I want to solve 5 degree polynomial equation for calculating uknowns. here in this equaion a=0 a1,a2,a3,a4,a5 are unknow need to calculate and x is column value of dataframe
df=pd.DataFrame({'x':[10,10,9,9,9,8]})
Equation need to solve
d = a0 + a1.x + a2.x^2 + a3.x^3 + a4.x^4 + a5.x^5
here 'a' is slope parameter
Will drop a handly link to sympy here.
https://docs.sympy.org/latest/modules/solvers/solvers.html
I have a large spreadsheet with a number of forumlas and they all make complete sense apart from one, which is listed below. Does anyone have any idea what this NORMALDIST calculation is trying to acheive or tell me? It has relevants to HE
=MAX(1,NORMDIST(3,N18,N18/4,TRUE)-NORMDIST(0,N18,N18/4,TRUE) + 2*(NORMDIST(6,N18,N18/4,TRUE)-NORMDIST(3,N18,N18/4,TRUE)) + 3*(NORMDIST(9,N18,N18/4,TRUE)-NORMDIST(6,N18,N18/4,TRUE)) + 4*(NORMDIST(12,N18,N18/4,TRUE)-NORMDIST(9,N18,N18/4,TRUE)) + 5*(NORMDIST(15,N18,N18/4,TRUE)-NORMDIST(12,N18,N18/4,TRUE)) + 6*(NORMDIST(18,N18,N18/4,TRUE)-NORMDIST(15,N18,N18/4,TRUE)) + 7*(NORMDIST(21,N18,N18/4,TRUE)-NORMDIST(18,N18,N18/4,TRUE)) + 8*(NORMDIST(24,N18,N18/4,TRUE)-NORMDIST(21,N18,N18/4,TRUE)) + 9*(NORMDIST(27,N18,N18/4,TRUE)-NORMDIST(24,N18,N18/4,TRUE)) + 10*(NORMDIST(30,N18,N18/4,TRUE)-NORMDIST(27,N18,N18/4,TRUE)) + 11*(NORMDIST(33,N18,N18/4,TRUE)-NORMDIST(30,N18,N18/4,TRUE)) + 12*(NORMDIST(36,N18,N18/4,TRUE)-NORMDIST(33,N18,N18/4,TRUE)) + 13*(NORMDIST(39,N18,N18/4,TRUE)-NORMDIST(36,N18,N18/4,TRUE)) + 14*(NORMDIST(42,N18,N18/4,TRUE)-NORMDIST(39,N18,N18/4,TRUE)) + 15*(NORMDIST(45,N18,N18/4,TRUE)-NORMDIST(42,N18,N18/4,TRUE)) + 16*(NORMDIST(48,N18,N18/4,TRUE)-NORMDIST(45,N18,N18/4,TRUE)) + 17*(NORMDIST(51,N18,N18/4,TRUE)-NORMDIST(48,N18,N18/4,TRUE)) + 18*(NORMDIST(54,N18,N18/4,TRUE)-NORMDIST(51,N18,N18/4,TRUE)) + 19*(NORMDIST(57,N18,N18/4,TRUE)-NORMDIST(54,N18,N18/4,TRUE)) + 20*(NORMDIST(60,N18,N18/4,TRUE)-NORMDIST(57,N18,N18/4,TRUE)) + 21*(NORMDIST(63,N18,N18/4,TRUE)-NORMDIST(60,N18,N18/4,TRUE)) + 22*(NORMDIST(66,N18,N18/4,TRUE)-NORMDIST(63,N18,N18/4,TRUE)) + 23*(NORMDIST(69,N18,N18/4,TRUE)-NORMDIST(66,N18,N18/4,TRUE)))
Strange question I know, but could not think of where else to ask!!!
Cheers
The equation has a series of terms of the form N*[NORMDIST(3N,mu,sigma)-NORMDIST(3N-3,mu,sigma)] where mu is the mean (N18 in the equation), sigma is the standard deviation (N18/4), and with N going from 1 to 23. This appears to be an estimate involving the average of the normal distribution. It would be more rigorous for N to go from minus infinity to plus infinity and it's not clear why this formula truncated the interval to 1..23. Nevertheless, if the person who wrote the equation was calculating the average, then from the properties of the normal distribution you can derive a closed form solution as:
Total of all NORMDIST terms = mu/3 + 1/2
This will be accurate as long as mu (N18) is in the between 0 and 30. If you plug this into the equation you get
=MAX(1,N18/3+0.5)
Hope that helps.
From the docs...
NORMDIST function
Excel for Office 365 Excel for Office 365 for Mac Excel 2019 Excel 2016 More...
Returns the normal distribution for the specified mean and standard deviation. This function has a very wide range of applications in statistics, including hypothesis testing.
Important: This function has been replaced with one or more new functions that may provide improved accuracy and whose names better reflect their usage. Although this function is still available for backward compatibility, you should consider using the new functions from now on, because this function may not be available in future versions of Excel.
For more information about the new function, see NORM.DIST function.
Hi, I am taking a course on Coursera and came into this question. My answer is 1-(4048+3456)/8124=0.076. However, the answer is 0.067. Anybody can help me to solve this? Thank you!!
Accuracy: The number of correct predictions made divided by the total number of predictions made.
We're going to predict the majority class associated with a particular node as True. i.e. use the larger value attribute from each node.
So the accuracy for:
Depth 1: (3796 + 3408) / 8124
Depth 2: (3760 + 512 + 3408 + 72) / 8124
Depth_2 - Depth_1 = 0.06745
First We will draw confusion metrics for both cases and then find accuracy.
Confusion metrics:
Accuracy= (TP + TN) / (Total number of observation)
Accuracy calculation:
Depth 1: (3796 + 3408) / 8124
Depth 2: (3760 + 512 + 3408 + 72) / 8124
Depth_2 - Depth_1 = 0.06745
Though answer is correct but the confusion matrix looks not correct.
This should be the confusion matrix( for depth-2).
enter image description here
Let us say I have log_a1=-1000, log_a2=-1001, and log_a3=-1002.
n=3
I want to get the harmonic mean (HM) of a1, a2 and a3 (not log_a1, log_a2 and log_a3) such that HM = n/[1/exp(log_a1) + 1/exp(log_a2) + 1/exp(log_a3)].
However, due to rounding error exp(log_a1)=exp(-1000)=0 and accordingly 1/exp(log_a)=inf and HM=0.
Is there any mathematical trick to do? It is okay to get either HM or log(HM).
The best approach is probably to keep things in log scale. Many scientific languages have a log-add-exp function (e.g. numpy.logaddexp in python) that does what you want to high precision, with both the input and the result in log form.
The idea is that you want to compute e^-1000 + e^-1001 + e^-1002, so you factor it to e^-1000 (1 + + e^-1 + e^-2) and take the log. The result is -1000 + log(1 + e^-1 + e^-2), which can be computed without loss of precision.
log(HM)=log(n)-log(1)+log_a_max - log(sum(1./exp(log_ai - log_a_max)))
For a=[-1000, -1001, -1002];
log(HM)=-1001.309
SPSS keeps excluding a variable from my regression, and I am not exactly sure why. Here is where I started:
Perf = ILTProt + LProt + AbsoluteFitProt + Male + EDUC + Age + C
I then decided to switch out the AbsoluteFitProt variable for a different measure of a similar thing to give:
Perf = ILTProt + LProt + FitProt + Male + EDUC + Age + C
But SPSS keeps omitting ILTProt so I end up with
Perf = LProt + FitProt + Male + EDUC + Age + C
Does anyone know why this may be? Or How to fix it?
So this was due to co-linnearity between ILTProt and FitProt. Not sure how to fix it though, but have instead just decided to omit the excluded variable.
This (a variable being automatically omitted from a regression model) typically occurs when the variable is a constant or has perfect colinearity/correlation with another variable. So check if ILTProt & FitProt, have a perfect correlation?