Using R for testing goodness of fit

Goodness of fit is a test used to find out whether a data sample fits a given distribution. In simpler terms, it is used to determine whether a sample of data representsthe data contained  in the population from where  the sample is taken. R has been used since time immemorial to test goodness of fit in data sets. Stick with us as our R assignment help experts explore this topic in detail.

Understanding how the goodness of fit works

The goodness of fit test is  used in businesses to help the management make informed decisions. The most commonly used test is Chi square, and to calculate it, one must first state the null and alternative hypotheses, determine the critical value, and choose a significance level. This test is exclusively used for sets of data categorized in classes (bins). To produce accurate results, one must have a sufficient sample size. To further understand how Chi square works, liaise with our testing goodness of fit homework help experts.

Tests  on goodness of fit topic

There are several goodness of fit tests that can be performed with R. Below are the most common ones explained by our R assignment help experts:

1.     Chi square

As stated above, Chi square is the most common goodness of fit test performed in R. It is used in discrete distributions such as the Poisson distribution and binomial distribution. But Chi square has its shortcomings. For instance, you can only use it on data that is stored in bins. If your data is not put into bins, then you will have to create a histogram or frequency table before carrying out the test. Another downside is that your sample must contain enough data for your approximations to be valid. Chi square is often confused with the Chi square test for independence which is another type of Chi square test. These two tests differ in the aspect that the test for independence studies two or more sets of data to determine the relationship between them. The goodness of fit on the other hand is used to check how a sample data fits a given population. Both tests are used hand in hand in R to get the most out of data.

2.     Kolmogorov-Smirnov

Also known as the test for normality, the Kolmogorov-Smirnov test is used to determine when it is unlikely to have a normal distribution. A sample data can be fitted to the initial population using a one sample Kolmogorov-Smirnov test or a two sample Kolmogorov-Smirnov test. The reason why this test is performed using a statistical program like R is that, calculating critical values for each distribution is not an easy task. Using R makes the identification of the tables of critical values much easier.

3.     Anderson-Darling

The Anderson-Darling test is a modification of the Kolmogorov-Smirnov test. Like the Kolmogorov-Smirnov test, it helps data analysts determine when it is not likely to have a normal distribution. It focuses more on how the distributions are deviated towards the tails.

4.     Shapiro-Wilk

The Shapiro-Wilk test is used to determine whether a random sample is derived from a population with a normal distribution. It is recommended for samples with larger data.

Understanding and completing assignments revolving around these tests can be an overwhelming task for students. But we are here to provide the necessary support. Just contact us for testing goodness of fit homework help.