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Understanding how information is linked is key to being able to use your collected information to make decisions. While not all data is correlated, you can use correlated data to determine where changes may be necessary or where there may be potential for greater profit. To gain the greatest impact from your data, it’s important to recognize how the relationship between various plots of data can point to trends and patterns in your business or industry. Through awareness of these trends and patterns, you can make better decisions for the future success of your company.
In this course on Correlation, you will learn about correlations, distributions, and how they relate to information gathered through graph analysis and variables. We’ll look at ways to visualize your data, such as crosstabs and scatterplots, so you can better evaluate relationships between data points. Dealing with an entire population of data can be complicated, so we’ll also cover how using a sampling distribution is an efficient way to represent a subset of the entire data population.
This course on Correlation covers correlations, distributions, and how they relate to information gathered through graph analysis and variables. It looks at ways to visualize data, such as crosstabs and scatterplots, and covers how sampling distributions efficiently represent a subset of an entire data population.
Yes. One of the learning objectives is to recognize the difference between correlation and causation, and the course includes a lesson titled "Correlation is not Causation."
According to the course, correlated data can be used to determine where changes may be necessary or where there may be potential for greater profit, and awareness of trends and patterns helps you make better decisions for the future success of your company.
The lessons are Introduction; Crosstabs and Scatterplots; Correlation is not Causation; Sampling Distributions; Multiple Comparisons; and Test Your Knowledge.
Because dealing with an entire population of data can be complicated, the course covers how using a sampling distribution is an efficient way to represent a subset of the entire data population.