Parametric Inference for Normal Distribution
Lets say you have a set of exam scores. You want to check if they follow a distribution. To do this you can utilize the Shapiro Wilk test, which’s a tool, in R. Here’s how you can perform parametric inference in R to assess normality:
R
# Sample data exam_scores <- c (85, 92, 78, 88, 95, 90, 87, 89, 82, 91) # Shapiro-Wilk test for normality shapiro.test (exam_scores) |
Output:
Shapiro-Wilk normality test
data: exam_scores
W = 0.96917, p-value = 0.883
By using the function you can test the hypothesis that the data conforms to a distribution. The results will provide a test statistic and a p value. If the p value is lower than your chosen significance level (, for example 0.05) it would imply rejecting the hypothesis and suggesting that the data does not exhibit a distribution.
Parametric Inference with R
Parametric inference in R involves the process of drawing statistical conclusions regarding a population using a parametric statistical framework. These parametric models make the assumption that the data adheres to a specific probability distribution, such as the normal, binomial, or Poisson distributions, and they incorporate parameters to characterize these distributions.
It is a technique that involves making assumptions, about the probability distribution underlying your data. Based on these assumptions you can then draw conclusions. Make inferences about population parameters. In the R programming language parametric inference is frequently employed for tasks such, as hypothesis testing and estimating parameters.