Assessing Function Fit Via Residual Analysis

Fit & Use Functions To Solve Data Problems

Fit Linear Function For Scatter Plot

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

Describing Events In Sample Space Using Set Theory

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 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).

Recognising A Statistical Question