2、ependent variable的数目, 不要求计算标准误 sbi.3.线性模型的显著性检验:1)Total sum of squares (SST) = (Y E(Y)2,Y 观测值;Regression sum of square (RSS) = (y E(Y)2,y 拟合值;Sum of squared error (SSE) = (Y - y)2;SST = RSS + SSE.2)方差分析表 (ANOVA):Source of variat...
即ANOVA表中最后一列,Mean Square of Regression / Mean Square of Residual 材料1-5 材料1-5探究了Oil Returns对Amtex股价的影响。结果显著为正。 27:考察OLS的假设。不要求因变量服从正态分布 28:考察SEE的定义式,即SEE={Σ[(Y-Yhat)^2]/(n-k-1)}^0.5,其中Σ[(Y-Yhat)^2]是Sum of Squared ...
1) Total sum of squares (SST) = Σ(Y – E(Y))2, Y – 观测值; Regression sum of square (RSS) = Σ(y – E(Y))2, y – 拟合值; Sum of squared error (SSE) = Σ(Y - y)2; SST = RSS + SSE. 2) 方差分析表 (ANOVA): ...
1)Total sum of squares (SST) =Σ(Y–E(Y))2,Y–观测值; Regression sum of square (RSS) =Σ(y–E(Y))2,y–拟合值; Sum of squared error (SSE) =Σ(Y - y)2; SST = RSS + SSE. 2)方差分析表(ANOVA): Source of variation Sum of squares Degree of freedom Mean sum of square Re...
决定系数(coefficient of determination) R^2 = \frac{SSR}{SST} = 1 - \frac{SSE}{SST} 。修正拟合优度 AdjR^2 = 1-\frac{n-1}{n-k-1}(1-R^2) \leq R^2 ,修正拟合优度可能小于0。 7、均方回归(MSR, mean square regression)与均方误差(MSE, mean square error),其中 k 为自变量的数量...
3. 线性模型的显著性检验: 1) Total sum of squares (SST) = Σ(Y – E(Y))2, Y – 观测值; Regression sum of square (RSS) = Σ(y – E(Y))2, y – 拟合值; Sum of squared error (SSE) = Σ(Y - y)2; SST = RSS + SSE. 2) 方差分析表 (ANOVA): Source of variation Sum ...
DegreeofSumof Meansum variation freedom squares ofsquares 2-332 专业来自百分百的投入 金程教育.GFEDU.NET 专业·领先·增值 Regression (explained) Error k=1 n‐2 RSS SSE SST RSS MSR= K SSE MSE=n−2 (unexplained) Total n‐1 1.6CoefficientofDetermination: Explainedvariation(RSS)SST-SSE R2= ...
Regression coefficients are biased and inconsistent, lack of confidence in hypothesis tests of the coefficients or in the model predictions. 1.10 Detecting Conditional heteroskedasticity: BP chi‐square test = n 2 resid R× (df = k) Where: ...
10. Autoregressive (AR) Models: Specified correctly if autocorrelation of residuals not significant: xt ? b0 ? b1 xt ?1 ? b2 xt ? 2 ? ... ? bp xt ? p ? ? t 11. Mean Reverting Level For AR(1): b0 (1 ? b1 ) 12. RMSE: square root of avg squared error. 13. Random Walk ...
: 3. 线性模型的显著性检验: 线性模型的显著性检验 2 2 1) Total sum of squares (SST) Σ(Y – E(Y)) , Y – ; 1) Total sum of squares (SST) Σ(Y – E(Y)) , Y – 观测值; 观测值 2 2 Regression sum of square (RSS) Σ(y – E(Y)) , y – ; Regression sum of square...