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Math

Algebra

Functions

Geometry

Mathematical Practices

Mathematics Connections

Measurement

Measurement And Data

Number System

Ratios And Proportional Relationships

Statistics And Probability

Basic Statistical Concepts

Understanding Statistical Variability (qualitative)

Recognising A Statistical Question

Basic Statistics

Statistical Questions

Understanding Statistical Variability (qualitative)

Recognising A Statistical Question

Basic Statistics And Visual Representations

Bivariate Statistics

Scatter Plots

Calculate & Interpret Correlation Coefficient

Correlation Vs Causation Distinction

Interpreting Frequencies & Trends In Data

Interpreting Slope & Intercept In Data Context

Scatter Plot Representation & Variable Relation

Assessing Function Fit Via Residual Analysis

Fit & Use Functions To Solve Data Problems

Fit Linear Function For Scatter Plot

Understanding And Applying Scatter Plots

Analyzing Residuals To Assess Model Fit

Distinguishing Between Correlation And Causation

Finding And Interpreting Correlation Coefficients

Fitting Lines And Functions To Data

Interpolating And Extrapolating With Linear Models

Using Fitted Functions To Solve Contextual Problems

Two-way Tables

Identifying And Interpreting Patterns Of Association

Calculating And Interpreting Relative Frequencies

Describing Possible Associations In Data

Interpreting Two-way Tables For Categorical Variables

Investigating Patterns In Scatter Plots

Bivariate Data

Linear Models

Applying The Fitted Straight Line To Identify Relationships And Solve Problems (including Interpreting Slopes And Intercepts)

Modeling & Assessing Linear Relationships

Solving Problems Using Linear Model Equations

Scatter Plots

Interpreting Scatter Plots & Data Patterns

Understanding And Applying Scatter Plots

Drawing Scatter Plots

Fitting A Straight Line And Assessing Its Fit (informally)

Interpreting Scatter Plots

Scatter Plots And Correlation

Calculate & Interpret Correlation Coefficient

Correlation Vs Causation Distinction

Interpreting Frequencies & Trends In Data

Interpreting Slope & Intercept In Data Context

Scatter Plot Representation & Variable Relation

Assessing Function Fit Via Residual Analysis

Fit & Use Functions To Solve Data Problems

Fit Linear Function For Scatter Plot

Understanding And Applying Scatter Plots

Two-way Tables

Identifying And Interpreting Patterns Of Association

Interpreting Associations In Bivariate Categorical Data

Data

Data Analysis

Interpreting Data

Data Representation

Organising And Representing Data

Representing Data Using Box-and-whisker Plots

Representing Data Using Circle Graphs (pie Charts)

Representing Data Using Dot Plots

Representing Data Using Histograms

Data Visualization

Organising And Representing Data

Representing Data Using Box-and-whisker Plots

Representing Data Using Dot Plots

Representing Data Using Histograms

Data Analysis

Analyzing Distributions

Analyzing Data Sets: Shape, Center, Spread & Outliers

Comparing Data Sets Using Statistics

Describing And Understanding Distributions

Comparing Two Or More Distributions In Terms Of Their Shapes, Centre And Spread

Describing Any Overall Pattern And Any Striking Deviations From The Overall Pattern With Reference To Context

Relating The Choice Of Measures Of Central Tendency To The Shape Of The Data Distribution And The Context

Relating The Choice Of Measures Of Variability To The Shape Of The Data Distribution And The Context

Basic Statistical Concepts

Understanding Statistical Variability (qualitative)

Recognising A Statistical Question

Data Representation

Data Representation On Real Number Line

Interpreting Data

Choosing And Interpreting The Scale In Graphs And Data Displays

Organising And Representing Data

Distinguishing Between Qualitative And Quantitative Data

Investigating And Describing Patterns Such As Clustering And Outliers

Representing Data Using Box-and-whisker Plots

Representing Data Using Dot Plots

Representing Data Using Histograms

Frequency Distribution

Analyzing Data Sets: Shape, Center, Spread & Outliers

Comparing Data Sets Using Statistics

Estimating Population Percentages Using Mean & Sd

Measures Of Variability

Estimating Measures Of Variability

Calculating Interquartile Range And Mean Absolute Deviation

Normal Distributions

Estimating Population Percentages Using Mean & Sd

Understanding And Applying Normal Distributions

Recognising That There Are Data Sets For Which Normalisation Cannot Be Done

Using Tables/technology To Estimate Areas Under The Normal Curve And Calculate Population Percentage.

Using The Mean And Standard Deviation Of A Data Set To Fit It To A Normal Distribution

Sampling And Inference

Data Displays

Interpreting Data

Analyzing Data Displays

Drawing Conclusions From Data

Representing Data

Creating Box Plots

Creating Dot Plots

Creating Histograms

Data Representation

Data Visualization

Data Representation On Real Number Line

Interpreting Data

Choosing And Interpreting The Scale In Graphs And Data Displays

Organising And Representing Data

Distinguishing Between Qualitative And Quantitative Data

Investigating And Describing Patterns Such As Clustering And Outliers

Representing Data Using Box-and-whisker Plots

Representing Data Using Dot Plots

Representing Data Using Histograms

Describing Distributions

Describing And Understanding Distributions

Comparing Two Or More Distributions In Terms Of Their Shapes, Centre And Spread

Describing Any Overall Pattern And Any Striking Deviations From The Overall Pattern With Reference To Context

Relating The Choice Of Measures Of Central Tendency To The Shape Of The Data Distribution And The Context

Relating The Choice Of Measures Of Variability To The Shape Of The Data Distribution And The Context

Understanding And Applying Normal Distributions

Recognising That There Are Data Sets For Which Normalisation Cannot Be Done

Using Tables/technology To Estimate Areas Under The Normal Curve And Calculate Population Percentage.

Using The Mean And Standard Deviation Of A Data Set To Fit It To A Normal Distribution

Interpreting Data Displays

Interpreting Data

Choosing And Interpreting The Scale In Graphs And Data Displays

Frequency Distributions

Describing Distributions

Describing And Understanding Distributions

Comparing Two Or More Distributions In Terms Of Their Shapes

Comparing Two Or More Distributions In Terms Of Their Shapes, Centre And Spread

Describing Any Overall Pattern And Any Striking Deviations From The Overall Pattern With Reference To Context

Relating The Choice Of Measures Of Central Tendency To The Shape Of The Data Distribution And The Context

Relating The Choice Of Measures Of Variability To The Shape Of The Data Distribution And The Context

Measures Of Variability

Estimating Measures Of Variability

Calculating Interquartile Range And Mean Absolute Deviation

Probability

Applying Probability

Analyzing Strategies Using Probability Concepts

Calculating Expected Values In Decision Making

Calculate Expected Payoff In Chance Game

Comparing Strategies Via Expected Values

Developing And Using Probability Models

Evaluating And Comparing Strategies Based On Expected Values

Finding Expected Payoffs For Games Of Chance

Using Probability Concepts For Decision-making

Finding Probabilities Of Compound Events

Designing Simulations For Compound Events

Representing Sample Spaces For Compound Events

Understanding Probability Of Compound Events

Predicting Probability & Relative Frequency

Probability Model Development & Analysis

Developing & Using Uniform Probability Models

Developing Non-uniform Probability Models

Basic Probability Concepts

Calculating Probability

Expected Value

Analyzing Strategies Using Probability Concepts

Calculating Expected Value & Interpreting Mean

Calculating Expected Values In Decision Making

Calculate Expected Payoff In Chance Game

Comparing Strategies Via Expected Values

Developing Probability Distribution & Expected Value

Empirical Probability Distribution & Expected Value

Understanding And Using Probability Distributions

Calculating And Interpreting The Expected Value Of A Random Variable

Developing A Probability Distribution For A Random Variable For Which Theoretical/experimental Probabilities Can Be Calculated (including Binomial Distribution).

Probability Applications

Developing And Using Probability Models

Evaluating And Comparing Strategies Based On Expected Values

Finding The Expected Payoff For A Game Of Chance

Using Probability Concepts To Analyse Strategies And Make Fair Decisions

Probability Concepts

Developing And Using Probability Models

Estimating Probabilities Of Compound Events

Probability Distributions

Calculating Expected Value & Interpreting Mean

Defining & Graphing Random Variables In Probability

Developing Probability Distribution & Expected Value

Empirical Probability Distribution & Expected Value

Understanding And Using Probability Distributions

Calculating And Interpreting Expected Values

Developing Probability Distributions For Random Variables

Random Variables And Expected Value

Understanding Probability

Developing An Intuitive Understanding Of Probability

Understanding That The Probability Of A Chance Event Lies Between Zero And One And Distinguishing Between Likely And Unlikely Events

Understanding Probability Of Chance Events

Sampling And Inference

Sampling Techniques

Using Random Sampling To Draw Inferences

Developing A Margin Of Error Through The Use Of Simulation Models

Explaining How Randomisation Relates To Sample Surveys, Experiments And Observational Studies

Recognising The Purposes Of And Differences Among Sample Surveys, Experiments And Observational Studies

Understanding Representative Samples And The Idea That Examining A Sample Can Lead To Useful Information About The Population

Using Data From A Sample Survey To Estimate A Population Mean Or Proportion

Statistical Estimation

Comparing Treatments & Parameters Via Data

Estimating Population Mean & Margin Of Error

Evaluating Data-based Reports

Statistical Inference Concepts

Assessing Model Consistency With Data Process

Understanding Inferences In Statistics

Understanding Surveys, Experiments & Randomization

Sampling And Population

Comparing Populations

Assessing Overlap Of Two Data Distributions

Comparing Data Variability & Center Measures

Drawing Inferences From Random Sample Data

Drawing Comparative Inferences About Two Populations Using Measures Of Central Tendency

Using The Measures Of Central Tendency Of Random Samples To Draw Comparative Inferences About Two Populations

Drawing Comparative Inferences About Two Populations Using Measures Of Variability

Using The Measures Of Variability Of Random Samples To Draw Comparative Inferences About Two Populations

Estimating Differences Between Populations Using Samples

Assessing The Degree Of Visual Overlap Of Two Numerical Data Distributions With Similar Variabilities

Estimating The Difference Between The Measures Of Central Tendency Of Random Samples

Expressing The Difference Between The Measures Of Central Tendency As A Multiple Of The Measures Of Variability

Random Sampling

Understanding Statistics & Representative Sampling

Using Random Sampling To Draw Inferences

Generating Multiple Samples Of The Same Size To Gauge Variation In Predictions

Understanding Representative Samples And The Idea That Examining A Sample Can Lead To Useful Information About The Population

Using Data From A Random Sample To Draw Inferences About A Population

Using Data From A Sample Survey To Estimate A Population Mean Or Proportion

Using Random Sampling To Draw Inferences

Generating Multiple Samples Of The Same Size To Gauge Variation In Predictions

Understanding Representative Samples And The Idea That Examining A Sample Can Lead To Useful Information About The Population

Using Data From A Sample Survey To Estimate A Population Mean Or Proportion

Statistical Analysis

Frequency Distribution

Analyzing Data Sets: Shape, Center, Spread & Outliers

Comparing Data Sets Using Statistics

Estimating Population Percentages Using Mean & Sd

Sampling And Statistical Inference

Statistical Foundations

Basic Statistics

Understanding Statistical Variability (qualitative)

Recognising A Statistical Question

Statistical Fundamentals

Basic Statistics

Understanding Statistical Variability (qualitative)

Recognising A Statistical Question

Statistical Variability

Understanding Data

Describing Data Distributions

Measures Of Center And Variation

Recognizing Statistical Questions

Statistics

Normal Distributions

Understanding And Applying Normal Distributions

Fitting Data To Normal Distributions Using Mean And Standard Deviation

Recognising Limitations Of Normal Distribution Models

Using Tables/technology For Normal Curve Calculations

Statistical Analysis

Analyzing Data Sets: Shape, Center, Spread & Outliers

Comparing Data Sets Using Statistics

Describing And Understanding Distributions

Comparing Distributions By Shape, Center, And Spread

Describing Patterns And Deviations In Data

Relating Measures Of Central Tendency To Distribution Shape

Relating Measures Of Variability To Distribution Shape

Estimating Population Percentages Using Mean & Sd

Statistical Inference

Assessing Model Consistency With Data Process

Comparing Treatments & Parameters Via Data

Estimating Population Mean & Margin Of Error

Evaluating Data-based Reports

Understanding Inferences In Statistics

Understanding Surveys, Experiments & Randomization

Using Random Sampling To Draw Inferences

Developing Margins Of Error Through Simulation

Estimating Population Parameters From Samples

Explaining Randomization Principles

Recognising Differences Among Sampling Methods

Understanding Representative Samples And Population Inference

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Data Analysis - Summary Statistics - Real World Applications

9th Grade - 12th Grade

Math

Explore real-world scenarios to efficiently interpret data using summary statistics. Calculate key metrics and identify trends for improved decision-making, focusing on the subject matter, goals, and learning process.

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1. 2021 survey of 800+ teachers conducted by Wayground

2. Journal of Education and e-Learning Research (Source)

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