Table Of Contents

- Using R for testing goodness of fit
- Understanding how the goodness of fit works
- Tests on the goodness of fit topic

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## Using R for testing goodness of fit

The 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 represents the data contained in the population from where the sample is taken. R has been used since time immemorial to test the 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 the goodness of fit topic

There are several goodnesses 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 have 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.
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