Histogram with # on edge of bin?

In summary, the conversation discusses constructing bins for data points, with a specific focus on a data point of 270. The question is raised as to which bin this data point would fall into, and if the current bin ranges are appropriate. It is suggested that non-overlapping bins should be used, with a suggestion to adjust the ranges to fit the data, especially if the values are arbitrary real numbers. The concept of using intervals, such as 259.5 to 269.5 and 269.5 to 279.5, is also mentioned as a way to encompass the same integer range while appearing to have no gaps.
  • #1
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let's say I have 10 data points. And one of my data points is 270.

Then let's say two of my bins are 260-270 and 270-280. Which bin would you put the 270 in?
Or would such a choice of bin range be inappropriate and new ranges have to be chosen?
 
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  • #2
When you construct your bins they should be literally non-overlapping - how you decide to do this is up to you. If your values are all integers, then most people would probably think it's more natural for the bins to be 260-269 and 270-279, if your values are arbitrary real numbers then the probability you pulled an integer is zero and you should be rethinking what the heck is going on (or more likely just arbitrarily picking whether everyone rounds down or up).

I have seen histograms often described as having intervals for example as 259.5 to 269.5 and 269.5 to 279.5 in order to encompass the same integer range, but look like there aren't any gaps.
 

1. What is a histogram?

A histogram is a graphical representation of data that shows the frequency of values within a given range or bin.

2. What does the # on the edge of a bin in a histogram represent?

The # on the edge of a bin in a histogram represents the upper limit of that particular bin. It is used to show the range of values included in that bin.

3. How is a histogram different from a bar graph?

A histogram is used to represent numerical data, while a bar graph is used to represent categorical data. In a histogram, the width of each bar is proportional to the range of values it represents, whereas in a bar graph, the width of each bar is usually the same.

4. Why is a histogram useful?

A histogram is useful because it allows us to visualize the distribution of data and identify patterns and trends. It also helps in identifying outliers and understanding the central tendency of the data.

5. How do you interpret a histogram?

To interpret a histogram, you need to look at the shape of the distribution, the range of values, and the frequency of each bin. A symmetrical distribution indicates that the data is evenly distributed, while a skewed distribution indicates that the data is concentrated towards one end. The range of values helps in understanding the spread of the data, and the frequency of each bin shows how many values fall within that particular range.

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