Im wondering how I would calculate the Sum of a Cumulative Probability in Excel?
I have attached the column of values that I am working with. Any help is appreciated
I have tried finding the mean/average of the values and then std deviation, then using the norm distribution function and then sum those values but it doesn't seem to be creating the right value.
You can use NORM.DIST(x,mean,standard_dev,cumulative) which allows you to specify the mean and the standard deviation. If the last argument is TRUE it returns the cumulative probability. Obviously, under the assumption, the distribution of your data corresponds to the Normal Distribution. If you are not sure about that, then you need to run a normality test that will confirm that first (anyway most natural phenomenons are distributed as Normal).
For the mean, you can use the AVERAGE function, and for the Standard Deviation STDEV.S.
So on cell D4 put the following formula to calculate the cumulative probability for 0.25:
=NORM.DIST(D3,D1,D2, TRUE)
So if your data correspond to a Normal Distribution, then the cumulative probability for 0.25 will be 0.361494.
Related
I am trying to recreate numbers which I easily calculated in excel and now I would like to have calculated in Power BI. To be more precise I would like to have it in power query/M and NOT in DAX due to later calculations.
To be more specific I would like to calculate the coefficients a and b of an exponential equation exponential y=ae^(bx).
In the following picture, you can see the data and also a graph over the data. Furthermore, the graph also displays a trendline using an exponential function and above the equation is shown y=6,5408e^(0,2834x).
These coefficients are calculated in cell b14 and b15 and the calculations are shown in d14 and d15 (my excel is set to Danish, the English version of a is calculated using ex(index(linest(ln( and b by index(linest(ln( ).
As you can see, to calculate the coefficients, a column with index have been created in column c.
To calculate the coefficients I used the LN() function on a list/array in excel, and the only power query/M function I can find is Number.Ln(), however, it does note take a list as input.
Due to the lack of on LN function in power query/M, I have a hard time calculating this, and I really hope someone has an answer to this!
Thank you in advance !
Kind Regards, Louise
Number.Ln()
Returns the natural logarithm of a number, number. If number is null Number.Ln returns null.
https://learn.microsoft.com/en-us/powerquery-m/number-ln
Also check out
https://www.bookkempt.com/2017/10/simple-linear-regression-in-power-query.html
I want to generate a single column of 6000 numbers with a normal distribution, with a mean of 30.15, standard deviation of 49.8, minium of -11.5, maximum 133.5.
I am a total newb at this so i tried to use the following formula in a cell and than just drag it down to cell 6000:
=NORMINV(RANDBETWEEN(-11.5,133.5)/100,30.15,49.8)
It returns a value but sometimes it returns #NUM! error. Thank you!
Unfortunately NORMINV expects a probability for the argument, which must be a value in the interval (0, 1). Any parameter outside that range will yield #NUM!.
What you're asking cannot be done directly with a normal distribution since that has no constraints on the minimum and maximum values.
One approach is to use a primary column to generate the normally distributed numbers, then filter out the ones you want in the adjacent column. But this will cause even the mean (let alone higher moments) to go off quite considerably due to your minimum and maximum values not being equidistant from the mean. You could get round this by recentering the distribution and adjusting afterwards.
I've been attempting to program a PowerPivot Workbook that I've been using to calculate a weighted standard deviation.
The problem is that when I use the code:
(the quality metric Q is weighted by the Product Tons for each record to get weighted statistics for variable periods [ie weeks, months, years])
Product Q-St.d:=SQRT((SUMX('Table',((([PRODUCT_Q]-[W_Avg_Q]))^2)*[TOTAL_PRODUCT_TONS]))/(((COUNTX('Table',[Production_Q])-1)*[Product Tons])/COUNTX('Table',[Production_Q])))
It calculates the [W_Avg_Q], which is the weighted average for Q, for each row as it iterates through instead of getting a weighted average for the whole context. I've learned pretty much all my DAX on the job or this site so I'm hoping there's some command to get the weighted average to calculate first. Does anyone know such a command? or another method of getting a weighted standard deviation out of DAX?
I think what you want to do is to declare [W_Avg_Q] a variable and then use it in your formula.
Product Q-St.d :=
VAR WtdAvg = [W_Avg_Q]
RETURN SQRT((SUMX('Table',((([PRODUCT_Q]-WtdAvg))^2)*[TOTAL_PRODUCT_TONS])) /
(((COUNTX('Table',[Production_Q])-1)*[Product Tons])/COUNTX('Table',[Production_Q])))
This way it gets calculated once in the proper context and then stored and reused within the formula.
I have a mean value x and I want to model it into the future. I want to output a value of what it could be in 6 months. Assuming the value follows a normal distribution and we have the standard deviation how do I randomize the value x while following a normal distribution? I'm doing this in excel, but just understanding it would help too! Basically I want to produce numbers 68% of the time within 1 deviation, 95% of the time withing 2 deviation etc. etc.
You can use the excel function 'NORMINV' to convert a random input 'RAND()' to a normal distribution.
=NORMINV(RAND(),Mean,Std Dev)
i.e. if you repeat this many times, save and analyze the results, you'll see a bell curve over the input Mean value.
Does that get you started?
The tricky bit comes when you come up with the formula to predict what a value will be in the future using this.
I have 2 columns and multiple rows of data in excel. Each column represents an algorithm and the values in rows are the results of these algorithms with different parameters. I want to make statistical significance test of these two algorithms with excel. Can anyone suggest a function?
As a result, it will be nice to state something like "Algorithm A performs 8% better than Algorithm B with .9 probability (or 95% confidence interval)"
The wikipedia article explains accurately what I need:
http://en.wikipedia.org/wiki/Statistical_significance
It seems like a very easy task but I failed to find a scientific measurement function.
Any advice over a built-in function of excel or function snippets are appreciated.
Thanks..
Edit:
After tharkun's comments, I realized I should clarify some points:
The results are merely real numbers between 1-100 (they are percentage values). As each row represents a different parameter, values in a row represents an algorithm's result for this parameter. The results do not depend on each other.
When I take average of all values for Algorithm A and Algorithm B, I see that the mean of all results that Algorithm A produced are 10% higher than Algorithm B's. But I don't know if this is statistically significant or not. In other words, maybe for one parameter Algorithm A scored 100 percent higher than Algorithm B and for the rest Algorithm B has higher scores but just because of this one result, the difference in average is 10%.
And I want to do this calculation using just excel.
Thanks for the clarification. In that case you want to do an independent sample T-Test. Meaning you want to compare the means of two independent data sets.
Excel has a function TTEST, that's what you need.
For your example you should probably use two tails and type 2.
The formula will output a probability value known as probability of alpha error. This is the error which you would make if you assumed the two datasets are different but they aren't. The lower the alpha error probability the higher the chance your sets are different.
You should only accept the difference of the two datasets if the value is lower than 0.01 (1%) or for critical outcomes even 0.001 or lower. You should also know that in the t-test needs at least around 30 values per dataset to be reliable enough and that the type 2 test assumes equal variances of the two datasets. If equal variances are not given, you should use the type 3 test.
http://depts.alverno.edu/nsmt/stats.htm