To understand the Central Limit Theorem (CLT), let's use the example of rolling two dice, repeatedly (say 30 times). Then calculate the sample mean (mean of two dice values) and plot its distribution.
The concept of degrees of freedom (df) is fundamental and commonly used in statistical analysis. In this blog post, let us thoroughly understand this concept with examples.
Many a time, you might have encountered that the degrees of freedom for a particular test is, let's say (n-1) or (n-2) etc. In the blog post, let us understand this concept with examples.
A) Without any restriction
Suppose I give you three boxes as shown below. You are free to fill all these three boxes with any values of your choice. Hence, in this case, the degrees of freedom are 3. In other words, df=n here.
Imagine, in a large gathering, people were given an option to buy any one of the two products for free. You want to test if is there any relation between gender and buying patterns.
When variables are independent
You take a random sample of 40 persons: in which there were 10 men and 10 women. You asked them what did they buy? A pen or a pencil?
The cross-tabulated data is shown in the following table:
Imagine you own an online business. You want to improve how the options are presented to potential buyers. Hence, you make minor adjustments in the displaying options.
The t-test was developed by William Sealy Gosset, an English statistician and a beer brewer.
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Tableau is a data visualization and analysis tool which is popular among the data science community.
For better understanding, differences between Tableau and other similar tools are provided below.
In this blog let us understand the basics of the Z-score. This is useful to know how the data is distributed.
Let us start.
Examples:
Dataset A is : 10, 12, 14, 16.
Dataset B is: 10, 200, 350, 600.
As you can see, both the mean of B (290) and the standard deviation of B (13) are higher than that of A.
