This article will take you through just a few of the methods that we have to describe our dataset. Let’s get started by firing up a season-long dataset of referees and their cards given in each game last season. The easiest way to produce an en-masse summary of our dataset is with the ‘.describe ()’ method.
The mean, also referred to by statisticians as the average, is the most common statistic used to measure the center of a numerical data set. The mean is the sum of all the values in the data set divided by the number of values in the data set. Keeping this in consideration, what does the mean tell you about your data?
Let’s get started by firing up a season-long dataset of referees and their cards given in each game last season. The easiest way to produce an en-masse summary of our dataset is with the ‘.describe ()’ method. This will give us a whole new table of statistics for each numerical column:
Mean is just another name for average. To find the mean of a data set, add all the values together and divide by the number of values in the set. The result is your mean! To see an example of finding the mean, watch this tutorial!
People also ask, what does the mean tell us in statistics? The statistical mean refers to the mean or average that is used to derive the central tendency of the data in question. It is determined by adding all the data points in a population and then dividing the total by the number of points. The resulting number is known as the mean or ...
The mean is essentially a model of your data set. It is the value that is most common. That is, it is the value that produces the lowest amount of error from all other values in the data set. An important property of the mean is that it includes every value in your data set as part of the calculation. Beside above, what does the mean of ...
The resulting number is known as the mean or the average. What does the mean and standard deviation tell you? Standard deviation is a number used to tell how measurements for a group are spread out from the average (mean), or expected value.
The complete description of the data is always the data itself . Descriptive statistics and other tools for describing data go one step further to summarize aspects of the data. Summaries are a way to compress the important bits of a thing down to a useful and manageable tidbit.
Describing data is necessary because there is usually too much of it, so it doesn’t make any sense by itself.
The mode is the most frequently occurring number in your measurement. That is it. How do you find it? You have to count the number of times each number appears in your measure, then whichever one occurs the most, is the mode.
Measures of central have one important summary goal: to reduce a pile of numbers to a single number that we can look at . We already know that looking at thousands of numbers is hopeless. Wouldn’t it be nice if we could just look at one number instead? We think so. It turns out there are lots of ways to do this. Then, if your friend ever asks the frightening question, “hey, what are all these numbers like?”. You can say they are like this one number right here.
Let’s introduce three important terms we will use a lot, distribution, central tendency, and variance. These terms are similar to their everyday meanings (although I suspect most people don’t say central tendency very often).
This chapter is about descriptive statistics. These are tools for describing data. Some things to keep in mind as we go along are: 1 There are lots of different ways to describe data 2 There is more than one “correct” way, and you get to choose the most “useful” way for the data that you are describing 3 It is possible to invent new ways of describing data, all of the ways we discuss were previously invented by other people, and they are commonly used because they are useful. 4 Describing data is necessary because there is usually too much of it, so it doesn’t make any sense by itself.
Descriptive statistics are great and we will use them a lot in the course to describe data. You may suspect that descriptive statistics also have some short-comings. This is very true. They are compressed summaries of large piles of numbers. They will almost always be unable to represent all of the numbers fairly. There are also different kinds of descriptive statistics that you could use, and it sometimes not clear which one’s you should use.
The easiest way to produce an en-masse summary of our dataset is with the ‘.describe ()’ method.
Our describe table above is great for a broad brushstroke, but it would be helpful to look at our referees individually. Let’s use .groupby () to create a dataset grouped by the ‘Referee’ column
In this section, we have seen how using the ‘.describe ()’ function makes getting summary statistics for a dataset really easy.