Summary Statistics Simplified – It’s Time For You To Nail It!
October 30, 2019
Statistics is the scientific field of learning from data. It helps us to apply the right methods for data collection, analysis, and presentation. Notably, statistics application is required in almost all disciplines. The most applied type of statistics is the summary statistics. In this post, we are going to demystify summary statistics to establish what they are and help you understand the best options for your study.
What Are Summary Statistics?
If you are studying statistics or working on a study that requires the application of summary statistics, the first step is to define them. Summary statistics are used to give a quick overview of data being used in research. The statistics can be broken into two main categories: measures of dispersion (scatter) and measures of central tendency.
In addition to summarizing details about the study sample, they also provide info on the values of the data sets you are working on in your research. The summary statistics help you to describe the main characteristics of the data in your sample, including potential correlation and distribution.
Summary Statistics Examples
If you are planning to do a study, understanding the nature of summary statistics is very important. This is because the nature of the research and data to use therein will determine how well you will answer the research questions. Here are the main summary statistics examples:
The average is one of the common summary statistics used in most studies. Average helps a researcher to measure the central tendency of a data set. The main methods of representing averages include mean, mode, and median.
The mean is the number you get after summing up a set of numbers and dividing the sum by the total numbers in the set. To be more accurate, you should call mean arithmetic mean.
Median is the number in the middle of any set of numbers. In research, median is used to measure dispersion. It is considered very crucial because it helps researchers to understand the behavior of the entire group.
Mode is the component that appears more frequently in a group of elements. It is also defined as the component with the highest frequency in a set of data.
Note that in statistics, it is possible to have more than one mode as far as the modal elements have the same frequency.
Shape is another commonly used type of summary statistic to demonstrate how data is distributed. It helps to answer the question: “What is the pattern?”
One of the common types of shapes used in summary statistics is skewness. This measures the asymmetry of possible distribution of a random variable around the mean.
Another type of shape is the L-moment, which is a sequence of statistics for summarizing the shape of the most probable type of distribution.
If the data you are using in statistics has multiple variables, you need to establish whether they are dependent or correlated. One of the main examples of dependence is the correlation coefficient. Correlation is a form of relationship exhibited between two or more variables where a change in one variable results in a change in the other.
A Closer Look At Descriptive Statistics
As the name suggests, descriptive statistics are used for describing the features of the data you are working on. They are part of summary statistics that help to generate a simple data summary and measures of the sample. Descriptive statistics work with simple graphics assessment to act as the bedrock of quantitative data analysis.
But how do descriptive statistics differ from inferential statistics? With inferential statistics, the aim is arriving at a conclusion. However, descriptive statistics target to describe the summary of what the data is showing simply.
When using descriptive statistics, the data is presented in manageable quantities.
For example, if you measure a specific parameter in a very large human population, descriptive statistics make it possible to simplify them sensibly and come-up with a simple and reliable statistical summary.
Which Summary Statistics to Use in Your Study?
When it comes to deciding the summary statistics to use in your research, the secret is looking ahead. You need to get it clear what is the problem you intend to solve using statistical evaluation of data. Indeed, you need to be clear about the overall goal before exploring how to achieve it. Here are some of the summary statistics options to consider:
Summary statistics R: The R summary function, summary (), offers a wide range of functions for generating summary statistics.
Panel Data: Panel data, commonly referred to as cross-sectional time-series data, is the type of data generated from a few observations overtime on a large number of units such as governments, homes, or people. Panel data contains the researchers’ observation about a specific phenomenon of interest.
Report: Summary statistics report is used to display different summaries about the data in the study population based on the researcher’s goals. The report can contain standard deviation, mean, mode, and other statistics of interest.
Simplifying Summary Statistics
To successfully use summary statistics, it is important to understand all the concepts. You should also follow other data summary examples and how they have been applied. Besides, you also need to have the right tools. One of the top examples is Strata. This is one of the powerful statistical software options for analyzing, managing, and generating graphical visualizations.
Another highly recommended tool is Excel. You can utilize the Excel Analysis Toolpak add-ins to generate the targeted summary statistics promptly. To do this, you only need to hit the Data Analysis command tab to get a pull-down menu and select descriptive statistics. Then, select the data that you intend to describe and specify the Output Option. Excel is one of the most utilized tools for summary statistics because it is easy to use and readily accessible. Other important tools you should consider include tables, calculators, and ArcGIS.
This paper has demonstrated that summary statistics are not necessarily complex if you take the time to understand them. Remember to hone your skills by practicing with summary statistics examples until you grasp every concept and learning from the best writing experts in this field.
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