What's the standard deviation of values in the histogram bar

In summary: The event occurs "randomly" a total of ##n## times. This means that the average value of ##k## is also ##n##. The variance is the square of the distance from the mean, and if all the events were equally likely, I'd divide by ##n## and have something like##\frac{1}{n}\Sigma(k_i - n)^2## But since the event occurs randomly a total of ##n## times, the variance is actually ##\frac{n^2}{n}##.
  • #1
whatisreality
290
1

Homework Statement


If one bar of a histogram has been generated with ##n## entries from a total of ##x## measurements, i.e. the event occurs randomly ##n## times in the ##x## event interval, then what is the standard deviation of values in this bar? Let ##k## be the range of values that could have been measured for this particular bar of the histogram, and assume that the expectation value of ##k## is ##n##.

Homework Equations

The Attempt at a Solution


I'm finding the wording tricky to understand. ##n## is the number of events in the interval, and the average value of ##k## is also ##n##? The variance is the square of the distance from the mean and if all the events were equally likely, I'd divide by ##n## and have something like
##\frac{1}{n}\Sigma(k_i - n)^2##
But I'm not sure that's what it means when it says the event occurs randomly a total of ##n## times. I'd really appreciate any hints on how to tackle this, thanks for any help!
 
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  • #2
It is strange to define k as a range. The range should be 0 to x.

I guess you have to assume that the x measurements are independent.
 
  • #3
mfb said:
It is strange to define k as a range. The range should be 0 to x.

I guess you have to assume that the x measurements are independent.
Would you say then that ##k## is the class width of the single histogram bar being considered? That's how I've interpreted it. I also thought it would be quite a strange coincidence if the number of events ##n## recorded, which corresponds to the area of the bar, happened to be also the average value of ##k##. Which is what I take all the above to mean.

I think the wording is very confusing, but I've written it as it was given to us.
 

1. What is standard deviation?

Standard deviation is a measure of how spread out the data points are from the mean or average value. It tells us how much the data deviates from the average value and gives an indication of the variability of the data.

2. How is standard deviation calculated?

To calculate standard deviation, we first find the mean of the data values. Then, we subtract each data point from the mean, square the differences, and find the sum of all squared differences. Next, we divide the sum by the total number of data points minus 1, and finally, we take the square root of this value to get the standard deviation.

3. What does the standard deviation in a histogram bar represent?

The standard deviation in a histogram bar represents the spread or variability of the data within that specific range of values. A larger standard deviation indicates that the data points are more spread out, while a smaller standard deviation suggests that the data is more clustered around the mean.

4. How does standard deviation impact the shape of a histogram?

The standard deviation can impact the shape of a histogram by influencing the width and height of the bars. A higher standard deviation will result in wider and shorter bars, while a lower standard deviation will lead to narrower and taller bars. This is because a larger standard deviation indicates more variability in the data, leading to a wider range of values and thus wider bars in the histogram.

5. Why is standard deviation important in data analysis?

Standard deviation is important in data analysis because it helps us understand the spread or variability of the data. It is a useful tool for summarizing and describing a dataset, and it allows us to compare the variability of different datasets. Standard deviation is also used in various statistical tests and models to make inferences and draw conclusions about a population based on a sample of data.

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