![]() ![]() We only need these to create the final calculation.įinally, it's time to create the line equation. Now hide the day_index, slope, and intercept calculations from the visualization. note: this has a lot in common with Ian Thompsons answer but the approach is different enough to have it be a separate answer. The new day index will be our "x column" and the measure (here unt) will be our "y column": Now let's use Looker's built-in slope function. You can use any timeframe or computation as long as your end result is an integer that increases as the date increases. We can do this with a simple table calculation like this: Here are the steps to take:įirst we need to convert the date to an integer. Fortunately Looker has the slope and intercept table calculation functions to help us with that! The only catch is that these functions expect integers and not dates. Now we can create the line equation with table calculations. Step 2: Calculating the Line Equation with Table Calculations If you cannot use dimension fill for some reason, you will need to right-join your explore with a calendar table containing future dates. The easiest way to achieve this is to turn on dimension fill and change the filters to the range you would like to see, as was done in the Explore shown below. The linear regression calculator generates the best-fitting equation and draws the linear regression line and the prediction interval. ![]() We need our graph to show both existing and future dates along the x-axis. Step 1: Creating Future Dates with Dimension Fill At a high level, our two steps are to create future dates and then to calculate the line equation with table calculations. We can achieve this kind of forecasting by using table calculations instead of trend lines. This Explore is my goal: A Solution: Linear Forecasting with Table Calculations Trend lines are partially useful, but they don't extend past the current data. It’s like the recipe for understanding relationships in your data. This page allows you to compute the equation for the line of best fit from a set of bivariate data: Enter the bivariate x,y data in the text box. I have some data from the past, and I want to use it to predict the future. The linear regression formula y a + bx or y a + bx is the core of this method. Starting in Looker 21.14, analysts can add data projections to new or existing Explore queries to help users predict and monitor specific data points with the Forecasting Labs feature. The linear regression calculator calculates the simple linear regression by using the least square method. Looker will not be updating this content, nor guarantees that everything is up-to-date. ![]()
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