A line plot shows data on a number line with dots or x's to to show frequency. A frequency table is made by arranging collected data values in ascending order of magnitude with their corresponding frequencies. Both will show you the absolute frequency of any given value. And both give you a visual idea of the shape of the frequency and some intuition about outliers and things like that. You can count the number of dots or x on your line plot and create a frequency table. The difference is that one of them already has numbers counted for you. So for small numbers of data, either one will do the same job. But imagine if you have 10000000 points. You really don't want to count them using a line plot. A frequency table will tell you how often each data point occurs. However, if there are lots of values that these points can take on, the frequency table will have too many values to be of much use. The line plot will give us a good visual if there is lots of data, say 1000000 temp measurements, but we only look at the temps between 90-100 and only use integer values.
When John Tukey invented the boxplot he suggested (somewhat arbitrarily) that any data points more than 1.5 times the length of the box (ie, the distance between the upper and lower quartiles) from the nearest end of the box should be regarded as outliers.For example, suppose the box length were 2, that the lower quartile were 5 and that the smallest data point were 1.1.5 * 2 = 35 - 3 = 21 < 2; in other words, this data point is too far away from the box.Hence, the smallest data point is an outlier.
Plot straight line or curved graphs on the Cartesian plane Plot a line of 'best fit' for any correlation of given data Solve simultaneous equations when the coordinates intersect each other Transformations
you could use almost any kind of graph if you label it. But i would stay away from pie graphs. I would use a box and whisker plot.
Data collected after any research to gather primary data.
The whiskers mark the ends of the range of figures - they are the furthest outliers. * * * * * No. Outliers are not part of a box and whiskers plot. The whiskers mark the ends of the minimum and maximum observations EXCLUDING outliers. Outliers, if any, are marked with an X.
THe maximum observed (excluding any outliers).
The range is very sensitive to outliers. Indeed if there are outliers then the range will be unrelated to any other elements of the sample.
the number in your piece of data = n lower quartile, n+1 divided by 4 upper quartile, n+1 divded by 4 and times by three interquartile range(IQR) = upper quartile - lower quartile outliers(O) = interquartile range x 1.5 lower than IQR-O is an outlier (h) above IQR+O is an outlier (h) the outliers on your box plot are any numbers that are the value i have named (h) ^
The easiest way is to plot the values on a number line, then look at any outliers and consider whether they may be anomalies.
You can describe if there's any obvious correlation (like a positive or negative correlation), apparent outliers, and the corrlation coefficient, which is the "r" on your calculator when you do a regression model. The closer "r" is to either -1 or 1, the stronger that correlation is.
The midhinge.this because it eliminates 25 percent of the largest data values and the smallest data values.this means any outliers present in the set of data values will be unable to throw the data
A number that is different from any other numbers in the data.!
Ages of people are sorted meaningfully by a stem and leaf plot. Any type of data set in which the first or last digit differs and can be sorted. By using this type of plot, one can readily see the magnitude of each group of data sorted.
At least 2 and up to 5.
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Mostly through statistics, or summaries of the data set (depending on the type of data). There are many different statistical methods used to analyze the many different types of data that come from research studies or experiments. However if you just want a relatively quick and simplistic overview of a set of data than you should follow SOCS: Shape, Outliers, Center, Spread. Shape (the shape of the graphed data points) Outliers (any data points that fall outside the realm of "normal") Center (where the data points are mostly centered around) and Spread (the range of the data points). This should give you some immediate conclusions from your data.