To determine the number of classes (or bins) in a histogram, you can use methods such as Sturges' rule, which suggests using the formula (k = 1 + 3.322 \log(n)), where (n) is the number of data points. Another approach is the square-root choice, where the number of classes is simply the square root of the total number of observations. Additionally, the Freedman-Diaconis rule can be used, which takes into account the data's interquartile range. Ultimately, the choice may depend on the specific characteristics of the dataset and the level of detail desired.
try sqrt(N) where N represents the number of observations you have...
number of classes
central tendency become more obvious
central tendancy gets more obvious
you have to find the class size by: (max-min)/number of classes Then use that class size to setup the class ranges Then use the class ranges to determine the frequency a sample occurs in each class. make a chart using the class ranges and the sample frequencies to display the histogram
try sqrt(N) where N represents the number of observations you have...
number of classes
central tendency become more obvious
central tendancy gets more obvious
you have to find the class size by: (max-min)/number of classes Then use that class size to setup the class ranges Then use the class ranges to determine the frequency a sample occurs in each class. make a chart using the class ranges and the sample frequencies to display the histogram
by using your brain
highest value-lowest value/number of classes
4
Yes!
5
Yes.
No it is not. The ogive is a graph that represents the cumulative frequencies for the classes in a frequency distribution.