Recognising A Statistical Question

Assessing Function Fit Via Residual Analysis

Fit & Use Functions To Solve Data Problems

Fit Linear Function For Scatter Plot

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

Calculating And Interpreting Relative Frequencies

Describing Possible Associations In Data

Interpreting Two-way Tables For Categorical Variables

Investigating Patterns In Scatter Plots

Drawing Scatter Plots

Fitting A Straight Line And Assessing Its Fit (informally)

Interpreting Scatter Plots

Assessing Function Fit Via Residual Analysis

Fit & Use Functions To Solve Data Problems

Fit Linear Function For Scatter Plot

Representing Data Using Box-and-whisker Plots

Representing Data Using Circle Graphs (pie Charts)

Representing Data Using Dot Plots

Representing Data Using Histograms

Representing Data Using Box-and-whisker Plots

Representing Data Using Dot Plots

Representing Data Using Histograms

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

Recognising A Statistical Question

Choosing And Interpreting The Scale In Graphs And Data Displays

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

Calculating Interquartile Range And Mean Absolute Deviation

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

Choosing And Interpreting The Scale In Graphs And Data Displays

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

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

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

Choosing And Interpreting The Scale In Graphs And Data Displays

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

Calculating Interquartile Range And Mean Absolute Deviation

Calculate Expected Payoff In Chance Game

Comparing Strategies Via Expected Values

Evaluating And Comparing Strategies Based On Expected Values

Finding Expected Payoffs For Games Of Chance

Using Probability Concepts For Decision-making

Designing Simulations For Compound Events

Representing Sample Spaces For Compound Events

Understanding Probability Of Compound Events

Developing & Using Uniform Probability Models

Developing Non-uniform Probability Models

Describing Events In Sample Space Using Set Theory

Developing And Using Non-uniform Probability Models By Observing Frequencies In Data

Developing And Using Uniform Probability Models (equally Likely Outcomes)

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

Estimating The Probability Of A Chance Event By Collecting Data (including Observing Long-run Relative Frequencies)

Identifying Outcomes In The Sample Space For Simple Events

Calculating Theoretical Probabilities In The Context Of Area Models

Calculate Expected Payoff In Chance Game

Comparing Strategies Via Expected Values

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).

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

Calculating And Interpreting Expected Values

Developing Probability Distributions For Random Variables

Calculate Expected Payoff In Chance Game

Comparing Strategies Via Expected Values

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).

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

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

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

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

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

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

Recognising A Statistical Question

Recognising A Statistical Question

Fitting Data To Normal Distributions Using Mean And Standard Deviation

Recognising Limitations Of Normal Distribution Models

Using Tables/technology For Normal Curve Calculations

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