Linear regression calculation

Need to know whether an algorithm to calculate linear regressions exist, given a determined number of points.

Dear Ingo,
I am not sure about your answer. Well, I need an script as a solution. Imagine, the time data is collected in a channel, DatCha_1. There are 1000 points corresponding to 10 seconds. I have the corresponding aceleration data, in Dat_Cha_2, that is, 1000 acceleration points. So, I need to calculate the linear regression with this acceleration data each second, that is, I must obtain the a, b data of a y=a+bx corresponding to each 100 values of the acceleration data, so 10 a values and 10 b values. I must divide the original acceleration data into 10 interval and obtain the linear regresion of each of this intervals.
What should be the script for this action? You mention to adjust the channel length of the time and data channel, but I do not know what you mean. In fact the function is:
Call CHNREGRYCALC("Time","Acceleration","Time","Regress_Y","linear") '... X,Y,XCHNNO,E,REGRTYPE
Could I use a new time channel only with 10 values and the original aceleration with 1000 values?
So, please could you send me a little example of the script I would need to solve the problem?
Regards,
Koniker

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Linear regression

Hi,
I would like to know how I could do the following linear regression calculation. I have two data channels, the first one corresponding to time information and the second one about aceleration. I must do linear regression with this acceleration data each second, but taking into account that the time channel has 10585 points for example for 44.1 seconds, corresponding to a 240msec sampling frecuency. I suppose that I must use the ChnRegrXYCalc function but I would like to know what the parameters should be.
REgards
Koniker.

Dear Ingo,
I am not sure about your answer. Well, I need an script as a solution. Imagine, the time data is collected in a channel, DatCha_1. There are 1000 points corresponding to 10 seconds. I have the corresponding aceleration data, in Dat_Cha_2, that is, 1000 acceleration points. So, I need to calculate the linear regression with this acceleration data each second, that is, I must obtain the a, b data of a y=a+bx corresponding to each 100 values of the acceleration data, so 10 a values and 10 b values. I must divide the original acceleration data into 10 interval and obtain the linear regresion of each of this intervals.
What should be the script for this action? You mention to adjust the channel length of the time and data channel, but I do not know what you mean. In fact the function is:
Call CHNREGRYCALC("Time","Acceleration","Time","Regress_Y","linear") '... X,Y,XCHNNO,E,REGRTYPE
Could I use a new time channel only with 10 values and the original aceleration with 1000 values?
So, please could you send me a little example of the script I would need to solve the problem?
Regards,
Koniker

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I'm interesting to know if anyone has faced the linear regression in a Tabular model by DAX formulas.
Thanks

It's been a while, but... recently Rob Collie did a blog post about this
here.

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We have created a forecast profile that uses strategy 35.
However when we run the forecast with this model for say the last 36months of history. The system proposes an almost straight line forecast. If we run it with 18 months history then the system proposes an acceptable forecast. I can now clearly see the sales history pattern in my forecast.
We do have enough data and seasonality in our history for the 36 months to work.
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We also recently upgraded to SCM 5.0
Thanks for your assistance
Robert

Before the system applies seasonal linear regression, it carries out a seasonal test. The system uses this test to check if the historical data contains any seasonal patterns. For this, the system determines the autocorrelation coefficient for all periods.
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dataset,
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I think I have an idea as to what to do. I still need to retain the integrity of the original data, therefore I will be creating new channels.
In a script you could do a loop looking for a value that is within the range then copy that data set to the new channel. Are there any builtin commands that would make this process quicker or more streamlined? All I know to do is to look at each value with CHD and make the decision from there.
Thanks,
Wayne

50G / simultaneous multiple scatter plots + intersection of linear regression lines

I have a real specific problem with scatter plots and linear regression lines.
First, I've been looking over the manual and the internet trying to figure out how to graph multiple scatter plots at the same time, but it seems like you can only do Columns 1 and 2 or Columns 1 and 3, etc., from Edat (greek letter), you can't do all three columns at once IF Column 1 represents X and 2 and 3 represent Y-values. Or columns 1 & 2 as one set, and 3 & 4 as a second set.
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I have a real specific problem with scatter plots and linear regression lines.
First, I've been looking over the manual and the internet trying to figure out how to graph multiple scatter plots at the same time, but it seems like you can only do Columns 1 and 2 or Columns 1 and 3, etc., from Edat (greek letter), you can't do all three columns at once IF Column 1 represents X and 2 and 3 represent Y-values. Or columns 1 & 2 as one set, and 3 & 4 as a second set.
Further, because it doesn't seem to be able to do this, I can't get it to find the intersection of these two lines. I can get the x-intercept and y-intercept just fine but I don't need that. What I can do is do a linear regression on both sets of data to get an equation for a line, put it into an array, and use the solver on it, that does indeed work. But that's a whole lot of extra keypresses just to get an ordered pair. I have experience on TI and Casio calculators and they don't have this seeming limitation.
Any suggestions?

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how can I make a graphic (scatter chart) with 1 "y", but multiple "x" (to make a multiple linear regression)?

Émilie,
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This answer assumes, as always, that I properly interpreted your problem statement. A linear fit line for each pair set should satisfy.
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gbaby wrote:
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Linear regression calculation

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