Design Recipe.]
 MLR.2

Collect and Display. [See: Contracts.]
 MLR.8

Discussion Supports. [See: Contracts; Method Chaining; Linear Regression.]
 MLR.4

Information Gap. [See: Simple Data Types.]
Math
 MP.1

Make sense of problems and persevere in solving them. [See: Solving Word Problems with the Design Recipe.]
 MP.8

Look for and express regularity in repeated reasoning. [See: Functions Make Life Easier!; Functions: Contracts, Examples & Definitions; Defining Table Functions.]
 MP.7

Look for and make use of structure. [See: Row and Column Lookups; Functions Make Life Easier!; Functions: Contracts, Examples & Definitions; Custom Scatter Plots; Solving Word Problems with the Design Recipe.]
 MP.4

Model with mathematics. [See: Histograms; Visualizing the “Shape” of Data; Box Plots; Standard Deviation; Table Methods; Solving Word Problems with the Design Recipe; Correlations; Linear Regression.]
 MP.2

Reason abstractly and quantitatively. [See: Probability, Inference, and Sample Size; Choosing Your Dataset; Scatter Plots; Grouped Samples.]
 MP.5

Use appropriate tools strategically. [See: Bar and Pie Charts; The Data Cycle; Choosing Your Dataset; Collecting Data.]
 MP.6

Attend to precision. [See: Simple Data Types; Bar and Pie Charts; The Data Cycle; Functions Make Life Easier!; Functions: Contracts, Examples & Definitions; Method Chaining; Defining Table Functions.]
 MP.3

Construct viable arguments and critique the reasoning of others. [See: Introduction to Data Science; Measures of Center; Box Plots; Standard Deviation; Ethics, Privacy, and Bias; Collecting Data; Solving Word Problems with the Design Recipe; Grouped Samples; Correlations; Checking Your Work; Threats to Validity.]
K12CS
 P4

Developing and Using Abstractions. [See: Functions Make Life Easier!; Defining Table Functions.]
 P6

Testing and Refining Computational Artifacts. [See: Collecting Data; Checking Your Work.]
 P2

Collaborating Around Computing. [See: Collecting Data.]
 P5

Creating Computational Artifacts. [See: Standard Deviation.]
 P3

Recognizing and Defining Computational Problems. [See: The Data Cycle; Method Chaining; Grouped Samples.]
 P7

Communicating About Computing. [See: Introduction to Data Science; Choosing Your Dataset.]
 P1

Fostering an Inclusive Computing Culture. [See: Computing Needs All Voices; Ethics, Privacy, and Bias; Threats to Validity.]
Social Justice
 SJ.15

Students will identify figures, groups, events and a variety of strategies and philosophies relevant to the history of social justice around the world.. [See: Checking Your Work.]
 SJ.14

Students will recognize that power and privilege influence relationships on interpersonal, intergroup and institutional levels and consider how they have been affected by those dynamics.. [See: Collecting Data; Checking Your Work.]
 SJ.12

Students will recognize unfairness on the individual level (e.g., biased speech) and injustice at the institutional or systemic level (e.g., discrimination).. [See: Ethics, Privacy, and Bias; Checking Your Work.]
 SJ.13

Students will analyze the harmful impact of bias and injustice on the world, historically and today. [See: Probability, Inference, and Sample Size; Ethics, Privacy, and Bias; Checking Your Work.]
 SJ.10

Students will examine diversity in social, cultural, political and historical contexts rather than in ways that are superficial or oversimplified.. [See: Computing Needs All Voices.]
 SJ.8

Students will respectfully express curiosity about the history and lived experiences of others and will exchange ideas and beliefs in an openminded way. [See: Computing Needs All Voices.]
 SJ.7

Students will develop language and knowledge to accurately and respectfully describe how people (including themselves) are both similar to and different from each other and others in their identity groups.. [See: Computing Needs All Voices; Bar and Pie Charts.]
 SJ.4

Students will express pride, confidence and healthy selfesteem without denying the value and dignity of other people.. [See: Computing Needs All Voices; Bar and Pie Charts.]
 SJ.2

Students will develop language and historical and cultural knowledge that affirm and accurately describe their membership in multiple identity groups. [See: Computing Needs All Voices; Bar and Pie Charts.]
 SJ.1

Students will develop positive social identities based on their membership in multiple groups in society. [See: Computing Needs All Voices.]