Hao's

#delimit;

recode V146B





(1=0)

(2=0.5)

(3=1.5)

(4=2.5)

(5=3.5)

(6=4.5)

(7=5.5)

(8=6.5)

(9=7.5)

(10=8.5)

(11=9.5)

(12=10.5)

(13=11.5)

(14=12.5)

(15=13.5)

(16=14.5)

(17=15.5)

(18=16.5)

(19=17.5)

(20=18.5)

(21=19.5)

(22=20.5)

(23=21.5)

(24=22.5)

(25=23.5)

(26=24.5)

(27=25.5)

(28=26.5)

(29=27.5)

(30=28.5)

(31=29.5)

(32=30.5)

(33=31.5)

(95=.)

(96=.)

(97=.)

(98=.)

, gen (hincome);



#delimit cr



sdtest hincomeb ,by (minnan)

ttest hincomeb ,by (minnan) unequal





recode V5 (1=1)(2=2)(3=3)(else=.), gen(ethnic)

recode V13A (1/5=1)(6/9=2)(10/18=3)(else=.), gen(education)

兩個類別之間的交叉表：

tab ethnic education





*依變項(反映變項)的敘述性統計按照因子水準（自變項的數值）來計算

tabstat

*ex-->tabstat miles, statistics( mean count max sum min ) by(car) columns(statistics)



oneway Anova:

*oneway miles cars, tabulate nomeans nostandard nofreq noobs



gen tvtime = v50b_1*60 + v50b_2



利用tabulate可以將一個k類別的類別變數的各組變成k個虛擬變數。

tabulate v2, gen(cohort)



Recode v2 (1=1)(else=0), gen(cohort1)

Recode v2 (2=1)(else=0), gen(cohort2)

Recode v2 (3=1)(else=0), gen(cohort3)



sum if midterm~=. & stata~=.



pwcorr來取得 pair-wise correlation

pwcorr midterm stata,obs star(0.05)





predict residual, resid

y-ybar

gen toty = midterm – 82.49091(mean)

(yhat – ybar)

gen explain = yhat - 82.49091



root MSE=Se

Abj R^=R^=1-Se/Sy

Ms residual=Se^2 =SSE/(n-k-1)

Total SS= Sy^2

T=(1,1)/(2,1)

(1,1)=b1



*excel ci Yhat

=H2-TINV(0.05,8)*$E$19*SQRT(1/10+(A2-$A$12)*(A2-$A$12)/($E$12))

=yhat-TINV(0.05,8)*Se*Sqrt(1/10+(Xi-$Xbar)*(Xi-Xbar)/(Xbar^2's mean)



Y's +1

=yhat-TINV(0.05,8)*Se*Sqrt(1+1/10+(Xi-$Xbar)*(Xi-Xbar)/(Xbar^2's mean)



*regress之後可以執行predict來儲存預測值及殘差值

predict newvarname , options

xb : fitted values; the default

residuals : residuals

stdp : standard error of the prediction--->Sqrt(1+1/10+(Xi-$Xbar)*(Xi-Xbar)/(Xbar^2's mean)

stdf : standard error of the forecast



tabstat income sen educ age, sta(mean sd max min) by(gender)

t=11.32, p-value this is no difference~



recode jstatus (1=1)(else=0), gen(prof)

recode jstatus (2=1)(else=0), gen(skilled)



gen educfem = educ*female interact within vars



regress income age agesqr ,beta

predict yhat, xb

list age yhat



gen lnincome=ln(income) get i in log~ be positive,