Syllabus Instructor: Yu-Ling Tseng.Office Hours: W. 12:00 ~ 13:00(理A409)Prerequisites:Probability Theory, Statistics, Linear Algebra

R Sample Programs ( in text format unless specified otherwise) : You will need to use R for most assignments (find and use the respective data set for each problem in the CD provided with your textbook) & you should find these sample programs quite useful.

Programs
(file name gives the date of its use in class) |
Description |

SetUp(1) SetUp(2) |
SetUp(1) shows you how to: 1. devide graphical windows 2. assign variables/sequences 3. plot and make titles for the graph 4. generate normally distributed observations/make histograms 5. do simple regressions and related plots (you should see the effect of sample size and s.d. on the fitted reg. lines) 6. quit R SetUp(2) shows you how to: 1. scan a data set in your working directory into R 2. get figures and tables in the textbook with the Toluca Company examples 3. construct confidence intervals for reg. coeff.'s 4. quit R, again. |

ConfInt | ConfInt shows you ,with the Toluca Company examples, how to 3. construct confidence intervals for reg. coeff.'s, confidence/prediction intervals for the mean response at given predictor x's values 4. construt conf. band for the entire reg line, 5. overlay three type of intervals in one plot for better comparison 6. save the output to a file for preparing your homework, or save the R commands for later use |

MLR1 | This program shows you how to 1. some basic matrix operations in R, 2. how to obtain the design matrix after fitting a simple linear regression model, 3. do multiple linear regression 4. get figures and tables in the textbook with the Dwaine Studios examples 5. make basic scatter plots for M-L-R data analysis 6. obtain the design matrix |

MLR2 MLR3 | MLR2 shows you ,with Awaine Studios example again, how to get (simultaneous) conf. intervals for reg. coefficients, confidence/prediction intervals for the mean response at given predictors's values MLR3 shows you how to 1. obtain SSEs from the full model and the reduced model 2. obtain F(1-alpha; m, k) in R 3. use the general linear test approach for testing certain hypotheses (by giving 3 examples) |

ResidPlot | This program ResidPlot 1. let you get a feeling as how a random sample of size n from N(0, 1) would look like in time sequence plots, and in histograms; and 2. with simulated regression data..... shows you some basic residual-plots for diagnostic in a regression analysis Please note that how a violation of certain assumptions made in reg model affect the display........ |

WLSE | This program shows you how to do W.L.S.E. when non-constant variances
occurs...... esp. shows you how to get figures and tables in the textbook with Blood Pressure Example on p427 . |

varstabtrans | This program varstabtrans illustrates a complete process when
analyzing a real data set with nonconstant variance problem........ Instead on using W.L.S.E. (which is covered in WLSE), we try transformations when nonconstant variances occur in this program. We run through the Case Example -- Plutonium Measurement on p 141 of textbook. Esp. you learn how to delete some data points from a data set, how to update model , and how to get basic diaqnostics residual plots. |

R ClassRoom

SimpleR (by John Verzani)

Data Sets of KNNL (5thED, .txt format)

Yes 公佈欄： You may find useful R programs here:

Topics for presentations: 0603 或 0606 報告。 各組報告時間 35～45 分鐘

1. 7.1 Extra sum of squares, p. 256 ~ p. 262

7.2 Use of extra sum of squares in tests for regression coefficients, ~ p. 265

曾祥恩、黃維平、張惠雯、林政彣

11.2 Multicollinaerity remedial measures-Ridge regression, p. 431~436

組員: 阿兩 房雨蓉 肥羊

p. 313~323

Assignments

Date |
Problems |

0523 | Ch.6 : 5 (a, b), 6 (a,
b, c), 10 (a), 11 (a, b, c), 15 (c), 16 (a, b, c,), 17, 19Ch.3 : 3 (a, b, c, d.) (For d, only
need to prepare a normal probability plot, i.e. the Q-Q plot)4 (a, b, c, d, e, f, h) (For e, only need to prepare a normal probability plot, i.e. the Q-Q plot) 6 (a, b, c.) ( For c, only need the Q-Q plot) 8 (a, b, c, d). ( Only Q-Q plot for d) 9 |

0418 | Ch.2 : 3, 4, 8 (a, c), 10,
13, 16, 17, 23 (a, c), 30Ch.6 : 2,
4, 7, 22, 23, 24, 25, 26 |

0415 | problems given in 0415's class |

0411 | Ch.1 :
45Ch.5 : 17, 18, 19 |

0307 | Ch.1 :
8, 20, 27, 30,33, 34, 35, 36, 39 (a) , 40, 41 |

Computing

- R website (original,
mirror
@ NTU)

- R ClassRoom
- SimpleR (by John Verzani)
- Document Reader: Ghostview, Acrobat Reader