General steps for executing parametric inference in R
- Data Collection: Begin by collecting and preparing your data. Data can be imported into R using various functions like read.csv() and read.table().
- Exploratory Data Analysis (EDA): Before proceeding to fit a parametric model, it is imperative to gain a comprehensive understanding of your data. Employ R functions like summary(), hist(), boxplot(), and scatterplots to visualize and summarize your dataset.
- Selection of a Parametric Model: Based on the insights gleaned during EDA, opt for a suitable parametric model that best characterizes the data’s distribution. For example, if your data exhibits characteristics resembling a normal distribution, you may opt for the normal distribution model.
- Parameter Estimation: Proceed to estimate the parameters intrinsic to the chosen parametric model from your dataset. R offers a variety of functions such as mean(), var(), and glm() (for more complex models) to facilitate parameter estimation.
- Hypothesis Testing: Conduct hypothesis tests to derive inferences regarding population parameters. Common hypothesis tests encompass t-tests, chi-squared tests, ANOVA, and others. R provides dedicated functions like t.test(), chisq.test(), and anova() for executing these tests.
- Confidence Intervals: Calculate confidence intervals to ascertain the probable ranges for population parameters. You can employ functions like confint() or craft custom code to accomplish this task.
- Model Assessment: Thoroughly evaluate the appropriateness of your parametric model’s fit to the data. This assessment involves utilizing diagnostic plots, conducting residual analysis, and applying goodness-of-fit tests.
- Drawing Inferences: Based on the outcomes of your analysis, formulate inferences pertaining to the underlying population. These inferences could include statements such as “there is substantial evidence to suggest that the population mean likely falls within a specific range” or “there exists statistical significance indicating a noteworthy difference between groups
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.