Question

If you have 256 data points, how many classes (bins) would sturges' rule suggest

Answer 1

Sturges' rule suggests using 9 classes or bins for 256 data points.

Step-by-step explanation:

Sturges' rule is a method for determining the number of classes or bins required for a histogram. According to Sturges' rule, the formula to calculate the number of classes is:

K = 1 + log2(n)

Where K is the number of classes and n is the number of data points. In this case, since you have 256 data points, the number of classes suggested by Sturges' rule would be:

K = 1 + log2(256)

K = 1 + log2(28)

K = 1 + 8

K = 9

Therefore, Sturges' rule would suggest using 9 classes or bins for the histogram.

Answer 2

Since Sturge's Rule states that the optimal number of bins is equal to the 1 plus 3.3 log(n), where n is the number of data points, the estimated solution (since you can only have whole bins) is to provide 6 bins, each storing 42 to 43 data points.