HISTOGRAM
BASICS OF
HISTOGRAM
·
Histogram is the graphical representation of numerical data in
the form of distribution.
·
It was first introduced by Karl Pearson.
·
Histogram is normally used for the large quantity material
observations.
·
If you have 1000 components dimensions and you have to check the
deviation between those all dimensions, deviation in the sense how many
components extends their tolerance limits.
·
This data is taken and insert into the histogram it will give us
the deviated values hence we can identify on which value the deviation is
there.
·
It is very user friendly and it will make easy to see where the
majorities of values fall on measurement scale and how much variation is there.
·
Compare the process parameters or any product dimensional limit
with the histogram result. If you add the dimensional or process limits on
histogram it can easily find out that our process or product having proper
functioning or not.
·
The specification limit may be in the form of height, depth,
width, length, density, temperature, quantity etc. whichever is important for
the product or process.
USE OF
HISTOGRAM
·
When in any product or process giving wrong information or
dimension continuously the by collecting data we can use the histogram for
evaluation of the process and products.
·
We can develop our histogram in such a way that it gives
directly results in the form of accept or reject by applying some limits and decision
parameters.
PARTS OF
HISTOGRAM
1. Title
It contain the main information on histogram is built.
2. X-axis or
horizontal axis
This contain the average value of the data summarized for a
certain period of time.
3. Bar chart
It contain two important parameters height and width. Height shows
number of times the value within an interval occurred and width represents the length
of interval covered which is same for all the bars in the histogram.
4. Y-axis or
vertical axis
It represents the frequency of the values occurred within
interval.
5. Legends
It provides the additional information about where the collected
data is from and how it is collected.
STEPS OR
EXAMPLE
·
To construct a histogram from a
continuous variable you first need to split the data into intervals, called bins.
|
36 |
25 |
38 |
46 |
55 |
68 |
72 |
55 |
36 |
38 |
|
67 |
45 |
22 |
48 |
91 |
46 |
52 |
61 |
58 |
55 |
·
In the example above, age has been split into bins, with each bin representing a
10-year period starting at 20 years.
·
Each bin contains the number of
occurrences of scores in the data set that are contained within that bin.
·
For the above data set, the frequencies
in each bin have been tabulated along with the scores that contributed to the frequency
in each bin.
|
Bin |
Frequency |
Scores Included in Bin |
|
20-30 |
2 |
25,22 |
|
30-40 |
4 |
36,38,36,38 |
|
40-50 |
4 |
46,45,48,46 |
|
50-60 |
5 |
55,55,52,58,55 |
|
60-70 |
3 |
68,67,61 |
|
70-80 |
1 |
72 |
|
80-90 |
0 |
- |
|
90-100 |
1 |
91 |
·
Notice that, unlike a bar chart, there
are no "gaps" between the bars (although some bars might be
"absent" reflecting no frequencies).
·
This is because a histogram represents
a continuous data set, and as such, there are no gaps in the data (although you
will have to decide whether you round up or round down scores on the boundaries
of bins).
·
In a histogram, it is the area of the
bar that indicates the frequency of occurrences for each bin.
·
This means that the height of the bar
does not necessarily indicate how many occurrences of scores there were within
each individual bin.
·
It is the product of height multiplied
by the width of the bin that indicates the frequency of occurrences within that
bin.
·
One of the reasons that the height of
the bars is often incorrectly assessed as indicating frequency and not the area
of the bar is due to the fact that a lot of histograms often have equally
spaced bars (bins), and under these circumstances, the height of the bin does
reflect the frequency.
·
The major difference in histogram and
bae chart is that a histogram is only used to plot the frequency of score
occurrences in a continuous data set that has been divided into classes, called
bins.
·
Bar charts, on the other hand, can be
used for a great deal of other types of variables including ordinal and nominal
data sets.
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