![raw right away 3.4.3 raw right away 3.4.3](https://usermanual.wiki/Pdf/RedHatEnterpriseLinux7SystemAdministratorsGuideenUS.291851052-User-Guide-Page-1.png)
![raw right away 3.4.3 raw right away 3.4.3](https://images-na.ssl-images-amazon.com/images/I/81q3JCO7w2L._AC_UL160_SR160,160_.jpg)
In other words, when \(x=0\) then \(y=y - intercept\). The y-intercept is the location on the y-axis where the line passes through. A negative slope indicates a line moving from the top left to bottom right. The slope is a measure of how steep the line is in algebra, this is sometimes described as "change in y over change in x," (\(\frac\)), or "rise over run." A positive slope indicates a line moving from the bottom left to top right. You may recall from an algebra class that the formula for a straight line is \(y=mx+b\), where \(m\) is the slope and \(b\) is the y-intercept. Both \(x\) and \(y\) must be quantitative variables. In regression, the explanatory variable is always \(x\) and the response variable is always \(y\).
![raw right away 3.4.3 raw right away 3.4.3](https://img1.wsimg.com/isteam/ip/04433131-cbb7-4d56-8eab-043900564722/logo/62182fd7-c82f-4299-b13b-37d25eb795ac.png)
Unlike in correlation, in regression is does matter which variable is called \(x\) and which is called \(y\). The "linear" part is that we will be using a straight line to predict the response variable using the explanatory variable. If there are two or more explanatory variables, then multiple linear regression is necessary. The "simple" part is that we will be using only one explanatory variable. In this course, we will be learning specifically about simple linear regression. Regression uses one or more explanatory variables (\(x\)) to predict one response variable (\(y\)).