How to Choose the Best Charts for Your Infographic - Venngage
See Appendix B for more information about using Excel to analyze data. to compare groups is through the use of bar graphs or bar charts referred to as histograms. relationship between two variables whose measures yield interval or ratio. The most useful graph for displaying the relationship between two A scatterplot shows the relationship between two quantitative variable appear on the horizontal axis, and the values of the other . constant rate and may even start. Or an icon chart (an icon with two fill colors, where the ratio is If you want to show similarities or differences among values or parts . Scatter plots are the easiest way to explore a potential correlation between two variables.
Odd number 5 of values: Even number 6 of values: The median is a better measure of central tendency than the mean for data that is asymmetrical or contains outliers.
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This is because the median is based on the ranks of data points rather than their actual values, and by definition, half of the data values in a distribution lie below the median and half above the median, without regard to the actual values in question. Therefore, it does not matter whether the data set contains some extremely large or small values because they will not affect the median more than less extreme values.
For instance, the median of all three of the following distributions is 4: This is partly a judgment call; in this example, the median seems reasonably representative of the data values in Distributions A and B, but perhaps not for Distribution C, whose values are so disparate that any single summary measure can be misleading. The Mode A third common measure of central tendency is the mode, which refers to the most frequently occurring value.
The mode is most often useful in describing ordinal or categorical data. When modes are cited for continuous data, usually a range of values is referred to as the mode because with many values, as is typical of continuous data, there might be no single value that occurs substantially more often than any other. If you intend to do this, you should decide on the categories in advance and use standard ranges if they exist.
For instance, age for adults is often collected in ranges of 5 or 10 years, so it might be the case that in a given data set, divided into ranges of 10 years, the modal range was ages 40—49 years.
In an asymmetrical or skewed distribution, these three measures will differ, as illustrated in the data sets graphed as histograms in Figures, and To facilitate calculating the mode, we have also divided each data set into ranges of 5 35— In this distribution, the mean and median are very close to each other, and the two most common ranges also cluster around the mean.
The modal range is A mean lower than the median is typical of left-skewed data because the extreme lower values pull the mean down, whereas they do not have the same effect on the median.
Measures of Dispersion Dispersion refers to how variable or spread out data values are. For this reason, measures of dispersions are sometimes called measures of variability or measures of spread. Knowing the dispersion of data can be as important as knowing its central tendency. For instance, two populations of children may both have mean IQs ofbut one could have a range of 70 to from mild retardation to very superior intelligence whereas the other has a range of 90 to all within the normal range.
The distinction could be important, for instance, to educators, because despite having the same average intelligence, the range of IQ scores for these two groups suggests that they might have different educational and social needs. The Range and Interquartile Range The simplest measure of dispersion is the range, which is simply the difference between the highest and lowest values.
Often the minimum smallest and maximum largest values are reported as well as the range. For the data set 95, 98, the minimum is 95, the maximum isand the range is 10 — If there are one or a few outliers in the data set, the range might not be a useful summary measure.
For instance, in the data set 95, 98,the range isbut most of the numbers lie within a range of 10 95— Inspection of the range for any variable is a good data screening technique; an unusually wide range or extreme minimum or maximum values might warrant further investigation. Extremely high or low values or an unusually wide range of values might be due to reasons such as data entry error or to inclusion of a case that does not belong to the population under study.
Information from an adult might have been included mistakenly in a data set concerned with children. The interquartile range is an alternative measure of dispersion that is less influenced than the range by extreme values.
Rank the observations from smallest to largest. Calculate the interquartile range as the difference between the 75th and 25th percentile measurements. Consider the following data set with 13 observations 1, 2, 3, 5, 7, 8, 11, 12, 15, 15, 18, 18, We can follow the same steps to find the 75th percentile: The resistance of the interquartile range to outliers should be clear.
The Variance and Standard Deviation The most common measures of dispersion for continuous data are the variance and standard deviation. Both describe how much the individual values in a data set vary from the mean or average value. The variance and standard deviation are calculated slightly differently depending on whether a population or a sample is being studied, but basically the variance is the average of the squared deviations from the mean, and the standard deviation is the square root of the variance.
4. Descriptive Statistics and Graphic Displays - Statistics in a Nutshell, 2nd Edition [Book]
If working with sample data, the principle is the same, except that you subtract the mean of the sample from the individual data values rather than the mean of the population. Formula for the sum of the deviations from the mean Unfortunately, this quantity is not useful because it will always equal zero, a result that is not surprising if you consider that the mean is computed as the average of all the values in the data set.
This may be demonstrated with the tiny data set 1, 2, 3, 4, 5. First, we calculate the mean: Calculating the sum of the deviations from the mean To get around this problem, we work with squared deviations, which by definition are always positive. Calculating the variance for a sample Note that because of the different divisor, the sample formula for the variance will always return a larger result than the population formula, although if the sample size is close to the population size, this difference will be slight.
Because squared numbers are always positive outside the realm of imaginary numbersthe variance will always be equal to or greater than 0.
The variance would be zero only if all values of a variable were the same, in which case the variable would really be a constant.
However, in calculating the variance, we have changed from our original units to squared units, which might not be convenient to interpret. Show the relationship of parts to the whole or highlight proportions. Pie chart Show the parts that contribute to the total and compare change over time.
Stacked column chart Show groups of related data. Bar chart, column chart Emphasize the magnitude of change over time. Area chart Show the relationship between two measures. Scatter chart Show the relationships between three measures. Bubble chart Show trends over time or compare data with two measures. Combination chart Identify patterns of high and low values. Tree map You can select the following formats for the chart types: The top of each stack represents the accumulated totals for each category.
This format highlights proportions. When actual values are important, use another format. When exact values are important, such as for control or monitoring purposes, use another format. The distortion in three-dimensional charts can make them difficult to read accurately. Column charts Column charts are useful for comparing discrete data or showing trends over time.
Column charts use vertical data markers to compare individual values. Line charts Line charts are useful for showing trends over time and comparing many data series. Line charts plot data at regular points connected by lines.
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Pie charts Pie charts are useful for highlighting proportions. They use segments of a circle to show the relationship of parts to the whole.
To highlight actual values, use another chart type, such as a stacked chart. Pie charts plot a single data series. If you need to plot multiple data series, use a percent stacked chart.
Bar charts Bar charts are useful for plotting many data series. Bar charts use horizontal data markers to compare individual values.
Area charts Area charts are useful for emphasizing the magnitude of change over time. Stacked area charts are also used to show the relationship of parts to the whole. Area charts are like line charts, but the areas below the lines are filled with colors or patterns. Point charts Point charts are useful for showing quantitative data in an uncluttered fashion.
Point charts use multiple points to plot data along an ordinal, or non-numeric, axis.