Spring 2023


Course Info

  • Syllabus
  • Lectures: T. 1310-1500, R. 1610-1700 @ SE A316
  • Instructor: Yu-Ling Tseng.      Office Hours: T. 1200~1300  R. 1500~1600@SE A409
  • TA: 張秝穎 (二)1700~1800 @SE A418  戴 傳軒 (一)1700~1800  @SE A433 ~  有問題要多多請教助教喔   
  • 數學小天地   ~~ 討 論 各類 數學相關問題 的好地方 ~~
  • Prerequisites: Probability Theory, Statistics, Linear Algebra

  • 學 期只有17周上課時間,學校行事曆18周的彈性補課我們就安排如下。。。。

    上 課  上 到  第17周 0111 星期二  (星期二 13點整 開始上課 )

    然 後

    期 末考相關:

    I 上 機考試時間 第 17周 0608(星 期四):    15:50 ~ 16:45  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,
       請 提早
    (電腦教室15點開始 借) 到電腦教室挑選要用的電腦,並將你覺得需要的檔案先放到電腦桌面,方便考試時的參考。

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


    每 組代表應考同學已隨 機 抽選 如下:

    101 華詩詠 102 李郁青 103 陳子嘉 104 吳沁璇 105 陳楷捷  106 王伊涵  107 官旻萱
    108 黃寬勉 109 黃鈺翔 110 廖廣筑 111 謝翼仲 112 李錦州  113 許鴻源  114 簡嘉紘
    各 組 可 以 有 一 人 陪考

    II  紙筆考試時間 第18周 0613 (星期二):    13:10 ~ 15:00   A210 教室考

      0509 期中考成績枝葉圖
    人數: 55 (不計缺考人數)總分:110

      0 | 023557
      1 | 04
      2 | 023

      3 | 057
      4 | 344568999

      5 | 00122347
      6 | 011258
      7 | 0000111256666

      8 | 2337

      9 | 5


    Q1= 40,   Q2= 52,   Q3= 70.5
    平均:50.9      標準差: 24.9

    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

    Data sets in txt form

    (date discussed in class)  Programs
    (20230309)  SetUp(1) SetUp(1) shows you how to:
    1. devide graphical windows (not necessary if using R studio where you use the left program with extension .r)
    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
    (20230316 )   SetUp(2)

      ch01ta01 data
    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
    (20230418/0502)  MLR

    ch06fi05 data
    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

    ch6pr18 data
    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........
    (20230523/25) WLSE

    ch11ta01 data
    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 .

    ch3ta10 data
    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.
    20230601/0606 Yes's note

      You may find useful R programs  here:
    Assignments (NO LATE HOMEWORK IS ALLOWED!)                                                                                                           
    Problems  (作 業成績10%由各組派一代表和助教面試講解一題要用R程式回答的問題;題目採先選先贏制,講過題目其他組不得再選擇喔)
    Due date
    Ch.3 : 9

    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
    這 份作業不用交。
    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)


    這 份作業就 不 用 交 囉。
    但,同學 一 定  要  自 己  做過這些題目喔
    Ch.6 : 6 (c),  11 (b), 16 (b, c), 17, 19 因期中考將近,這份作業就不用交囉,同學請自行練習。
    These Problems  and
    Ch.2 : 27 , 28
    Ch.6 : 4, 5 (a, b), 6 (a, b), 7, 15 (c), 16 (a)(已 被選, 26
    Ch.6 : 2, 22, 23, 24, 25 0413
    Ch.1 : 19 (已 被選, 28 (已 被選,
    (已 被選
    Ch.2 :
    (已 被選, 8 (a, c), 10, 13(已 被選 , 23 (a, c)(已 被選
    Ch.5 : 17, 18. 19
    Ch.1 : 7, 8, 33, 34, 39 (a) , 41
    Ch.2 : 3, 17.

    20230214 First day of class

    課本:Applied Linear Statistical Models, Applied Linear Regression Models, 5th ed, Kutner, Nachtsheim, Neter and Li, 2019, McGRAW-HILL International. 洽華泰書局 周益彰 先生(0910-275877)

    1. Applied Regression Analysis, 2nd ed, Draper, N. R. and Smith, H., 1981, Wiley.
    2. Introduction to Linear Regression Analysis, 2nd ed, Montgomery, D. C. and Peck,     E., 1991, Wiley.

    Course Grade

    Homework (20%=10%+10%)  Midterm (30%)  Final (50%= 30%+20%)


    1. R website (original, mirror @ NTU)  
    2.     R ClassRoom 
    3. Document Reader: Acrobat Reader


    Last modified: 20200908