Statistical Models
Interpreting values of linear regression

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}​

If a linear correlation is found to be the best fit for the data, then an equation can be found that best describes this correlation. This equation can be represented in slope-intercept form, where the slope represents the rate of change and the y-intercept represents the initial value.