I want to convert my data which is in Vpp/sqrt(Hz) to rad^2/sqrt(Hz) to compare with other data. So that I can go forward in my research work.
How can I achieve this?
Related
Trying to draw a Bell Curve/Normal Distribution curve with the data set provided, but it is not getting created on Excel. Can anyone help me in creating the same.
https://docs.google.com/spreadsheets/d/1ipDo6WlbmDUBZuuS4ya3ZGD7mkP_vnbByK3KvyLbJ88/edit?usp=sharing
The above file can be used as the data set for creating the curve. Can someone explain me the procedure of how to make a curve with the above data set in Excel?
if your data is normally distributed it should resemble a bell curve.
By "Trying to draw a Bell Curve/Normal Distribution curve", are you referring to a line diagram?
Remember, the bell curve is a histogram of your data. If you inserted a histogram of your data, would that be enough?
If not, what you could do is calculate the standard deviation of your data (and the mean), then you could make a column for different standard deviations and what value we expect it to be.
We could then incorporate that into your old histogram. You could use a "Combo" chart and plot the histogram on one axis and the a line for your calculated values (you can make it smooth if you think it's too sharp. Also, you could decrease the distance between each of your calculated values (1.1, 1.2, ...) instead of let's say halves of standard deviations.
Unfortunately, the data you provided is not at all normally distributed.
So you can't create a bell curve based on this data, no.
I have three Excel columns of data from an experiment with a pendulum: time, angle displacement, and angular velocity. I was wondering if there is a way in Excel to calculate and then graph the period (and, if possible, display the function for the graph)... I realize it's kinda a dumb question. I'm still new at Excel.
Thanks for any pointers u can give!
In case the Analysis ToolPak is installed, one can use Tools->Data Analysis->Fourier Analysis. If the data is a superposition of harmonic functions (sin,cos), the corresponding frequencies (or inverse periods) will appear as peaks in the Fourier analysis.
I have time series data , the two columns are traffic density and date. I wish to predict the density for next 7 days.
I am using arime time series forecasting. I am able to forecast density but I want to forecast density with time. How can it be done?
GO with RNN(LSTM) or FBProphet
Here's a good piece of work for FBProphet:
https://towardsdatascience.com/a-quick-start-of-time-series-forecasting-with-a-practical-example-using-fb-prophet-31c4447a2274
Here's a good piece of work for LSTM:
https://colah.github.io/posts/2015-08-Understanding-LSTMs/
However you can also look into ARIMA Variants.
I have some wind data in excel that is binned by wind speed and direction. I want to re-bin it to cover different intervals (assuming the data is uniform across the original intervals). i.e I want to go from:
To
Can anyone give me some pointers? Struggling to think of an elegant way to do this.
Is there a function within Excel to Interpolate while taking into account a logarithmic prediction?
At the moment I am using linear interpolation but would like to find a better way to fill in the blanks if possible.
There's no logarithmic regression or interpolation in Excel, even in the Anlaysis ToolPak. You'll need much more advanced software for that, such as MatLab.
If you're stuck working in Excel... here's a possible mathematical solution:
Rather than working with the raw data x and y, instead try plotting x and a^y, where a is the base. (Or plotting log(x,a) against y.) If you have the correct base a (and there's no vertical offset), you will then have a linear relationship from which you can perform a linear interpolation as normal, then convert the interpolated values back to actual values by taking the log of them.
If you don't know what a is, then you can instead calculate a line of best fit for an arbitrary a, calculate the standard residuals, and then use Problem Solver to modify a until you get the lowest possible standard residuals, at which point you have the best estimate of a.
Similarly if there is a vertical offset b, you'll need to test some variables there that also result in a linear relationship. Plot x against a^(y-b)