Data analysis descriptive statistics

Retrieve Value Given a set of specific cases, find attributes of those cases. What is the value of aggregation function F over a given set S of data cases?

They provide simple summaries about the sample and the measures. Together with simple graphics analysis, they form the basis of virtually every quantitative analysis of data.

Descriptive statistics are typically distinguished from inferential statistics.

Measure of variablity

With descriptive statistics you are simply describing what is or what the data shows. With inferential statistics, you are trying to reach conclusions that extend beyond the immediate data alone. For instance, we use inferential statistics to try to infer from the sample data what the population might think.

Or, we use inferential statistics to make judgments of the probability that an observed difference between groups is a dependable one or one that might have happened by chance in this study. Thus, we use inferential statistics to make inferences from our data to more general conditions; we use descriptive statistics simply to describe what's going on in our data.

Descriptive Statistics are used to present quantitative descriptions in a manageable form. In a research study we may have lots of measures.

Or we may measure a large number of people on any measure. Descriptive statistics help us to simplify large amounts of data in a sensible way.

Each descriptive statistic reduces lots of data into a simpler summary. For instance, consider a simple number used to summarize how well a batter is performing in baseball, the batting average. This single number is simply the number of hits divided by the number of times at bat reported to three significant digits.

A batter who is hitting. The single number describes a large number of discrete events. This single number describes the general performance of a student across a potentially wide range of course experiences. Every time you try to describe a large set of observations with a single indicator you run the risk of distorting the original data or losing important detail.

The batting average doesn't tell you whether the batter is hitting home runs or singles. It doesn't tell whether she's been in a slump or on a streak. The GPA doesn't tell you whether the student was in difficult courses or easy ones, or whether they were courses in their major field or in other disciplines.

Even given these limitations, descriptive statistics provide a powerful summary that may enable comparisons across people or other units.

What is 'Descriptive Statistics'

Univariate Analysis Univariate analysis involves the examination across cases of one variable at a time. There are three major characteristics of a single variable that we tend to look at: The distribution is a summary of the frequency of individual values or ranges of values for a variable.

The simplest distribution would list every value of a variable and the number of persons who had each value. For instance, a typical way to describe the distribution of college students is by year in college, listing the number or percent of students at each of the four years. Or, we describe gender by listing the number or percent of males and females.

In these cases, the variable has few enough values that we can list each one and summarize how many sample cases had the value. But what do we do for a variable like income or GPA?

With these variables there can be a large number of possible values, with relatively few people having each one. In this case, we group the raw scores into categories according to ranges of values.

For instance, we might look at GPA according to the letter grade ranges. Or, we might group income into four or five ranges of income values. One of the most common ways to describe a single variable is with a frequency distribution.Descriptive statistics are used to summarize data.

Inferential Statistics	Typically, in most research conducted on groups of people, you will use both descriptive and inferential statistics to analyse your results and draw conclusions. So what are descriptive and inferential statistics?
Popular Searches	Range Minimum and Maximum Keeping with the pattern, a minimum can be computed on a single variable using the min VAR command. The maximum, via max VARoperates identically.

Learn about the different kinds of descriptive statistics, the ways in which they differ from inferential statistics, how they are calculated.

Descriptive statistics are used to describe the basic features of the data in a study. They provide simple summaries about the sample and the measures. Click the Data tab’s Data Analysis command button to tell Excel that you want to calculate descriptive statistics.

Excel displays the Data Analysis dialog box. In Data Analysis dialog box, highlight the Descriptive Statistics entry in the Analysis Tools list and then click OK.

Types of descriptive statistics All quantitative studies will have some descriptive statistics, as well as frequency tables. For example, sample size, maximum and minimum values, averages and measures of variation of the data about the average.

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In many studies this is a first step, prior to more complex inferential analysis. Descriptive statistics is the term given to the analysis of data that helps describe, show or summarize data in a meaningful way such that, for example, patterns might emerge from the data.

Descriptive statistics do not, however, allow us to make conclusions beyond the data we have analysed or reach conclusions regarding any hypotheses we might have made. Excel is the widely used statistical package, which serves as a tool to understand statistical concepts and computation to check your hand-worked calculation in solving your homework problems.