Ever hear this rule of thumb: “The Chi-Square test is invalid if we have fewer than 5 observations in a cell”.
I frequently hear this mis-understood and incorrect “rule.”
We all want rules of thumb even though we know they can be wrong, misleading, or misinterpreted.
Rules of Thumb are like Urban Myths or like a bad game of ‘Telephone’. The actual message gets totally distorted over time.
Data Cleaning is a critically important part of any data analysis. Without properly prepared data, the analysis will yield inaccurate results. Correcting errors later in the analysis adds to the time, effort, and cost of the project.
No, degrees of freedom is not “having one foot out the door”!
Definitions are rarely very good at explaining the meaning of something. At least not in statistics. Degrees of freedom: “the number of independent values or quantities which can be assigned to a statistical distribution”.
This is no exception.
In the world of statistical analyses, there are many tests and methods that for categorical data. Many become extremely complex, especially as the number of variables increases. But sometimes we need an analysis for only one or two categorical variables at a time. When that is the case, one of these seven fundamental tests may come in handy.
These tests apply to nominal data (categories with no order to them) and a few can apply to other types of data as well. They allow us to test for goodness of fit, independence, or homogeneity—and yes, we will discuss the difference! Whether these tests are new to you, or you need a good refresher, this training will help you understand how they work and when each is appropriate to use.
The scatterplot is a simple display of the relationship between two, or sometimes three, variables. You have a wide range of options for displaying a scatterplot. In particular, you can control the location, size, shape, and color of the points in your scatterplot.
One activity in data analysis that can seem impossible is the quest to find the right analysis. I applaud the conscientiousness and integrity that underlies this quest.
The problem: in many data situations there isn’t one right analysis.