Histogram of Sinusoid: Is it the Same as PDF?

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Discussion Overview

The discussion revolves around the relationship between the histogram of a sinusoidal function and its probability density function (PDF). Participants explore the implications of constructing a histogram from sinusoidal data, particularly in the context of statistical analysis and approximation of the PDF.

Discussion Character

  • Exploratory
  • Technical explanation
  • Mathematical reasoning

Main Points Raised

  • Brent Ellis questions whether the histogram of a sinusoid is equivalent to its probability density function and seeks clarification on what the histogram represents statistically.
  • One participant asserts that a histogram can represent a probability density function, noting that if derived from experimental data, it serves as an approximation, while theoretical derivation can yield an exact PDF if no averaging occurs.
  • Another participant discusses the process of creating a histogram from 4096 data points and inquires if normalizing the histogram by dividing by 4096 would yield an approximation of the PDF.
  • A further reply emphasizes that for continuous data, each bin should represent a range of values rather than a single value, suggesting that using more bins can improve the approximation of the true underlying PDF. It also mentions the necessity of standardizing the histogram so that the y-axis reflects proportions of observations per bin.

Areas of Agreement / Disagreement

Participants express differing views on the relationship between histograms and PDFs, with some suggesting they are equivalent under certain conditions while others highlight the need for careful consideration of binning and normalization. The discussion remains unresolved regarding the precise conditions under which a histogram can be considered an approximation of a PDF.

Contextual Notes

Limitations include the dependence on the choice of bin sizes and the method of data collection, which may affect the accuracy of the histogram as an approximation of the PDF. The discussion does not resolve how these factors influence the relationship between histograms and PDFs.

X1088LoD
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I am looking at values of a sinusoid, y = A sin(2*Pi*f*t), oscillating between A and -A at a frequency of 25 Hz over 0.650 milliseconds.

If I find the histogram of the sinusoid, is this the same thing as the probability density function of that sinusoid? If this is not the case, what does the histogram represent in terms of statistical analysis?

I appreciate it.

~ Brent Ellis
 
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The histogram is a representation of the probability density function. If you get it by some experimental menas, it is an approximation. If you get it from theory, then it will be exact if you don't average over intervals (sorting into bins is what a histogram usually refers to).
 
so let's say I got my data by means of measurements, 4096 data points total, then i sort them into histogram form

If each bin is represented by 1 value, If I divide each point by 4096, would that normalize the plot into the actual (approximation) PDF?
 
If the data you have collected is continuous, which i think is the case, then you can't let one bin represent one value, it has to be a range of values. The smaller the range of values, or the larger the number of bins you use, the closer you will get to approximating the true underlying pdf. Of course, the histogram has to be standardized so that the y-axis represents a proportion of observations per bin, so divide the frequencies of each bin by 4096.
 

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