A linear model uses the standard equation for a line to estimate an output based on an input. In the linear equation, y=mx+b, y is the output value while x is input value. M is the slope (rise over run) and b is the y-intercept or where the line crosses the y-axis. Ultimately, all you need is a slope (m) and an intercept (b) and you can be off an running! For instance, you could predict the what the value of a home should be by how many bedrooms it has. You would collect sales price and the number of bedrooms data from home sales and chart them. From this, you would identify the slope and intercept of the model. Using the linear equation, you could plug in any number of bedrooms for x and get a sales price y. It would not take into account location, size of home and other factors that ultimately affect the overall sales price, so multiple datasets might need to be developed for a thorough understanding. what is a good response to this post