rstudio regression analysis

equation of the original variable y and x: (Situps-mean (situps))/sd (situps) =-0.13846688* (Weight-mean (weight))/sd (weight)- 0.52444579* (Waist-mean (waist))/sd (waist) -0.08542029* (Pulse-mean (Pulse))/sd (Pulse)--GT;SITUPS=SD (situps) [- 0.13846688* (Weight-mean (weight))/sd (weight) -0.52444579* (Waist-mean (waist))/sd (waist) -0.08542029* (Pulse-mean ( Pulse))/sd (pulse)]+mean (waist)SD (y[,2]) *-0.1384668393/sd (x[,1]) #weight的回归系数SD (y[,2]) *-0.52444579/sd (x[,2]) #waist的回归系数SD (y[,2])

. Because R2 >0.99, so this is a very obvious experimental model of linear characteristics, that is, the fitting line can be explained by more than 99.99%, covering the measured data, has a good general, can be used as a standard work curve for other unknown concentration solution measurement. To further use more metrics to describe this model, we use the "regression" tool in data analysis to analyze this

As the saying goes, every day an apple, the doctor away from me ~ Thanks to the peasant uncle's hard work, let us all four seasons can eat apples. So how can apple trees grow more fruit?How do you know whether these two factors have an effect on the number of apple trees as a result of the number of results that are known to each apple tree and the average daily sunshine time and watering times during their planting? Regression

Principle and application of Ridge regression technologyauthor Ma WenminRidge regression analysis is a biased estimation regression method dedicated to collinearity analysis, which is essentially an improved least squares estimation method, which is more consistent with the

Regression analysis is a statistical analysis method to study the quantitative relationship between two or more variables, which is widely used in many industries. Whether in the banking, insurance, telecommunications and other service industry business analysts in the database marketing, fraud risk detection, or semiconductor, electronics, chemical, pharmaceutic

#-*-Coding:utf-8-*-"""Created on Sat 18 11:08:38 2018@author: Acadsoc"""Import Pandas as PDImport NumPy as NPImport MatplotlibImport Matplotlib.pyplot as PltFrom pyecharts import Bar, line, Page, overlapImport Statsmodels.api as SMFrom sklearn.preprocessing import Standardscaler# import PymssqlFrom Dateutil Import ParserImport CopyImport OSImport SysFrom featureselection import featureselectionPlt.style.use (' Ggplot ') # set GGPLOT2 paint style# Set the text body path based on different platfor

deviation of B, and then according to the pre-set significance level U (usually u = 0.05) and degrees of freedom (D = n-2), query the t distribution table to obtain a critical value of Tu/2, if | TB |> tu/2, it indicates that the probability of regression coefficient β = 0 is less than 0.05, And we can conclude that β = 0, that is, Y has a linear relationship with X. Otherwise, the conclusion is the opposite. Calculated: | TB | = 19.78057827 tu/2 =

article describes the Microsoft Linear regression analysis algorithm, the principle and the Microsoft Neural Network analysis algorithm, just like the focus is not the same, the Microsoft Neural Network algorithm is based on a certain purpose, using the existing data for "probing" analysis, focusing on

The function of linear regression analysis in R is LM ().(1) Unary linear regressionWe can analyze whether the strength of the alloy is related to the carbon content according to the above data.First read the data into R using the following command:x Y Plot (x, y)Draw to get a linear relationship between x, y two variablesTherefore, the LM () function can be used to fit the line, and the

The first satisfying condition of linear regression is the linear relationship between the dependent variable and the independent variable, and then the fitting method is based on it, but if the dependent variable and the independent variable are nonlinear, then the nonlinear regression is needed to analyze it.There are two processes that can be called in the nonlinear