1303_0506sem2

1303_0506sem2 - THE UNIVERSITY OF HONG KONG DEPARTMENT OF...

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Unformatted text preview: THE UNIVERSITY OF HONG KONG DEPARTMENT OF STATISTICS AND ACTUARIAL SCIENCE STAT1303 Data Management May 30, 2006 Time: 9:30 a.m. - 11:30 a.m. Candidates taking examinations that permit the use of calculators may use any cal- culator which fulfils the following criteria: (a) it should be self-contained, silent, battery-operated and pocket-sized and (b) it should have numeral-display facilities only and should be used only for the purposes of calculation. It is the candidate’s responsibility to ensure that the calculator operates satisfactorily and the candidate must record the name and type of the calculator on the front page of the examination scripts. Lists of permitted/prohibited calculators will not be made available to candidates for reference, and the onus will be on the candidate to ensure that the calculator used will not be in violation of the criteria listed above. Answer ALL questions. Marks are shown in square brackets. An abridged version of SAS syntax is provided in ANNEX 2. 1. Each of the programs in (a) to (e) create data set OUT. Write down the contents of OUT after each program in (a) to (e) is run. (a) The program: data out; set filel; if _n_=1 then var6=0; var6=var6+var2; if var1>10; retain vars; drop varl var2; run; The data set FILEl contains: [4 marks] S&AS: (b) STAT1303 Data Management The program: data out; merge file2 (rename=(var1=var4)) file3 (rename=(var6=var2)); by var4; run; The data set FILEZ contains: The data set FILE3 contains: -- The program: data out; array readingCS) a b c d e; do j=1 to 5; set file4; reading(j)=number; end; output; drop 3'; run; [4 marks] S&AS: STAT1303 Data Management 3 The data set FILE4 contains: [4 marks] ((1) The program: data out (where=(var1<=8)); set files; set file6; var1=var1+var4; run; The data set FILES contains: [4 marks] S&AS: (e) The program: data out; STAT1303 Data Management 4 input (D1 continent $ @21 country $21-36 (041 city :$20.; /* 12345678901234567890 1234567890 123456789012345678901234567890 */ cards; Asia Australasia Africa Europe North America run; Bangladesh Dhaka New Zealand Wellington South Africa Cape Town Denmark Copenhagen Canada Ottawa [4 marks] [Total: 20 marks] 2. The data set ADDCHILD contains information about 359 children who dur- ing childhood had exhibited symptoms of attention deficit disorder (ADD). Three ADD scores were obtained for each child at primary two, four and five. The three scores were averaged to produce the overall ADDSC score. The higher the score, the more ADD-like behaviours the child exhibited. At the end of secondary school years, information on the performances of these chil— dren were obtained from school records. A description of the variables in the data set ADDCHILD follows: Variable Name Brief description and coding scheme ADDSC SCORE2 SCORE4 SCORE5 GENDER IQ LANGG GPA SOCPROB Numeric Numeric Numeric Numeric Numeric Numeric Numeric Numeric Numeric The average of the three ADD-like behaviour scores The ADD-like behaviour score obtained in Primary 2 The ADD-like behaviour score obtained in Primary 4 The ADD-like behaviour score obtained in Primary 5 1 = male; 2 = female IQ obtained from a group-adminstered IQ test Grade in Chinese in Form 3: 4 = A; 3 = B; 2 = C; 1 = D Grade point average in Form 3 Social problem in Form 3: 1 = Yes; 0 = N0 S&AS: STAT1303 Data Management 5 (a) For each of the following variables, which statistical measures would you ‘ choose to describe the central location and dispersion of the data? (i) SOCPROB, (ii) LANGG, (iii) IQ. [3 marks] (b) The ADDSC score is the average of three ADD scores (stored in variables SCOREQ, SCORE4, and SCORES). There may be missing values in the three ADD scores. They are ignored in the calculation of ADDSC, i.e. ADDSC is calculated as the average of the non-missing ADD scores. (i) An inspection on the data reveals that some observations have too many missing ADD scores. We decide to keep those observations with at most one missing ADD score in the data set ADDCHILD. Write a SAS DATA step program to do this. (ii) The following program creates data set ADDCHILDB from the data set ADDCHILD. data addchi1d_b; set addchild; if score2 not = . and score4 not = . and score5 not = . ; add = mean(score2, score4, score5) ; keep score2 score4 score5 add; run; For the following observations in ADDCHILD, which observations will be output to ADDCHILDB by the program? Write down the contents of the output observations. m addsc score2 score4 score5 gender iq langg gpa socprob [8 marks] (c) The following program selects observations based on the variable SOCPROB. proc format; value code_a 0, 1 = ’Valid’ . = ’Missing’ other = ’Invalid’; r1111; proc print data=addchild; where put(socprob,code_a.)=’Missing’ or put(socprob,code_a.)=’Invalid’ - format socprob code_a.; run; S&AS: STAT1303 Data Management 6 (i) Which of the following observations will be selected by the program? m addsc score2 score4 score5 gender iq langg gpa. socprob (ii) In the context of data cleaning, describe what you will do after read— ing the printout of the PROC PRINT step. [7 marks] (d) We are interested in studying the relationship between later performances and the ADDSC score. (i) To facilitate the analysis, we decide to transform the variable ADDSC from continuous nature to categorical nature: 55 to < 65 3 ADDSC score 45 to < 55 2 ADDGROUP 1 (after transformation) 265 4 Write a SAS DATA step program, using a user-defined format or otherwise, to transform the variable ADDSC as described. (ii) The following program is used to study the relationship between the transformed variable ADDGROUP and SOCPROB. proc freq data=addchild; tables socprob*addgroup / chisq nocol norow nopercent; I’ll-I1; The printout of the program is on the next page. (1) Which ADDGROUP has the highest proportion of children hav- ing social problem? (2) Are the two variables statistically independent? Give reason to support your answer. (3) From (1) and (2), deduce the relationship between ADDSC and SOCPROB. S&AS: STAT1303 Data Management The FREQ Procedure la Statistics for Table of socprob by addgroup Effective Sample Size = 348 Frequency Missing = 11 S&AS: STAT1303 Data Management 8 The following boxplot is used to study the relationship between the ' variables GPA and ADDGROUP. Boxplot of GPA by ADDGROUP 400 300 100 " ‘— addgroup (1) Perform a descriptive analysis on GPA by ADDGROUP. (2) From (1), deduce the relationship between ADDSC and GPA. (iv) We can examine the relationship between GPA and ADDSC directly. Suggest two methods to examine this relationship. Note: You are only required to use 1 to 2 sentences to describe each method. [15 marks] S&AS: STAT1303 Data Management 9 (e) Identifying outliers is a common data cleaning task. (i) (ii) (iii) (iv) What is an outlier? What is the difference between an outlier and an extreme observation? What criterion is used in determining outliers for the variable IQ in the following program? proc univariate data=addchild; var iq; output out=addchild_e1 mean=mean std=std ; r1111; data addchild_e2; set addchild; if _n_ = 1 then set addchild_e1; if (iq not = . and iq < mean-2*std) or iq > mean+2*std then output; run; proc print data=addchild_e2; title ’List of outliers for 10’; run; The following table gives some statistics on the variable IQ. 1: Which range of IQ values will be classified as outliers if (1) 3 MAD (median absolute deviation) method, (2) 1.5 IQR (inter—quartile range) method, are used respectively? What is the advantage of using the methods in part (iii) over the method in part (ii)? [7 marks] [Total: 40 marks] S&AS: STAT1303 Data Management 10 3. (a) The data set VISIT contains details of visits to a clinic by heroin ad— ‘ dicts who had successfully completed a methadone maintenance treatment program there. Each heroin addict joining the methadone maintenance treatment program was required to visit the clinic on a regular basis and methadone was prescribed to the addict in each visit. The methadone dosage was determined by doctors. In addition, a urine sample was taken from the addict On each visit and was tested for heroin use. The test results and the methadone dosages were monitored continuously by doc- tors. Upon satisfying certain conditions, addicts are declared as having successfully completing the methadone maintenance treatment program and exit the program. A partial PROC CONTENTS printout for VISIT follows: # Variable Type Len Format Label 1 dt Num 8 DDMMYYB. Visit date in dd/mm/yy 2 id Num 8 Patient identification number 3 dose Num 8 Methadone dosage The data set VISIT was sorted in ascending order of ID , and then DT. ANNEX 1 shows the first 30 observations of VISIT. The following program summaries some information in VISIT. data treatment; set visit; by id; if first.id then do; visit=0; dose1=0; dt1=dt; end; dose1=dose1+dose; visit=visit+1; if dose2<dose then dose2=dose; if last.id then do; dose1=dose1/visit; time=dt-dt1; output; end; drop dose dt dtl; retain visit dtl dosel dose2; run; S&AS: STAT1303 Data Management 11 (1) State the c0nditions for variables FIRSTID and LAST.ID to be de- fined and automatically updated by a SAS DATA step program. (ii) Give two scenarios that an explicit OUTPUT statement must be used in a SAS DATA step program. (iii) Data set TREATMENT created from the above DATA step pro— gram contains the following variables: ID, VISIT, DOSEI, DOSE2, and TIME. What information are stored in each of the above vari— ables? (iv) Which of the following variables: VISIT, DOSEl, DOSE2, and TIME, will have data values affected by occasional missing values in vari— ables DT and DOSE of data set VISIT? How will they be affected? (v) Alternative to the above DATA step program, the data set TREAT-~ MENT can be created using SAS PROCS. The following SAS pro- gram attempts to create TREATMENT. Complete the program. proc means data=visit n0print nway; class id; output out=treatment [14 marks] (b) We want to include more information for the heroin addicts data in the data set TREATMENT in part (a). The data set PATIENT contains details of patients of the clinic men— tioned in part (a) and is also identified by variable ID. Among the other patients, PATIENT contains the heroin addicts who joined the methadone treatment program in part (a). Also, among the other patient informa- tion, PATIENT contains variables GENDER and AGE. (i) Write a SAS DATA step program to add the GENDER and AGE information from PATIENT to TREATMENT. (ii) The data set PATIENT is not cleaned in the sense that there are some observations having the same ID value. What is the effect of having duplicate ID values in PATIENT on your program and the resultant data set in (i)? (iii) Write a SAS program to list all observations which have duplicate ID values in PATIENT. [12 marks] S&AS: STAT1303 Data Management 12 (c) The resultant data set TREATMENT in part (b) contains data of 238 ‘ heroin addicts who had completed the methadone treatment program in the recent one year period. A new study which requires 40 subjects who have recently completed a methadone treatment program is to be started. We want to randomly select 40 subjects from these 238 heroin addicts and invite them to join the study. The following program (referred to as Program 1) does a random sampling from TREATMENT to select 40 subjects. /* PROGRAM 1 */ data treatment; set treatment; r=ranuni(0); run; proc sort data=treatment; by I; run; data sample; set treatment (obs=40); run; (1) Explain how the random sampling works in Program 1. (ii) Can the random number generator RANUNI be replaced by a stan— dard normal random number generator which generates random num— bers from a standard normal distribution in Program 1? Explain briefly. (iii) The following program (referred to as Program 2) also does the job. /* PROGRAM 2 */ data sample; set treatment; retain n k ; if _n_ = 1 then do; n=238; k=40; end; d = k/n; if ranuniCO) < d then do; output; k=k-1; end; n=n—1; dr0p n k d; run; Discuss the advantage(s) and disadvantage(s) of the algorithm im- plemented in Program 2 comparing to the algorithm in Program 1. S&AS: STAT1303 Data Management 13 (iv) Can the random number generator RANUNI be replaced by a stan- dard normal randOm number generator in Program 2? Explain briefly. (v) Later, it is decided that the study requires 20 male and 20 female subjects. Assume that the variable GENDER in TREATMENT has the coding scheme: 1 for male and 2 for female. Write a SAS program to perform a random sampling incorporating this new re— quirement. [14 marks] [Total: 40 marks] ********** END OF PAPER ********** S&AS: STAT1303 Data Management ANNEX 1 : The first 30 observations of VISIT in Question 3(a) Obs dt id dose 1 03/10/05 3 30 2 10/10/05 3 3O 3 17/10/05 3 35 4 24/10/05 3 35 5 31/10/05 3 30 6 06/11/05 3 30 7 10/11/05 3 30 8 17/11/05 3 45 9 23/11/05 3 50 10 30/11/05 3 50 11 04/12/05 3 3O 12 11/12/05 3 30 13 18/12/05 3 55 14 23/12/05 3 30 15 30/12/05 3 30 16 13/01/06 3 30 17 19/01/06 3 40 18 25/01/06 3 40 19 31/01/06 3 30 20 04/02/06 3 2O 21 03/09/05 5 90 22 15/09/05 5 90 23 17/10/05 5 75 24 14/11/05 5 75 25 31/12/05 5 55 26 06/01/06 5 55 27 17/01/06 5 55 28 27/01/06 5 45 29 13/02/06 5 50 30 24/02/06 5 40 S&AS: STAT1303 Data Management ANNEX 2 : An abridged version of SAS Syntax A. DATA STEP LIBNAME libref ’S'AS-data-l'ibrary’ ; DATA dataset-l <(data—set—options)> . . . - INPUT variablds) <f07mat> . . . ; LENGTH variable-1 <$>length. . . ; INFORMAT variable-1 <inf0rmat> . . . - LABEL variable—1=’label-1’ . . . ; FORMAT variable-1 <format> . . . - CARDSIDATALINES ; data RUN; LIBNAME libref ’SAS—data-libmry’ ; DATA dataset-J <(data—set—options)> . . . - INFILE filename; INPUT variable(s) <f0rmat> ; LENGTH variable-1 <$>length . . . ; INFORMAT variable—1 <inf0rmat> . . . ' LABEL variable-1=’label~1’. . . ; FORMAT variable-1 <format> . . . - RUN; S&AS: STAT1303 Data Management 16 LIBNAME libref ’SAS~data-library’ ; DATA dataset-I <(data-set-0ptions)> . . . ; MERGEISET dataset-I <(data-set-options)> <dataset-2 <(data—set—options)> > ...; UPDATE dataset-l <(data—set—options)> dataset-2 <(data—set-options)> ; BY <DESCENDING> variable-1 . . . ; DROP van’able(s) ; KEEP variable(s) ; variablezezpression ; ARRAY array—name (subscript) <array-elements> ; DELETE ; FILE fileref. . . ; OUTPUT dataset-I . . . ; PUT ’chamcter—string’ variable-1: . . . RENAME old-name-lznew—name-J . . . ; RETAIN variable(s) ; STOP ; WHERE where-expression ; IF expression ; IF expression THEN statement ; <ELSE statement ; > D0; D0 index-variable=start TO stop ; . more SAS statements . . . . more statements. . . END; END; RUN; 1. data set options in DATA step and other SAS PROCs: DROP=, FIRSTOBS=, IN=, KEEPz, OBS=, RENAME; WHERE: SScAS: STAT1303 Data Management 17 B. The following statements are common to All SAS PROCs 1. FORMAT statement: FORMAT variable-1 <f0rmat> . . . ; 2. LABEL statement: LABEL variable-1 =’label-1’ . . . ; 3. WHERE statement: WHERE where—expression ; C. APPEND PROC APPEND BASE=datasetname <DATA=datasetname> <FORCE> ; RUN; D. CONTENTS PROC CONTENTS <DATA=datasetname> <VARNUM> ; RUN; E. CORR PROC CORR DATA = datasetname <options1 > ; VAR variable {5); RUN; 1. options in PROC CORR: COV, NOSIMPLE, NOPROB, PEARSON, OUT: datasetname S&AS: STAT1303 Data Management 18 F. EXPORT PROC EXPORT DATA=datasetname OUTFILE=“filename” | OUTTABLE=“tablename” <DBMS=identifier><REPLACE> ; <data—source—statements ;> RUN; G. FORMAT PROC FORMAT <options1 > ; INVALUE <$>name value-or-mnge-lzinformat-value-Z < value-or-mnge-nzinformat-value—n> ; VALUE <$>name value-or-range-I =format—value-I < value-or-range—n=format-value-n> ; RUN; 1. options in PROC FORMAT: CNTLIN=, CNTLOUT=, LIBRARY: H. FREQ PROC FREQ <DATA==datasetname <data set options>> <optionsl>g TABLES variablel variableQ variabl82*variablel </0ption52> ; WEIGHT variable; BY <DESCENDING> variable—1 < <DESCENDING> variable-n); RUN; 1. options in PROC FREQ: FORMCHAR(1,2,7)=f0rmchar-string, PAGE, NOPRINT S&AS: STAT1303 Data Management 19 2. options in TABLE statement: ‘ NOCOL, NOROW, NOPRECENT, NOFREQ, NOCUM, NOPRINT, TESTP=(p1p2 . . .), EXPECTED, CHISQ, FISHERIEXACT, MEASURES, MISSING, MISSPRINT, OUT=datasetname <data set options> I. GCHART PROC GCHART DATA 2 datasetname ; HBAR I HBARBD | VBAR l VBAR3D chart-variable(s)</ option(s)1 > ; PIE | PIE3D | DONUT chart-variable(s) </ option(s)2 > ; BY grouping—variable(s} ; RUN; 1. options in HBAR | HBAR3D | VBAR | VBARBD statement: LEGEND, GROUP=, SUBGROUP=, MIDPOINTS=, SUMVAR=, TYPE=, NOSTAT S 2. options in PIE I PIE3D l DONUT statement: LEGEND, SLICE=, VALUE=, PERCENT=, GROUP=, SUBGROUP=, ACROSS=, DOWN=, MIDPOINTS=, SUMVAR=, TYPE: J. GPLOT PROC GPLOT DATA = datasetname ; PLOT vertical*horz’zontal < / options > ; PLOT vertical*horz'zontal = symbol—variable < / options > ; PLOT vertical*horiz0ntal = class-variable < / options > ; BY grouping—variable(s); RUN; 1. options in PLOT statement: CAXISICA : Lucia-color, CTEXTIC = text-color, GRID, HREszalue- list, VREszalue-list, OVERLAY, LEGEND S&AS: STAT1303 Data Management 20 K. IMPORT PROC IMPORT DATAFILE:“filename” | TABLE:“tablename” OUT=datasetname <DBMS=identifier><REPLACE> ; RUN; L. MEANS PROC MEANS <DATA=datasetname <data set options>> <optionsl > statistic-keywordzg BY <DESCENDING> variable—1 < <DESCENDING> variable-n>; CLASS grouping-variable(s); VAR variablds); FREQ variable; ID variablds}; OUTPUT OUT=datasetname <data set options> statistic-keyword3< ( variable( 5 ) ) > = <name(s)>; RUN; 1. options in PROC MEANS: ALPHA=, MISSING, NONOBS, NOPRINT, NWAY 2. statistic-keyword in PROC MEAN S: CLM CSS CV KURTOSIS LCLM MAX MEAN MIN N NMISS RANGE SKEWNESS STD STDERR SUM SUMWGT UCLM USS VAR MEDIAN P1 P5 P10 Q1 Q3 P90 P95 P99 QRANGE PROBT T 3. statistic-keyword in OUTPUT statement: CSS CV KURT OSIS LCLM MAX MEAN MIN N NMISS RANGE SKEWNESS STD STDERR SUM SUMWGT UCLM USS VAR MEDIAN P1 P5 P10 Q1 Q3 P90 P95 P99 QRANGE PROBT T S&AS: STAT1303 Data Management 21 M. PRINT PROC PRINT <DATA=datasetname <data set options>> <optionsl > ; VAR variable(s); BY <DESCENDING> variable{s); ID variablds); SUM variablefls’); RUN; 1. options in PROC PRINT: NOOBS, LABEL N. REPORT PROC REPORT <DATA=datasetname <data set 0ptions>> <options1 >; BY <DESCENDING> variable-1 < . .. <DESCENDING> variable-n>; FREQ variable; COLUMN column-specification(s) ; DEFINE variable / <usage options2 > ; BREAK location3 variable </option(s)4 > ; RBREAK location3 </option(s)4 > ; RUN; 1. options in PROC REPORT: MISSING, FORMCHAR(l,2,7)=formchar—string, N OWINDOWS 2. options in DEFINE statement: ACROSS, ANALYSIS, DISPLAY, GROUP, ORDER 3. location in BREAK and RBREAK statements can be either BEFORE or AFTER. 4. options in BREAK and RBREAK statements: OL, PAGE, SKIP, SUMMARIZE, UL S&AS: STAT1303 Data Management 22 O. SORT PROC SORT <DATA=datasetname <data set options>> <OUT=datasetname <data set options>> ; BY <DESCENDING> variable-1 < <DESCENDING> variable-n>; RUN; P. SQL PROC SQL ; CREATE TABLE table-name AS query-expression <ORDER BY order—by—item <,order-by-item>...>; SELECT <DISTINCT> object-item <,object-item>... <INTO :macro-variable—specification <, :macm-variable-specification>...> FROM from-list <WHERE sql—expression> <GROUP BY group—by—item <,g7‘0up—by-item>...> <HAVING sql—expression> <ORDER BY order-by—item <,order-by-item>...>; QUIT; S&AS: STAT1303 Data Management 23 Q. TABULATE PROC TABULATE <DATA=datasetname <data set 0ptions>> <options1 >; BY <DESCENDING> variable-1 < <DESCENDING> variable-72>; CLASS grouping-variable(s); VAR analysis-variable(s); FREQ variable; TABLE <<page-ea:pressz'0n,> row-expression) column~expression </ table—option(s)2 > ; KEYLABEL keyword-1=‘label-1’ <keyw0rd~n=‘label~n’> ; RUN; 1. options in PROC TABULATE: MISSING, FORMCHAR(1,2,7)=f0rmchar—string 2. options in TABLE statement: BOX=, MISSTEXT=, RTSPACEz R. UNIVARIATE PROC UNIVARIATE <DATA=datasetname <data set 0ptions>> <opti0ns1 >; BY <DESCENDING> variable-1 < .. . <DESCENDING> variable—TD; CLASS grouping—variable(s); VAR variable{s); FREQ variable; ID variable{s); HISTOGRAM variable(s) / normal; QQPLOT variable(s) / normal (muzest sigma=est); OUTPUT OUT = datasetname statistic—keyword2< (variable (s))> = <name(s)>; RUN; S&AS: STAT1303 Data Management 24 1. options in PROC UNIVARIATE: ‘ ALL, ALPHA=value, CIBASIC<TYPE=LOWER|UPPERITWOSIDE>, MUO=value{s), NORMAL, ROBUSTSCALE, FREQ, NOPRINT, PLOTS, NEXTROBS=n, NEXTRVALG 2. statistic-keyword in OUTPUT statement: OSS CV KURTOSIS MAX MEAN N MIN MODE RANGE NMISS NOBS STDMEAN SKEWNESS STD USS SUM SUMWGT VAR MEDIAN P1 P5 P10 P90 P95 P99 Q1 Q3 QRANGE GINI MAD QN SN STD-GINI STDMAD STD_QN STD_QRANGE STD_SN ...
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1303_0506sem2 - THE UNIVERSITY OF HONG KONG DEPARTMENT OF...

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