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Which is the best test for uniformity in R?
Per whuber’s comment, the standard way to test for a particular discrete distribution (e.g., a uniform one – but also any other one) is the Pearson chi squared test. In R, use chisq.test (). A simulation:
How to perform a Kolmogorov Smirnov test in R?
To perform a one-sample or two-sample Kolmogorov-Smirnov test in R we can use the ks.test () function. This tutorial shows example of how to use this function in practice. The following code shows how to perform a Kolmogorov-Smirnov test on this sample of 100 data values to determine if it came from a normal distribution:
How are critical values of K-S test determined?
This is due to limitation 3 above (i.e., the distribution parameters are typically not known and have to be estimated from the data). So in practice, the critical values for the K-S test have to be determined by simulation just as for the Anderson-Darling and Cramer Von-Mises (and related) tests.
Is the K-S test based on the maximum distance?
The graph below is a plot of the empirical distribution function with a normal cumulative distribution function for 100 normal random numbers. The K-S test is based on the maximum distance between these two curves. Characteristics and Limitations of the K-S Test
What is the syntax for t test in R?
The basic syntax for t.test () is: t.test (x, y = NULL, mu = 0, var.equal = FALSE) arguments: – x : A vector to compute the one-sample t-test – y: A second vector to compute the two sample t-test – mu: Mean of the population- var.equal: Specify if the variance of the two vectors are equal.
How to relax the equal variance assumption in R?
One solution to relax the equal variance assumption is to use the Welch’s test. R assumes the two variances are not equal by default. In your dataset, both vectors have the same variance, you can set var.equal= TRUE. You create two random vectors from a Gaussian distribution with a higher mean for the sales after the program.
What is the p-value of one sample t test?
The p-value of the one sample t-test is 0.1079 and above 0.05. You can be confident at 95% that the amount of sugar added by the machine is between 9.973 and 10.002 grams. You cannot reject the null (H0) hypothesis.