Reply to both posts in 250 words for each, APA format and references needed. Treat as forum posts that you are replying.
1- A histogram is graphing data into a table or even drawing one using different bars and heights to display the data. If I wanted to display a histogram, which is similar to a bar graph, I could calculate an example of average heights for men in Charlotte, North Carolina. The difference between bar graphs and histogram is a bar graph has categories and histograms display number ranges. On the left of the histogram would be the number of men and on the bottom would be the heights displayed in either inches or centimeters. Using a pool of let’s say 200,000 men in the city of North Carolina the heights would go up and down on the graph of average heights to the number of men on the left side of the graph. Eventually, you come up with a graphical interpretation of average heights and can see which bar is the highest to see the largest group of men with the most average height.
To make a histogram in excel you must first ensure you have the analysis toolpak in the add-ins menu. To create a histogram type a column of numbers in a new worksheet. Click on an empty cell and in the data analysis box click histogram, it will then ask the input range and choose the column of cells you created the numbers in. The bin is the next column of ranges. Under the new worksheet click on the chart output option which will create a histogram in the new worksheet.
Descriptive statistics is a set of data summarized from a set of numbers and do not explain beyond the data that is gathered. Using the example from above, descriptive statistics could show the average heights for ages of men in Charlotte, North Carolina. It is describing the set of data from the statistics. Again, ensure you have the analysis toolpak in excel, use a set of data such as the heights set in columns, click descriptive statistics in the data analysis tab. Input the ranges and output ranges with summary statistics grouped by columns and you will get your result.
As far as being sued from decision making, I don’t know that it would be credible to do so. Statistics are everywhere in the world and give information for people, consumers and businesses to make informed decisions. Ultimately, it is up to the humans to make the choice to listen to a statistic or not.
2-Histograms are graphical displays of data. Typically this involves the use of bars that are at different heights, based upon the data provided. In fact, histograms are very similar to bar charts, with the difference being that histograms uses groups, or bins, to group the data on the chart.
The main use of a histogram is to plot and discover the underlying frequency distribution of a set of data. When using inferential statistics, this allows the user to see the data and make inferences based upon the distribution of data. Moreover, other determinations can be made from the data when it provides an overall view of the distribution such as whether the data is normally distributed, it’s skewness, and any outliers.
In order to create a histogram, the data must be split into intervals, or bins. The data being referenced is then placed into the bin ranges and displayed on the histogram based upon frequency. The result is a bar graph displaying the distribution of the data.
Descriptive statistics is a broad term used to categorize different measures of centralness of data. In short, it describes the features of a specific set of data by giving short summaries of the data. Some of the summaries that are provided in descriptive statistics are the mean, the mode, the range, the variance, the co-effecients, and the standard deviation.
Often, descriptive statistics are used to understand what would otherwise be hard to understand data. The different measurements of descriptive statistics allow the user to understand the differences, the spread, and the variability of the data that is being investigated.
In my personal opinion, the use of both descriptive statistics combined with histograms “tells a story” of the data. Often these two analysis techniques can be combined to extract information that is not easily seen when reviewing raw data results.
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