Qlik Sense documentation and resources.
I spent a little time working through the formulas required to mimic the trend lines that are available in QlikView and currently not available in Qlik Sense without knowing how to write reasonably complex expressions.
Here are some examples of exponential and 2nd order polynomial trend lines with the relationship expressed as a formula in the subtitle.
Exponential Trend Line
2nd order polynomial trend line
To make things as clear as possible I have included the Excel variants of these formulas so you have something to reference as I have done whilst checking these formulas calculate correctly. These excel formulas were sourced from Excel Tips From John Walkenbach: Chart Trendline Formulas
The below table shows the equivalent formulas in Qlik for the excel formulas provided. I have also attached a QVF file with examples of each one so that you have something real to reference as you build these into your own applications. In addition I have included the excel file that I was using for testing to ensure my calculations were correct.
Excel Formulas | Qlik Formulas | |
---|---|---|
Linear Trendline | ||
Equation | y = m * x + b | y = m * x + b |
m | = SLOPE(y,x) | = LINEST_M(y,x) |
b | = INTERCEPT(y,x) | = LINEST_B(y,x) |
Logarithmic Trendline | ||
Equation | y = (c * LN(x)) + b | y = (c * LOG(x)) + b |
c | = INDEX(LINEST(y,LN(x)),1) | = LINEST_M(y,LOG(x)) |
b | = INDEX(LINEST(y,LN(x)),1,2) | = LINEST_B(y,LOG(x)) |
Power Trendline | ||
Equation | y=c*x^b | y = c * POW( x , b) |
c | = EXP(INDEX(LINEST(LN(y),LN(x),,),1,2)) | = EXP(LINEST_B(LOG(y),LOG(x))) |
b | = INDEX(LINEST(LN(y),LN(x),,),1) | = LINEST_M(LOG(y),LOG(x)) |
Exponential Trendline | ||
Equation | y = c *e ^(b * x) | y = c * POW( e , b * x) |
c | = EXP(INDEX(LINEST(LN(y),x),1,2)) | = EXP(LINEST_B(LOG(y),x)) |
b | = INDEX(LINEST(LN(y),x),1) | = LINEST_M(LOG(y),x) |
e | = EXP(1) | = e() |
2nd order Polynomial Trend | ||
Equation | y = (c2 * x^2) + (c1 * x ^1) + b | y = (c2 * POW(x,2)) + (c1 * x) + b |
c2 | = INDEX(LINEST(y,x^{1,2}),1) | see example - variable c2 |
c1 | = INDEX(LINEST(y,x^{1,2}),1,2) | see example - variable c1 |
b | = INDEX(LINEST(y,x^{1,2}),1,3) | see example - variable b |
Thanks, this is really useful.
Sorry for my English.
Can it be more accurate?
linest_m(total aggr(sum(Expenses),MonthYear),(Aggr(Log(Rowno()) * MonthYear, MonthYear)))
* (Aggr(Log(Rowno()) * MonthYear, MonthYear))
+ linest_b(total aggr(sum(Expenses),MonthYear),(Aggr(Log(Rowno()) * MonthYear, MonthYear)))
Hi Andy,
Great to share different options so we can find the best fit for our needs but I'd be cautious with your use of the term 'accurate'. To me the yellow line is more representative of the trend than the red one but that is subjective...
If you solve the 3rd an 4th order polynomial lines please let me know as this was a step too far for me 😉
Cheers
Richard
Hi Richard,
Thanks for your solution on the 2nd order polynomial. In your comments you wrote that there is no equivalent INDEX(y,x^{1\2},1). I'm not sure if you already know that {1\2} is an extra x -column. For example:
LINEST_M equals the LINEST function in Excel.
for the following table:
Y value | X value |
0,6931 | 1 |
0,7581 | 2 |
0,7553 | 3 |
the LINEST(Y:Y, X:X) = 0.0311. Same goes for LINEST_M in Qlik.
Adding the ^{1\2] leads to the following table in Excel (first x^1 and second x^2)
Y value | X value | X-2 value |
0,6931 | 1 | 1 |
0,7581 | 2 | 4 |
0,7553 | 3 | 9 |
With LINEST(Y:Y ; X:X-2) you'll get -0.0399 which is your C2 value.
So the LINEST_M in Qlik comes close to the LINEST function in Excel only I don't exactly know how to get two X-columns into the LINEST_M function. Maybe you are able to figure it out.
If you already knew about the two X-columns than you can ignore my post of course.
Anyway thank you very much for the calculations.
Hi all,
in a scatterplot where my measures are y=sum(sales) and x=sum(profit) and i have one dimension
my linear regression line would be
linest_m(total aggr(sum(sales),dimension)), aggr(sum(profit),dimension)) * sum(profit) + linest_b(total aggr(sum(sales),dimension)), aggr(sum(profit),dimension)) ?