temp')R2_adj <- c()p_value <- c()for (i in env) { fit_stat <- summary(lm(dat[['fish']]~dat[[i]])) #一元线性回归 R2_adj <- c(R2_adj, fit_stat$adj.r.squared) #提取校正后 R2 p_value <- c(p_value, fit_stat$coeffici
Linear regression models have a special related measure called R2 (R-squared). R2 is a value between 0 and 1 that tells us how well a linear regression model fits the data. When people talk about correlations being strong, they often mean that the R2 value was large....
Multiple R-squared: 0.9692, Adjusted R-squared: 0.9688 F-statistic: 2330 on 3 and 222 DF, p-value: < 2.2e-16 当把自变量x2去掉后,自变量x3的T检验反而变大了,同时Adjusted R-squared变小了,所以我们这次调整是有问题的。 如果通过生产和原材料的内在逻辑分析,焦煤与焦炭属于上下游关系。焦煤是生产焦...
Multiple / Adjusted R-Square: For one variable, the distinction doesn’t really matter. R-squared shows the amount of variance explained by the model. Adjusted R-Square takes into account the number of variables and is most useful for multiple-regression.然后是R方和调整的R方，R方为这个模型能...
Frost, J. Multiple Regression Analysis: Use Adjusted R-Squared and Predicted R-Squared to Include the Correct Number of Variables. In The Minitab Blog. 2013. Available online: http://blog.minitab.com/blog/adventures-in-statistics-2/multiple-regession-analysis-use-adjusted-...
codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘’ 1 Residual standard error: 0.4697 on 116 degrees of freedom Multiple R-squared: 0.8281, Adjusted R-squared: 0.8207 F-statistic: 111.7 on 5 and 116 DF, p-value: < 2.2e-16 3.模型解释 从以上最终得到的模型(fit_2)可...
由Multiple R-squared: 0.8973可知,所以预测变量解释了89.73%的销售价格的方差。由Residual standard error: 791.7知估计标准误差为791.7,说明用以上三个预测变量来估计销售价格时,平均的估计误差为791.7元/平方米。 三、假设检验 在我的上一篇文章中,提到了线性回归分析需要满足的一些假设条件。如果各位朋友忘记了可以...
Multiple R-squared:为相关系数R^2的检验,越接近1则越显著。 Adjusted R-squared:为相关系数的修正系数,解决多元回归自变量越多,判定系数R^2越大的问题。 F-statistic:表示F统计量,自由度为(1,n-2) p-value:用于F检验判定,匹配显著性标记。 模型改进 ...
Multiple R-squared: 0.0148, Adjusted R-squared: -0.0468 F-statistic: 0.24 on 1 and 16 DF, p-value: 0.631 AIC(model.1) [1] 99.133 summary(model.2) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -9.00e+02 2.70e+02 -3.33 0.0046 ** ...
MultipleR-squared:0.8031,AdjustedR-squared:0.8001 F-statistic:267.8on3and197DF,p-value:0 我们可以使用confint()来获得一个模型的参数的置信区间。 一下是模型参数的置信区间: confint(model,level=0.95) 2.5%97.5% (Intercept)502.5676524.66261 poly(q,3)11919.27392232.52494 ...