Descriptive Statistics
Knowing how to improve your processes and workflow depends on how well you can interpret the data you collect during your testing phases. Even if data is collected appropriately, if it is reported and interpreted incorrectly, it will not improve your organization’s performance.
To make sure that everyone on your team can correctly read and interpret your data, learn more about descriptive statistics and how to properly use them in your quest to improve your organization.
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12m
Unlocking actionable insights from data can be a particular challenge for organizations across industries. However, it’s increasingly becoming a critical skill set for business professionals to develop. The world’s most valuable resource is data.
School of Six Sigma courses can make understanding how to report, analyze, and incorporate this data easier. The data-driven methodology of Six Sigma was designed to improve business processes, products, and services and, by extension, provide more value to the end consumer.
The Six Sigma methodology comprises five phases: Define, Measure, Analyze, Improve, Control. The Analyze phase examines the data collected during the Measure stage to identify any inefficiencies, defects, and discrepancies. Insights gleaned here will pave the way for process improvements for your team to implement.
What You’ll Learn in this Online Course
From measures of central tendency to measures of dispersion, this course will break down all of the things you need to know in about Descriptive Statistics.
This course will go over the meaning of descriptive statistics and how to use the appropriate techniques to summarize and describe data accurately. You will also be offered descriptive statistics examples in everyday life to help you better grasp the concept through the use of real-world examples.
What Is Descriptive Statistics?
Descriptive statistics is the process of organizing, analyzing, and reporting data. It includes the average, mean, mode, measure about the problem frequency, and the variability of the data.
Analysis of this data will help your team determine the scope of the problem and how often it occurs, as well as providing validation that a problem truly exists.
Any variation along your production process will impact your outcome, which makes the descriptive statistics, meaning the accurate reporting of your data, very useful.
You can measure the variations by focusing on these two characteristics: measures of central tendency and measures of dispersion.
Measures of Central Tendency
The measures of central tendency consist of two characteristics: the mean and the median.
The first characteristic is the mean, which is the arithmetic balance or average of a distribution. To calculate the mean, you need to add all the figures within a data set and then divide by the number of figures within the set. You can tell if your data is normally distributed by looking at it in the form of a histogram.
The second measure of central tendency is the median, which is the midpoint of a data set. To capture this, you need to arrange the numbers in ascending order, and then locate the number that lands in the middle of that data set. In the event that you have a data set made up of even numbers, you would simply average the two middle figures to calculate the median.
When referring to the mode of a data set, we’re describing the number in the set that occurs most often. The mode is most useful when dealing with attributes data and is usually the statistic used to create Pareto charts.
Measures of Dispersion
In descriptive statistics, dispersion is a way of describing how spread out a set of data is. It defines how the data disperses, stretches, or spreads in different categories. The three primary measures of dispersion are range, variance, and standard deviation.
Range is the difference between the largest and smallest observation in a data set and is generally used when the data is not normally distributed. In other words, when deciding to use the median as your central tendency because your data is skewed or you have outliers that appear to be driving the average up, you can use your range as the measure of dispersion.
Sample variance is the average squared distance between observation and the mean. It is used to check the deviation of data points with respect to the data’s average.
The final measure of dispersion is standard deviation, which is the square root of the sample variance.
Benefits of This Online Course
The main advantages of taking this course are being able to present data in a meaningful way by accomplishing the following tasks:
- Identifying numerical measures such as range, variance, and standard deviation.
- Learning how to use tables and charts to compare combinations of variables.
- Exploring the relationships between variables.
- Analyzing any issues you may encounter in interpreting your results.