Statistical Models
Causality

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}​

Correlation between two variables does not mean that a change in one variable causes a change in the other. For example:

• If a strong correlation between the number of cat owners and number of burglaries in towns is found, it does not mean that owning a cat increased or decreased the number of burglaries. We would describe this correlation as either being coincidental, or a third variable may be the cause. In this case the third variable may be the population of the town.

If a change in a variable does cause a change in the other variable, then we say a causal relationship occurs between them.