You can configure the following options in the Trend Line Options dialog box: Note: To edit a trend line in Tableau Online or Tableau Server, you must have web editing permissions. In the tooltip that appears, select Edit to open the Trend Line Options dialog box.In the visualization, click the trend line, and then hover your cursor over it.In Tableau Desktop: Right-click a trend line in the visualization, and select Edit Trend Lines. Once you add a trend line to the visualization, you can edit it to fit your analysis. However, trend linesĬannot be turned on for stacked bars. When you change the viewīack to a state that allows trend lines, they reappear.Īutomatically stacks bar marks in many cases. Turned on and you modify the view in a way where trend lines are notĪllowed, the trend lines do not show. Therefore, trend lines are not allowed.Īdditionally, the ‘m/d/yy’ and ‘mmmm yyyy’ date formats on all data You can add a trend line to a view of sales over time because both salesĪnd time can be interpreted as numeric values.įor multidimensional data sources, the date hierarchies actually contain
On the Columns shelf and the Profit measure on the Rows shelf. That has the Product Category dimension, which contains strings, For example, you cannot add a trend line to a view Lines to a view, both axes must contain a field that can be interpretedĪs a number. The latter allows you to build a Confidence Interval around your regression model estimates.About adding trend lines (and when you can't add them) The former allows you to build a Confidence Interval around your regression coefficient. You should certainly not confuse the Standard Error of a regression coefficient with the Standard Error of your overall model. So, it is really key to allow you to interpret and evaluate your regression model. And, just as importantly it allows you to evaluate how statistically significant is your independent variable within this model. But, it allows you to construct Confidence Intervals around your regression coefficient.
Excel, R and most other software programs have ready formulas to calculate such P values.Īs outlined, the regression coefficient Standard Error, on a stand alone basis is just a measure of uncertainty associated with this regression coefficient. The latter is calculated using a T distribution function that just needs the Degree of Freedom in your model (number of observations minus number of variables) in addition to the t stat.
And, a t stat of 19 translates into a very statistically significant regression coefficient with a P value of 0.000. This is a huge statistical distance away from zero.
In other words, your regression coefficient stands 19 Standard Errors away from Zero or from being Null. The t stat is equal to your regression coefficient divided by its Standard Error. In this case your 95% CI for this regression coefficient would range from 0.46 to 0.56. And, the high frontier of this same CI would be: 0.51 + 1.96(Standard Error). In your case, the low frontier of this Confidence Interval would be equal to: 0.51 - 1.96(Standard Error). Sometimes, outputs also give you a 95% Confidence Interval around that coefficient. The standard error of this regression coefficient captures how much uncertainty is associated with this coefficient. And, together they give you information of how statistically significant is the regression coefficient associated with your variable excesslnst. It has a regression coefficient of 0.51 a standard error of 0.026 a t stat of 19 and a P value of 0.000.Īll those values are related. You have just a single variable in this linear regression:"excesslnst".