多元线性回归 multiple linear regression ##例1:new.eg1 rm(list=ls()) setwd("/Users/sifan/R/datasets") dat <- read.csv("new.eg1.csv",header=T) dat ## x1 x2 x3 x4 y ## 1 5.68 1.90 4.53 8.2 11.2 ## 2 3.79 1.64 7.32 6.9 8.8 ## 3 6.02 3.56 6.95 10.8 12.3 ## 4 4.85 1.0...
简单线性回归 simple linear regression x <- c(60,62,64,65,66,67,68,70,72,74) y <- c(63.6,65.2,66,65.5,66.9,67.1,67.4,68.3,70.1,70) dat <- data.frame(x=x,y=y) plot(dat) fit <- lm(y~x) summary(fit) ## ## Call: ## lm(formula = y ~ x) ## ## Residuals: ## Mi...
1 Simple Linear Regression Load the data set pressure from the datasets package in R. Perform a Simple Linear Regres sion on the two variables. Provide the regression equation, coefficients table, and anova table. Summarize your findings. What is the relationship between the t statistic for temp...
2 Multiple Linear Regression Load the swiss data set from the ‘datasets’ package in R. Find the correlation matrix and print the pairwise scatterplots. What variables seem to be related? Run a Multiple Regression on Fertility using all of the other variables as predictors. Print the model ...
决定系数(Multiple R-squared) R^2 = 0.8782 = 87.82%,表示在销售收入取值的总误差中,有87.82%可以由销售收入与广告支出之间的线性关系来解析,模型的拟合程度较高。 模型给出的检验统计量(F-statistic) F = 129.8,P = 1.161e-09,P<0.05接近于0,表示销售收入与广告支出之间的线性关系显著。
A multiple linear regression (MLR) model that describes a dependent variable y by independent variables x1, x2, ..., xp (p > 1) is expressed by the equation as follows, where the numbers α and βk (k = 1, 2, ..., p) are the parameters, and ϵ is the error term. For ...
Multiple R-squared: 0.1701, Adjusted R-squared: 0.1653 F-statistic: 35.26 on 4 and 688 DF, p-value: < 2.2e-16 √输入4: install.packages("MASS")library(MASS)step <- stepAIC(linqol, direction="both") √结果4: Start: AIC=35...
在此單元中,我們將會使用「簡單線性迴歸」來對比多個線性迴歸。 我們也會查看名為 R2 的計量,此計量通常用來評估線性迴歸模型的品質。多元線性迴歸「多個線性迴歸」會將數個功能和一個變數之間的關聯性模型化。 在數學上,其與簡單線性迴歸相同,而且通常符合使用相同成本函式的功能,但有更多功能。
# Multiple Linear Regression Examplefit<-lm(y~x1+x2+x3,data=mydata)summary(fit)# show results # Other useful functionscoefficients(fit)# model coefficientsconfint(fit,level=0.95)# CIs for model parametersfitted(fit)# predicted valuesresiduals(fit)# residualsanova(fit)# anova tablevcov(fit)# ...
In the context of amultiple regressionanalysis, the question is how fuel economy depends on country of originafter controlling for other variables. Here, dummy variables have been created for the country codes to address this issue, and it turns out that the country effect places Japan in betwee...