The histogram of the given data would likely have a right-skewed shape, with a concentration of values at the lower end. The scores of 1 appear three times, while the scores of 2 and 3 appear less frequently. This results in a peak at the score of 1, tapering off as the scores increase. Overall, the distribution shows a clear concentration of lower scores with fewer higher scores.
To determine the percentage of scores between 63 and 90, you would need the complete dataset or a statistical summary (like a frequency distribution or histogram) of the scores. By counting the number of scores within that range and dividing by the total number of scores, then multiplying by 100, you can calculate the percentage. Without specific data, it's impossible to provide an exact percentage.
A histogram
All that histogram equalization does is remap histogram components on the intensity scale. To obtain a uniform (­at) histogram would require in general that pixel intensities be actually redistributed so that there are L groups of n=L pixels with the same intensity, where L is the number of allowed discrete intensity levels and n is the total number of pixels in the input image. The histogram equalization method has no provisions for this type of (arti®cial) redistribution process.
A dataset is likely to be symmetrical in a histogram if it follows a normal distribution, where the values are evenly distributed around the mean. Examples of such data include heights, weights, or test scores in a large, homogeneous population. Additionally, datasets that are generated from processes that balance around a central value, like the results of repeated measurements, often exhibit symmetry in their histograms.
Assuming that if one were to list the sides of a geometric shape he would do so following the shape's perimeter, my guess is that consecutive sides is a synonym for adjacent sides.
As a visual representation of data, then a histogram is a way of analysing data.
NO where!
It would help if the "following" did actually follow!
You draw a series of line segments joining the points which would be the middle of the top of each bar of the histogram.
A histogram
bimodal histogram is a histogram where there are two clear high points on the graph. ex.) age of people at a preschool play group. There would be preschool age and adult age. Not many teenagers or elderly. Bimodal...the ages representing preschool and adult (parents?) would stand above the rest
The aggregate of the scores made by the batsman would be the total sum of all the scores. In this case, the aggregate would be 15 + 10 + 30 + 70 = 125.
All that histogram equalization does is remap histogram components on the intensity scale. To obtain a uniform (­at) histogram would require in general that pixel intensities be actually redistributed so that there are L groups of n=L pixels with the same intensity, where L is the number of allowed discrete intensity levels and n is the total number of pixels in the input image. The histogram equalization method has no provisions for this type of (arti®cial) redistribution process.
A dataset is likely to be symmetrical in a histogram if it follows a normal distribution, where the values are evenly distributed around the mean. Examples of such data include heights, weights, or test scores in a large, homogeneous population. Additionally, datasets that are generated from processes that balance around a central value, like the results of repeated measurements, often exhibit symmetry in their histograms.
No. That would be a histogram.
Choosing wider class boundaries would cause a histogram of the data to present the appearance of a uniform distribution. This is because the data points within each wider class would be spread out more evenly, giving the histogram a more uniform look.
Bad test scores or periods of missed school.