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# Tarea Inicial Series

fabimatho Sep 10th, 2019 (edited) 123 Never
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1. # Datos #
2.
3. rinden=c(0:2002,153383,169376,176314,211261,216892,242130,251634,250758,231172,233302,232861,247291);
4. postulan=c(0:2002,66947,76292,77765,87617,81909,92154,87875,84512,106719,118208,119161,125588);
5. seleccion=c(0:2002,52431,56547,58329,62188,63045,64784,67013,67803,93574,95300,95568,97620);
6.
7. # Gráficos #
8. ts.plot(rinden,main="Chilenos que rinden la PSU",xlim= c(2004,2015),ylim=c(0,260000),col="blue");
9. ts.plot(postulan,main="Chilenos que postulan a una carrera universitaria",xlim=c(2004,2015),ylim=c(0,260000),col="red");
10. ts.plot(seleccion,main="Chilenos seleccionados en una carrera universitaria",xlim=c(2004,2015),ylim=c(0,260000),col="purple");
11.
12. # Diferenciar la serie #
13.
14. dif.rinden=diff(rinden)
15. dif.postulan=diff(postulan)
16. dif.seleccion=diff(seleccion)
17.
22.
23. #Funciones de autocovarianza, autocorrelación y autocorrelación parcial#
24.
25. acov.rinden <-acf(dif.rinden, type ="covariance", plot = FALSE);
26. acov.postulan <-acf(dif.postulan, type ="covariance", plot = FALSE);
27. acov.rinden <-acf(dif.seleccion, type ="covariance", plot = FALSE);
28.
29. acor.rinden <-acf(dif.rinden, type ="correlation", plot = FALSE);
30. acor.postulan <-acf(dif.postulan, type ="correlation", plot = FALSE);
31. acor.rinden <-acf(dif.seleccion, type ="correlation", plot = FALSE);
32.
33. acorp.rinden <-pacf(dif.rinden, type ="correlation", plot = FALSE);
34. acorp.postulan <-pacf(dif.postulan, type ="correlation", plot = FALSE);
35. acorp.rinden <-pacf(dif.seleccion, type ="correlation", plot = FALSE);
36.
37. plot(acov.rinden)
38. plot(acov.postulan)
39. plot(acov.rinden)
40.
41. plot(acor.rinden)
42. plot(acor.postulan)
43. plot(acor.rinden)
44.
45. plot(acorp.rinden)
46. plot(acorp.postulan)
47. plot(acorp.rinden)
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