The frequency distribution shows in a graph or a table all the possible values of a variable, called the random variable, and the frequency or the count of each value. For example, if you had the ages of 100 people you could do a frequency distribution and split the ages into 10 year categories and then show how many of the 100 people were in the 20s, how many in their 30s, how many in their 40s and so on.
Yes.
1) Ungrouped2) Grouped 3) Qualitative
to quickly and effectively represent data
Frequency and cumulative frequency are two types of frequency distributions. These are frequency tables that show statistical data for different types of frequencies that include absolute, relative, and cumulative frequencies. There are mathematical formulas used to calculate these frequencies.
Organizing the data into a frequency distribution can make patterns within the data more evident.
The Gaussian curve is the Normal distributoin curve, the commonest (and most studied) of statistical distributions.
Finding the average from the raw data requires a lot more calculations. By using frequency distributions you reduce the number of calculations.
The difference between frequency polygon and line graphs is their purpose. Frequency polygons are for understanding shapes distributions, while line graphs shows information that is related in some way.
There are many frequency distributions: Uniform, Binomial, Multinomial, Poisson, Gaussian, Chi-square, Student's t, Fisher's F, Beta, Gamma, Lognormal, Logistic to name some off the top of my head. And I am sure I've missed many more. You need to specify which ones you are interested in. Forgot the Exponential.
Yes, open-ended classes are allowed in frequency distributions. These classes do not have a defined upper or lower limit, which can be useful for representing data that extends indefinitely, such as income or age. However, while they can provide a general overview of data trends, they may limit the precision of statistical analysis since exact values are not specified.
No. There are many other distributions, including discrete ones, that are symmetrical.
Adjusted frequency in statistics is calculated to account for biases or irregularities in data sampling. The formula typically takes the form: [ \text{Adjusted Frequency} = \frac{\text{Observed Frequency} + \text{Adjustment}}{\text{Total Observations} + \text{Adjustment Factor}} ] where the adjustment can be a constant or derived from other statistical measures, depending on the context. This helps to provide a more accurate representation of frequency distributions.