When there is an identifiable trend in the data (i.e., at least a moderately strong correlation between x and y), we often want to model this relationship so that we can interpolate (estimate the value of y for any given value of x within the range of data we have) and extrapolate (predict the value of y for any given value of x beyond our range of data).

To model relationships, we can use a line or curve. The type of curve that best fits the data below is logistic.

There are many possibilities for which functions you could use to model the data, but the simplest is with a line. Therefore, this is called **linear regression**.

As you saw in Lesson 14, each x-value is x_{i} and each y-value is y_{i}. The line used to model the trend between the x_{i}’s and y_{i}’s is called the **regression line** or **line of best fit**.

**This is a preview of Lesson 15. To access the full book, please purchase a hard copy or a digital version. If you opt for the digital version, you will receive a link via email within 1 business day.**

Continue to Lesson 16, or select a lesson below.

Lesson 1: Introduction to Statistical Research Methods

Lesson 2: Visualizing Data

Lesson 3: Central Tendency

Lesson 4: Variability

Lesson 5: Standardizing

Lesson 6: Normal Distribution

Lesson 7: Sampling Distributions

Lesson 8: Estimation

Lesson 9: Hypothesis Testing

Lesson 10: t-Tests for Dependent Samples

Lesson 11: t-Tests for Independent Samples

Lesson 12: Intro to One-Way ANOVA

Lesson 13: One-Way ANOVA: Test significance of differences

Lesson 14: Correlation

Lesson 15: Linear Regression

Lesson 16: Chi-Squared Tests

Afterward

Index