m and b are our two unknown constants that set the slope of the line (m) and move it up and down (b). In this case, as we can see from the graph (Figure 1), the y axis is CO 2 concentration, and the x axis is the year. The simplest approach, and the one we’ll try first is to fit a straight line through the data.
LEAST SQUARE REGRESSION EXCEL HOW TO
Our first decision is about how to model it. Now, looking at the data, we can see there is some sort of trend. For ease of demonstration, I’ve picked a few of the annual average CO 2 concentration measurements at random to work with: Billions, if not trillions of dollars depend on those predictions (and how governments decide to respond). It’s also an important data set to model because matching a function to the data will allow us to predict the change in CO 2 over the next 100 years, and those predictions are the ones the IPCC uses in their estimates of the impact of global climate change around the world.
The atmospheric CO 2 data from Mauna Loa is a convenient dataset. We’ll start with a data set that we want to match. Selected annual average carbon dioxide concentrations since 1959 (see Table 1 for data). Most spreadsheet programs, like Excel, will do some curve matching for you when you add trendlines to graphs, but for more sophisticated work - in pre-Calculus and beyond for example - you need a more general approach. Least Squares Regression can be used to match pretty much any type of function to any type of data. Here I’ll go over how to do Least Squares Regression, as simply as possibly, using Excel and its Solver*.
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