I want to show measure values in 'measure' not as a dimension without aggregation
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I have some raw spreadsheet data that's in a format, like:
12/7/2016 3:07:00, 88.05,
12/7/2016 3:08:00, 89.10,
12/7/2016 3:13:00, 87.00,
etc
These data points are not sampled at a regular interval, but are randomly collected throughout the day.
Using Google Sheets I'm able to graph this easily onto a Timeline chart. This puts the values at the correct position on the timeline and takes the uneven sampling intervals into account.
I would like to generate a histogram of the timeline data while taking into account the timestamps and calculate an average value over a timeframe. I believe if I simply run this through the built-in histogram chart or select my data values and run it through an averaging function, it will be skewed by the uneven sampling intervals.
What's the easiest way to quantize the sampling intervals (ideally within Google Sheets) for generating my histogram and averaging?
or
Is there a built-in method to generate histograms/averaging of values while taking timestamp data into account, eliminating the need for quantized data?
You can calculate the appropriate average as follows (assuming your data is in the range A2:B50)
=sum(arrayformula((A3:A50-A2:A49)*(B3:B50+B2:B49)/2))/(A50-A2)
This formula implements the Trapezoidal rule: the value assigned to each time interval is the average of observed values at the ends of that interval.
There isn't a built-in "weighted histogram" tool, so it appears that needs re-sampling to create a representative histogram. Here is one way to resample. Let's say you want 20 samples; then in C2 enter
=arrayformula(A2+(row(1:20)-1)*(A50-A2)/19)
to get 20 uniformly distributed time values. (Division by 19 because of the fence-post distinction.) Then in D2,
=arrayformula(vlookup(C2:C21, A2:B50, 2))
will lookup a value for each sample time. Then you can build a histogram from column D.
I have 10,000 data in column A that hold values for a normal distribution; I have to estimate the variance using the series
series here
I've already estimated the sample mean x̄
, but my question is how do I apply the series to all the cells in a column? Or if I have to split it up into parts (subtraction, then squaring, then summation) how do I change the data in each cell by the same amount?
I have very large data of size (1 x 23750811). I would like to visualise this data in histogram-Matlab.
As the data is very large, I am getting only a single dot in my plot. But I could visualise them separately, the first 1/4th of data and so on.
Any suggestion to visualise the entire data in a single plot at once.
Thanks !
Loading all your data into MatLab is inefficient; you can try using DuckDB; it allows you to use SQL to query very large datasets in several formats like CSV or Parquet; you can pre-compute the bins and heights, then export them and plot them using matlab.
This is a snippet you can use:
select
floor(column/bin_size)*bin_size,
count(*) as count
from "path/to/file.csv"
group by 1
order by 1;
Alternatively, you can try sampling your data.
I am currently working on a project in which I will be creating a vertical scatter plot using on average 6 points of y-axis data using Sigmaplot. The units of the graph are depth of snow in cm vs time. However the data I have collected is gathered over a range of days (i.e. 173-176) and I am having trouble applying my data sets to their respective ranged abscissa. I've noticed inputting the data in this manner finds the difference in the abscissa (i.e. 173-176 would correspond to 3) rather than interpreting the data as a range. Can anyone help me find a way in which to input abscissa not as singular values but rather ranges of those values using Sigmaplot?
Leaving the abscissa ranges in tact, plot all of the data. Sigmaplot does not want sort these data and rather places them in their respective chronological order. I did not find a way to make the width of the points as wide as the abscissa.
I have used 5th moment of my data as a feature for classification and it gives good results, but i don't know what it measures? is it a shape parameter like kurtosis and skewness?
I'm using matlab's
m=moment(X,order);
which returns the central sample moment of X specified by the positive integer order.