组间平方和 (SSR:Sum of squares of the regression):预测数据与标签均值之间差值的平方和。 公式:SSR = \Sigma (\bar{y_{i}}-\hat{y_{i}})^{2} 离差平方和(SST:Total sum of squares):实际值和均值之间的差值的平方和。 公式:SSR = \Sigma (\bar{y_{i}}-y_{i})^{2} 得到,R^{2}=\f...
1·SST(Sum of Square Total):(Yi为实际值,Ybar为均值) 2·SSR(Sum of Square Regression):(Yhat为预测值) 3·SSE(Sum of Square Error):(下图也表示了这三者之间的关系) 计算出上述值之后,我们在实际中常用RMSE去评价(越接近于1越好): Ps:当然还有MAE、MSE、(R‘)^2等可以很好的评价这个模型的好坏。
回归平方和(SSR,Sum of Squares forRegression):因变量的回归值(直线上的Y值)与其均值(给定点的Y值平均)的差的平方和,即,它是由于自变量x的变化引起的y的变化,反映了y的总偏差中由于x与y之间的线性关系引起的y的变化部分,是可以由回归直线来解释的 残差平方和(又称误差平方和,SSE,Sum of Squaresfor Error)...
SSE—Sum of squared errors numeric value SSR—Regression sum of squares numeric value SST—Total sum of squares numeric value Fitting Method Robust—Robust fit information structure Steps—Stepwise fitting information structure Input Data Formula—Model information ...
需要了解概念SST(总平方和), SSR(回归平方和),SSE(残差平方和), R^2 (判定系数)= SSR/SST R^2∈[0,1] 估计标准误差 了解的概念:估计标准误差(standard error of estimate),是均方残差(MSE)的平方和.MSE是度量实际观测值与回归观测值的差异程度。
function prodict = Linear_Regression(X,Y) x = sym('x'); n = max(size(X)); %%定义画图窗格属性 h = figure; set(h,'color','w'); %%回归相关值 XX_s_m = (X-Expection(X,1))*(X-Expection(X,1))'; XY_s_m = (X-Expection(X,1))*(Y-Expection(Y,1))'; ...
simplelinearregressionmodelcanbegiveby:SourcedegreesoffreedomSumsofSquaresMeanSquaresF-StatisticRegression1SSRMSR=SSR/1F=MSR/MSEErrorn–2SSEMSE=SSE/(n–2)Totaln–1Variation iny(SST)32UsingtheRegressionEquation…Wecoulduseourregressionequation: y=17.250–.0669xtopredictthesellingpriceofacarwith40(40,000)...
1、学习-好资料Class4:Inferenceinmultipleregression.I.TheLogicofStatisticalInferenceThelogicofstatisticalinferenceissimple:wewouldliketomakeinferencesaboutapopulationfromwhatweobservefromthesamplethathasbeendrawnrandomlyfromthepopulation.Thesamples'characteristicsarecalled"pointestimates."Itisa 2、lmostcertainthatthe...
Since Y and e are independent (i.e. cov(Y,e) = 0), var(Y ) = var(Y + e) = var(Y) + var(e) This leads ton∑i=1(Yi Y)2 =n∑i=1(Yi Y)2 +n∑i=1e2i 18Decomposing the Variance – ANOVA TablesSSR: Variation in Y explained by the regression line. SSE: Variation in...
Regression K SSR =Σ(Yˆj−Y¯)2 MSR = SSRK Error N–K–1 SSE = Σ(Yj−Yˆj)2 MSE =SSEN−K−1 The coefficient of determination is (1-54)r2=1−SSESST and the correlation coefficient is (1-55)r=(1−SSESST)0.5 The test statistic is the F - ratio, which we ...