Analysis of statistical analysis functions and application case analysis in Commons Math

Commons Math is a statistical analysis library widely used in Java program development.It provides a series of powerful and easy -to -use statistical analysis tools, which can help developers carry out various statistical analysis tasks and provide a large number of application cases.This article will introduce some common statistical analysis functions in the Commons Math, and explain its application through cases. Commons math's statistical analysis function is mainly divided into the following aspects: 1. Description Statistical analysis: Commons Math can calculate the basic statistical indicators of a set of data, such as average, median, variance, collaborative differences, etc.Developers can use these functions to understand the distribution and characteristics of data. 2. Probability distribution: Commons Math supports various common probability distribution, including normal distribution, uniform distribution, Beta distribution, etc.Developers can use these distributions to generate random numbers, or calculate the probability value under specific distribution. 3. Assuming inspection: Commons Math provides some tools for assumptions for testing, such as T test, card testing, etc.Developers can use these tools to determine whether there are significant differences between the two sets of samples. 4. Return analysis: Commons Math supports linear regression analysis and non -linear regression analysis.Developers can use these functions to fit the data and get the fitting curve equation or model. The following cases are used to illustrate the application of Commons Math. Case: Use Commons Math for a linear regression analysis Suppose we have a set of data of height and weight, we want to predict a person's weight through a linear regression analysis.Now we use Commons math to analyze. First of all, we need to construct the height and weight data into a Realmatrix object: double[][] data = {{160, 55}, {165, 60}, {170, 65}, {175, 70}, {180, 75}}; RealMatrix matrix = MatrixUtils.createRealMatrix(data); Then, we use the OlsmultipleLinerregression class for regression analysis: OLSMultipleLinearRegression regression = new OLSMultipleLinearRegression(); regression.newSampleData(matrix.getColumnVector(1).toArray(), matrix.getColumnVector(0).toArray()); double[] coefficients = regression.estimateRegressionParameters(); double coefficientOfDetermination = regression.calculateRSquared(); Finally, we can get the regression coefficient and decision coefficient, so that we can predict the weight based on the height of the given body: double height = 185; double weight = coefficients[0] + coefficients[1] * height; System.out.println("Predicted weight: " + weight); Through the above code, we can use the Commons Math for a simple linear regression analysis and predict weight based on height. In summary, Commons Math is a powerful and easy to use Java statistical analysis library that can help developers perform various statistical analysis tasks.Its extensive application cases cover the description of statistical analysis, probability distribution, assumption testing, and regression analysis.Developers can choose the corresponding functions according to their needs, and use the Commons Math to complete complex statistical analysis tasks.