stock prices of Korean financial companies--"sh"."kb" and "wr"
stock indices "KS" and "DJ".
下面是我用R做的,可是我不会分析结果,谁能告诉我
> ( res<- lm(shm~ksm+djm,data=mdata ) )
Call:
lm(formula = shm ~ ksm + djm, data = mdata)
Coefficients:
(Intercept) ksm djm
-1.1057 0.3272 1.0115
> anova(res)
Analysis of Variance Table
Response: shm
Df Sum Sq Mean Sq F value Pr(>F)
ksm 1 33.825 33.825 5175.8 < 2.2e-16 ***
djm 1 10.559 10.559 1615.7 < 2.2e-16 ***
Residuals 715 4.673 0.007
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> coef(res)
(Intercept) ksm djm
-1.1056836 0.3271607 1.0115317
> prx<-exp(predict(res))
> matplot(cbind(sh,prx),type="ll",col=c("red","black"))
> ( res<- lm(kbm~ksm+djm,data=mdata ) )
Call:
lm(formula = kbm ~ ksm + djm, data = mdata)
Coefficients:
(Intercept) ksm djm
-2.495 0.349 1.172
> anova(res)
Analysis of Variance Table
Response: kbm
Df Sum Sq Mean Sq F value Pr(>F)
ksm 1 43.528 43.528 2434.2 < 2.2e-16 ***
djm 1 14.173 14.173 792.6 < 2.2e-16 ***
Residuals 715 12.786 0.018
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> coef(res)
(Intercept) ksm djm
-2.4950005 0.3489978 1.1719284
> prx<-exp(predict(res))
> matplot(cbind(kb,prx),type="ll",col=c("red","black"))
> ( res<- lm(wrm~ksm+djm,data=mdata ) )
Call:
lm(formula = wrm ~ ksm + djm, data = mdata)
Coefficients:
(Intercept) ksm djm
-7.9184 0.6368 1.3835
> anova(res)
Analysis of Variance Table
Response: wrm
Df Sum Sq Mean Sq F value Pr(>F)
ksm 1 78.156 78.156 2877.6 < 2.2e-16 ***
djm 1 19.754 19.754 727.3 < 2.2e-16 ***
Residuals 715 19.420 0.027
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> coef(res)
(Intercept) ksm djm
-7.9184349 0.6367976 1.3835410
> prx<-exp(predict(res))
> matplot(cbind(wr,prx),type="ll",col=c("red","black"))
>