Regression

Spring 2026

MandH

Course Info

  • Syllabus
  • Lectures: T. 1310-1500, R. 1610-1700 @ SE A211
  • Instructor: Yu-Ling Tseng.      Office Hours: T. 1200~1300  R. 1500~1600@SE A409
  • TA:   黃煜霖  Office Hour  F. 1100~1200 @ SE 408  ~  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 
  • YesKno時間   有點迷路時聽聽第一堂課的也許有幫助,請直接收聽第29集: https://play.kkbox.com/podcast/episode/9XiNiBkrgcwXNDx5MA


  •     

    期 末考相關:

    I 上 機考試時間  0611 或 0618(星 期四)(視進度):    16:10 ~ 16:50  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,
       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,於0602課堂上隨 機 抽選


    各 組 可 以 有 一 人 陪考


    II  紙筆考試時間 0616 (星期二):    13:00 ~ 15:00   A211 教室考
        同學請攜帶計算機應考

     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

    0512 期中考成績枝葉圖
    人 數: 30 (不計缺考人數)總分:115

      0 | 00225678
      1 | 4557
      2
    |
      3 | 045
      4 | 2348

      5 | 226
      6 |
      7 | 69

      8 | 35

      9 | 5
    10| 25
    11| 5

     
    Q1= 8,   Q2= 38.5,   Q3= 76
    平均:41.07      標準差: 36.57

    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


    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~


    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 

    星期 二  都 13 點 整 開始上課  ~~大家一起加油喔~


    *******

    Data sets in txt form
    https://drive.google.com/drive/folders/1rcQeNvDQ2d8OzBcYPBy9ydeB874ss6p-?usp=sharing


    (date discussed in class)  Programs
    Description  Code with Output in HTML
    (20260305)  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
    SetUp1html
    (20260326/31 )   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. construct 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
    SetUp2html

    SetUp2pdf
    (20260505)  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
    MLRpdf
    (20260507)GLT

    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)
    GLTpdf
    (20260521) 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........

    (20260528)varstabtrans

    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.
    varstabtranspdf


      You may find useful R programs  here:
                   
    Assignments (NO LATE HOMEWORK IS ALLOWED!)                                                                                                           
    Date
    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
     
    助教office hour:F. 1100~1200 @ SE 408    時 間若不行請另行和助 教email約時間)
    Due date
    0526 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)
    0602
    0505
    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。
    0423
    These Problems  and
    Ch.2 : 27 , 28
    Ch.6 : 4, 5 (a, b), 6 (a, b), 7, 15 (c)(已被選), 16 (a), 26
    0507
    0414
    Ch.5 : 17, 18. 19
    Ch.6 :
    2, 22, 23, 24, 25
    0423
    0326
    Ch.1 : 19(已被選) , 28(已被選) , 45
    Ch.2 :
    4
    (已被選), 8 (a, c)(已被選), 10, 13 , 23 (a, c)
    0409
    0310
    Ch.1 : 7, 8, 33, 34, 39 (a) , 41
    Ch.2 : 3, 17
    0326

    20260224 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)

    參考書目:
    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%)



    Computing                                                                                               

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

    Murray_0408

    Last modified: 20200908
    yltseng@mail.ndhu.edu.tw