Fall 2015


Course Info

  • Syllabus
  • Instructor: Yu-Ling Tseng.      Office Hours: T. 13:00 ~ 15:00  (理A409)
  • TA:  潘恩勤  office hour : R. 12:00~ 13:00 @SE A413

  • Prerequisites: Probability Theory, Statistics, Linear Algebra


    期末考將於 2016 0106 (星期三)舉行

    紙筆考試時間:    13:30 ~ 15:00 (考試教室:  理  D131 )

    II 上機考試時間:   15:10 ~ 15:50  在系電腦教室考
       每組 代表應考同學 請 務 必 以USB攜帶課本的所有資料檔案來考試。

       上機考試:open book, open notes, open web, open anything........

       每組代表應考同學由Yes 隨 機 抽選。



    人數: 42

      0 | 002558
      1 | 024469999
      2 | 123445559
      3 | 000355
      4 | 1359
      5 | 5
      6 | 1459
      7 | 1
      8 | 4
      9 | 6

    Q1= 19,   Q2= 25,   Q3= 43
    平均:30.6      標準差: 23.32

    註:成績顏色帶代表:紅色 警告 區,黃色 小心後通行 區, 綠色 安全通行

    (就像 紅綠燈 號誌 呀)

    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.

    (date discussed in class)  Programs

    (20150923)  SetUp(1)

    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

    (20151014)   SetUp(2) 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, 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
    7. quit R, again.
    (20151112, 1125)  MLR  MLR 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,

    With the Dwaine Studios examples
    3. do multiple linear regression
    4. get figures and tables in the textbook
    5. make basic scatter plots for M-L-R data analysis
    6. obtain the design matrix
    7. get (simultaneous) conf. intervals for reg. coefficients,  confidence/prediction  intervals for the mean response at given predictors's values
    8. get 3D scatterplots
    (20151125)GLT GLT 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)

    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;
    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 WLSE 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 .

    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)
      You may find useful R programs  here:
    Assignments (NO LATE HOMEWORK IS ALLOWED!)                                                                                                           
    Due date
    In R, sum((fitted.values(fm)-mean(y))^2), sum(residuals(fm)^2) give you SSR and SSE, respectively;
    where y denotes the response variable and fm is the fitted model obtained from lm(y~.....)

    Ch.10 :

    Ch.11 : 6 (a~f), 7(a~f), 13, 17
    但,同學 一 定  要  自 己  做過這些題目喔
    NOTE: When asked to draw a dot plot in this homework, you may draw the stem-and-leaf plot, instead. 
    In R, stem(x)
    gives you the stem-and-leaf plot of data in x.
    Ch.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)
    Ch.6 : 6 (c),  11 (b), 16 (b, c), 17, 19 1209 期中考,所以這作業不用交,請同學自行練習喔。
    These Problems  and
    Ch.2 : 27, 28
    Ch.6 : 4, 5 (a, b), 6 (a, b), 7, 15 (c), 16 (a), 26 
    Ch.6 : 2, 22, 23, 24, 25 1112
    Ch.1 : 19, 28, 45
    Ch.2 : 4, 8 (a, c), 10, 13, 23 (a, c)
    Ch.5 : 17, 18. 19
    Ch.1 : 7, 8, 33, 34, 39 (a) , 41
    Ch.2 : 3, 17.


    1. R website (original, mirror @ NTU)        
    2. R ClassRoom
    3. SimpleR (by John Verzani)
    4. Document Reader: Ghostview, Acrobat Reader


    Last modified: 150907