going to far will give you unexpected resultsĬ) you can combine few smaller functions like: if less than for example 10 use linear aproximation (green arrow) else use square aproximation if less than 50 else. who knows where it going?!ī) keep aproximation in that range. If you have more aditional points (green arrow) you can't do that anymore with simple functions now you need to take some polynomial fuction wich will give you best aproximation in this interwal but on further picture. If you want to combine both of them you should use some square function (blue line) wich will give you biggest difference in small intervals but better aproximation on whole image. If you want some aproximation in the side you should use linear function (red line) wich will give you big mistake in the middle. If you wish some precise aproximation in some short interval (in the middle) you should use SIN function (black curve).īut that would give you HUGE mistake as you go further. Everything depends on what you want to achieve. The name of the trendline as text.There is no general answer. If this argument is omitted, Microsoft Excel generates a name True to display the R-squared value of the trendline on the chart (in the same data label as the equation). True to display the equation of the trendline on the chart (in the same data label as the R-squared value). If this argument is omitted, the intercept is automatically set by the regression The number of periods (or units on a scatter chart) that the trendline extends backward The number of periods (or units on a scatter chart) that the trendline extends forward Must be an integer greater than 1 and less than the number of data points in the series you are adding a trendline to Must be an integer from 2 to 6, inclusive

Uses an equation to calculate the least squares fit through points, for example, y = ax^b. Uses an equation to calculate the least squares fit through points, for example, y = ax^6 + bx^5 + cx^4 + dx^3 + ex^2 + fx + g. The number of points equals the total number of points in the series less the number specified for the period. Uses a sequence of averages computed from parts of the data series. Uses the equation y = c ln x + b to calculate the least squares fit through points. Uses the linear equation y = mx + b to calculate the least squares fit through points. Uses an equation to calculate the least squares fit through points, for example, y=ab^x.

Excel exponential trendline equation