| > | restart: read `C:\\Tanis-Hogg\\Maple Examples\\stat.m`: read `C:\\Tanis-Hogg\\Maple Examples\\HistogramFill.txt`: read `C:\\Tanis-Hogg\\Maple Examples\\ScatPlotCirc.txt`: read `C:\\Tanis-Hogg\\Maple Examples\\ScatPlotPoint.txt`: with(plots): |
Elliot Tanis
June 12, 2006
Figures 5.4-2(a) and 5.4-2(b) (Fig5_4-2.mws)
Figures 5.4-2(a) and 5.4-2(b) Simulation of observations from an exponential distribution.
| > | randomize(): |
| > | for k from 1 to 1000 do X := [seq(-ln(1 - rng()), k = 1 .. 16)]: TT[k] := (Mean(X) - 1)/(StDev(X)/4): od: T := [seq(TT[k], k = 1 .. 1000)]: |
| > | tbar := Mean(T); |
| > | s := StDev(T); |
| > | Min(T), Max(T); |
| > | xtics := [seq(-7 + k*0.5, k = 0 .. 21)]: ytics := [seq(0.05*k, k = 1 .. 8)]: P1 := HistogramFill(T, -6.5 .. 3.5, 20): P2 := plot([[0,0],[0,0]], x = -6.5 .. 3.5, y = 0 .. 0.41, xtickmarks=xtics, ytickmarks=ytics, labels=[``,``]): display({P1, P2}); |
Similar to Figure 5.4-2(a)
| > | #zper := [seq(NormalP(0, 1, k/1001), k = 1 .. 1000)]: k := 'k': for k from 1 to 500 do zp[k] := NormalP(0, 1, k/1001): zp[1000-k+1] := -zp[k]: od: |
| > | zper := [seq(zp[k], k = 1 .. 1000)]: |
| > | Torder := sort(T): |
| > | ScatPlotPoint(Torder, zper); |
Similar to Figure 5.4-2(b)
| > |
| > | X := [seq(-ln(1 - rng()) + 9, k = 1 .. 16)]; Mean(X); StDev(X); |
The above are newly generated data. The following are those data in the text. We use the following at this time.
| > | X := [11.9776, 9.3889, 9.9798, 13.4676, 9.2895, 10.1242, 9.5798, 9.3148, 9.0605, 9.1680, 11.0394, 9.1083, 10.3720, 9.0523, 13.2969, 10.5852]; |
| > | xbar := Mean(X); sx := StDev(X); |
| > | probs := [seq(1/16, k = 1 .. 16)]; empPDF := zip((x,y)->(x,y),X,probs); |
| > | for k from 1 to 1000 do XX := DiscreteS(empPDF, 16): TT[k] := (Mean(XX) - 10.3003)/(StDev(XX)/4): od: Tre := [seq(TT[k], k = 1 .. 1000)]: tbar := Mean(Tre); s := StDev(Tre); Min(Tre), Max(Tre); |
| > | xtics := [seq(-6.5+0.5*k, k = 0 .. 20)]: ytics := [seq(0.05*k, k = 1 .. 8)]: |
| > | P1 := HistogramFill(Tre, -6.5 .. 3.5, 20): P2 := plot([[0,0],[0,0]], x=-6.5 .. 3.5, y = 0 .. 0.445, xtickmarks=xtics, ytickmarks=ytics, labels=[``,``]): display({P1, P2}); |
Similar to Figure 5.4-3(a)
| > | Treorder := sort(Tre): |
| > | ScatPlotPoint(Treorder, zper); |
Similar to Figure 5.4-3(b)
Now find the 0.025 and 0.975 quantiles
| > | c := Percentile(Tre, 0.025); d := Percentile(Tre, 0.975); |
| > | left := evalf(xbar - d*sx/4); |
| > | right := evalf(xbar - c*sx/4); |
| > | xbar; |
| > | sx; |
Hopefully "10" is in the interval
| > | [left, right]; |
| > |