# Welcome!

In most career fields, it is necessary to collect and analyze data. Researchers collect data to look for trends and make predictions about the future. In order to accurately predict or estimate values, you need to have an appropriate function to model the data.

In this module, you will:

- Analyze real-world data in order to predict future outcomes.
- Build on your prior understanding of linear, exponential and quadratic models to assess the fit of a regression model using residuals and the correlation coefficient.
- Engage in activities that allow you to collect and analyze data, determine a model of best fit, and evaluate the reasonableness of the model selected.
- Learn to distinguish between correlation and causation.

To find other Algebra II modules, go to Common Core Algebra II Statistics and Probability.

# Lessons

## A Good Enough Fit?

In this lesson, you will review linear regression and analyze how well this model fits the data. You will also be introduced to residuals and the correlation coefficient, and use these tools to determine whether or not a linear model is the best fit for a set of data.

> Go to Lesson: A Good Enough Fit?

## Blame It on the Rain

In this lesson, you will distinguish between correlation and causation.

> Go to Lesson: Blame It on the Rain

## Which Model Makes Sense?

In this lesson, you will apply what you have learned to find a model of best fit for data sets. You will justify your model using residuals and/or the correlation coefficient (if a linear model).