Multiple Linear Regression

6 Concepts
Time Series (Smoothing)

4 Concepts
Time Series

6 Concepts
Time Series (Regression)

3 Concepts
Use **dummy variables** to **represent** **seasonal** or **cyclical** components of your time series!

**Scenario**: Given the Tinder stats for Crammer Nation University's Delta Apple Pi chapter, *interpret b _{Q2}.*

In Q2 of every year, it's estimated that Delta Apple Pi gets 329.39 *less* Tinder matches than Q1 (baseline)!

Just in case you're curious...

- In Q3, they're estimated to get 63.44
*more*than Q1 (baseline) - In Q4, they're estimated to get 68.53
*more*than Q1 (baseline)

**y-hat** = 1029.23 + 43.89(**b _{Year}**) - 329.39(

**y-hat** = **T** + **S**

**y-hat** is the predicted value ("Tinder matches").**T** is the *trend* component of the time series.**S** is the *seasonal* component of the time series.

**y-hat** = e** ^{T}** x e

**y-hat** is the predicted value ("Tinder matches").**T** is the *trend* component of the time series.**S** is the *seasonal* component of the time series.

By using dummy variables... we're **adding the seasonal component** to the time series **regression**!

Without them, the regression is only assessing the *trend* component across the periods of time.

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