Statistical Models
Residual Plots {TI-84 Plus CE}

Statistical Models

Bivariate Statistics

• The statistical investigation process
• Association between variables
• The effects of outliers on correlation
• Causality
• Interpreting Pearson's correlation coefficient
• Calculating linear regression {TI-84 Plus CE}
• Interpreting values of linear regression
• Residual plots {TI-84 Plus CE}
• Calculating exponential regression {TI-84 Plus CE}
• Interpreting values of exponential regression
• ​Interpolation and extrapolation​

The Normal Distribution

• Properties of the bell shape curve
• Finding integral and non-integral probabilities {TI-84 Plus CE}
• Finding quantiles {TI-84 Plus CE}​

Residual plots can be used to determine whether a particular model of correlation is the best model to fit the data. In general, a residual plot with points randomly scattered about the x-axis indicates that model chosen is the best fit for the data. A residual plot that shows a clear pattern indicates that the model chosen is not appropriate for the data.
​Large residual values may also indicate that the model chosen is not appropriate.