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

Assessing Function Fit Via Residual Analysis

Fit & Use Functions To Solve Data Problems

Fit Linear Function For Scatter Plot

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

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

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

Calculating And Interpreting Expected Values

Developing Probability Distributions For Random Variables

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

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