Samsubar SALEHINSTITUTION : FEB-UGM-ECONOMICSWORK EXPERIENCE:1. DIRECTOR OF MASTER & Ph.D PROGRAM FEB-UGM.2. DIRECTOR OF RESEARCH & DEVELOPMENT EC FEB-UGM3. CONSULTANT OF BAPPEDA DIY PROVINCE.4. BSNP SUPERVISOR OF EDUCATION MINISTRY.5. LOCAL GOVERNMENT FINANCE CONSULTANT.6. DEPUTY OF RESEARCH & TRAINING PROGRAM FEB.7. MEMBER OF ACADEMIC AUDITOR UGM.8. SECRETARY OF ECONOMICS PROGRAM FEB-UGM.9. RESEARCHER OF INDONESIAN EC REVIEW & OUTLOOK.10. INSTRUCTOR OF LOCAL GOVERNMENT FINANCE.HP =0811253224, EMAIL = ssamsubar@yahoo.com

REGULATIONS1. PLEASE COME BEFORE CLASS STARTED.2. PLEASE TURN-OFF YOUR MOBILE PHONE. 3. YOU MUST BRING YOUR CALCULATOR.4. DONT MAKE ANY OTHER ASSIGNMENTS DURING THE CLASS ACTIVITY.5. YOU MUST DO THE QUESTION WHENEVER ONE OF YOUR FRIENDS ASK FOR YOU TO DO SO.6. YOU MUST ATTEND THE CLASS AT LEAST 80%.7. THERE WILL BE NO MAKE UP EXAM AND QUIZ.8. PLEASE BE HONEST. PLEASE GIVE YOUR SIGNATURE ONLY ON YOUR NAME NOT FOR ANYBODY ELSE!

Text-books1. Douglass, Lind and Marchal: STATISTICS FOR BUSINESS DECISION.2. Wonnacott, Wonnacott :STATISTICS.3. Samsubar: Statistik Deskriptif Statistik Induktif Aplikasi Statistik dalam Bisnis

WEEK: 1. STATISTICSA COLLECTION OR A SET OF NUMERICAL INFORMATION FROM SAMPLE IS CALLED STATISTICS. DATA CAN BE COLLECTED FROM POPULATION ( THE ENTIRE SET OF INDIVIDUALS OR OBJECTS OF INTEREST ). ( PARAMETER ).3. STATISTICS IS THE SCIENCE OF COLLECTING, ORGANIZING, PRESENTING & INTERPRETING DATA TO ASSIST IN MAKING MORE EFFECTIVE DECISION.

STATISTICS vs PARAMETER PARAMETER STATISTICSAVERAGE X

VARIANCE 2 S2

STD DEV S

PROPORTION P

BASIC-CONCEPTSWHY SAMPLING?SAMPLING ERRORNON-SAMPLING ERRORVALIDITYREABILITY

DESCRIPTIVE vs INFERENTIALDESCRIPTIVE STATISTICS IS A METHODS OF ORGANIZING, TABULATING, SUMMARIZING, PRESENTING, ANALYZING, INTERPRETING DATA IN AN INFORMATIVE WAY.

INFERENTIAL STATISTICS IS THE METHODS USED TO DETERMINE SOMETHING ABOUT CHARACTERISTICS OF A POPULATION ON THE BASIS OF A SAMPLE OR TO INFER SOMETHING ABOUT A POPULATION. X CAN BE USED TO ESTIMATE THE .

TYPES OF DATA1.TIMES-SERIES2. CROSS-SECTION3. PANEL ( POOLING-DATA )4. PRIMARY DATA5. SECONDARY DATA

VARIABLES & LEVELS OF MEASUREMENTQUALITATIVE OR ATTRIBUTE: CHARACTERISTIC OF THE DATA IS NONNUMERIC: GENDER, EYE COLOR, RELIGIOUS AFFILIATION ETC.QUANTITATIVE :CHARACTERISTIC OF THE DATA IS NUMERIC: NUMBER OF CHILDREN IN A FAMILY, TOTAL MONEY SUPPLY IN THE ECONOMY.LEVELS OF MEASUREMENT: NOMINAL (THERE IS NO PARTICULAR ORDER) , ORDINAL ( DATA ARE RANKED OR ORDERED ACCORDING TO THE PARTICULAR TRAIT THEY POSSES.INTERVAL: IQ, TEMPERATURE, HUMIDITY.RATIO: NUMBER OF PEOPLE GOES TO HOSPITAL PER ANNUM, NUMBER OF HOURS STUDENTS STUDY PER DAY.

CONSTRUCTING A FREQUENCY DISTRIBUTION STEP 1. DECIDE ON THE NUMBER OF CLASSES BY USING THE FORMULA 2k > N or 1 + 3.3 log N.STEP 2. DETERMINE THE CLASS INTERVAL OR WIDTH: ci = ( HIGHEST LOWEST ): k.STEP 3. SET THE CLASS LIMITS AND COUNT THE NUMBER OF ITEMS ( FREQUENCIES ) IN EACH CLASS.STEP 4. DETERMINE CLASS BOUNDARIES, THE MIDPOINTS, CUMULATIVE FREQUENCY DISTRIBUTION ( UPPER AND BELOW THE CLASS BOUNDARIES).

MEASURES OF CENTRAL TENDENCYUNGROUPED DATA:MEAN = Xi / NMEDIAN: LOCATION OF MEDIAN : (N + 1) : 2, THEN FIND THE MEDIAN.MODE: FIND THE LARGEST FREQUENCY.EXAMPLE : THE TOEFL SCORE OF 8 MASTER MANAGEMENT STUDENTS CAN BE SEEN AS FOLLOW: 524, 535, 545, 525, 516, 530, 535, 540.CALCULATE: THE MEAN, MEDIAN AND MODE.

MEASURES OF CENTRAL TENDENCYGROUPED DATA :MEAN = ( F.M / N )MEDIAN = LCB + ( N/2 - FCL ) x Ci ( FCU - FCL )FCU FCL = THE FREQUENCY OF MEDIAN. MODE = LCB + ( d1 ) / ( d1 + d2 ) x Ci. . THE RELATIVE POSITIONS OF THE MEAN, MEDIAN AND MODE: SYMMETRIC, SKEWED TO THE RIGHT OR LEFT.

APPLICATIONTHE FOLLOWING SCORES WERE MADE BY PROF x ACCOUNTING STUDENTS ON A TEST:68 52 49 56 6974 40 59 79 8143 57 60 88 8747 65 55 68 6550 78 61 90 8365 66 72 63 94 COMPUTE: THE MEAN, MEDIAN & MODE.

SKEWNESS COUNTRIES: A B CINCOME:MEAN $ 1100 $ 1000 $ 1200 MEDIAN 1100 1100 1100MODE 1100 1200 1000Std dev 25 30 40

SK = 3 ( MEAN MEDIAN ) / STD DEV.

GEOMETRIC MEANGM IS VERY USEFUL IN FINDING THE AVERAGE OF PERCENTAGES, RATIOS, INDEXES OR GROWTH RATE.

(x1)(x2) or n(x1)(x2) (xn)

n(VALUE AT THE END PERIOD : VALUE AT START OF PERIOD) - 1

EXAMPLESUPPOSE LABOUR RECEIVE A 10 PERCENT INCREASE IN WAGE THIS YEAR AND A 15 PERCENT INCREASE NEXT YEAR. HOW MUCH THE AVERAGE ANNUAL INCREASE IN WAGE?THE RETURN ON DEPOSIT EARNED BY BNI FOR 5 SUCCESSIVE YEARS WAS: 10%, 12%, 17%, - 5%, 50 %. WHAT IS THE AVERAGE ANNUAL RETURN ON DEPOSIT?TOTAL ASSETS OF X COMPANY IN 1995 WAS $ 1300 M. IN 2005, THE AMOUNT OF TOTAL ASSETS WAS NEARLY TO $ 1750 M. WHAT IS THE AVERAGE ANNUAL INCREASE IN ITS ASSETS?

Week 2. Measures of LocalityUNGROUPED DATA.QUARTILES : LQ1 = ( N + 1 )/ 4, LQ2 = 2 ( N +1)/4 LQ3 = 3 ( N + 1 )/4.DECILES: LD1 = ( N + 1 )/10 ,LD9 = 9 ( N + 1 )/10PERCENTILES: LP1 = ( N + 1 )/100,. LP99 = 99( N + 1 )/100.EXAMPLE:TOEFL SCORES OF 8 STUDENTS:516 524 525 530 535 535 540 545.CALCULATE THE SCORES: Q3, D4, D7, P80.

WEEK: 2. MEASURES OF LOCALITY GROUPED DATA1. QUARTILES: THE DISTRIBUTION OF DATA IS DIVIDED INTO FOUR PARTS, EACH PART IS EQUAL TO 25 %.LQ1 = N/4, LQ2 = 2N/4 LQ3 = 3N/4Qi = LCB + ( LQi FCL ) / ( FCU FCL ) x Ci2. DECILES: THE DISTRIBUTION OF THE DATA IS DIVIDED INTO 10 PARTS, EACH PART IS EQUAL TO 10 %.3. PERCENTILES: THE DISTRIBUTION OF THE DATA IS DIVIDED INTO 100 PARTS, EACH PART IS EQUAL TO 1 %.

THE DISTRIBUTION OF SCORES IN A TESTSCORES F CB FCLT M FM FM2 39.5 040 50 5 45 50.5 551 61 7 56 61.5 1262 72 9 67 72.5 2173 83 5 78 83.5 2684 94 4 89 94.5 30CALCULATE: Q1 , D6, P90 AND D2. : coefficient of variation

DISPERSIONDISPERSION CAN BE USED TO MEASURE THE VARIABILITY OF THE DATA OR TO MEASURE THE DIFFERENCE BETWEEN THE VALUE OF EACH OBSERVATION AND ITS AVERAGE VALUE.IN OUR DAILY LIFE THIS METHOD CAN BE USED TO INSPECT THE QUALITY CONTROL, OF PRODUCT, TO HANDLE RISK, TO KNOW THE EQUALITY DISTRIBUTION OF INCOME, TO DETECT THE LIMIT OF POLUTANTS.

EXAMPLESTUDENT SCORES IN STATISTICS TEST CLASS: A B C1 62 100 602 60 20 603 58 95 604 61 25 605 59 90 606 60 30 607 61 85 608 59 35 60

MEAN SCORES: 60 60 60 - CAN YOU EXPLAIN SOMETHING ABOUT THE SCORES ABOVE?- IN WHICH CLASS THE SHAPE OF THE DATA IS FLATTER?

EXAMPLETHE FLUCTUATION OF STOCK PRICES DURING THE LAST 5 DAYS OFFERED BY 3 COMPANIES IN JAKARTA STOCK MARKET IS AS FOLLOW: DAY COMPANY A B C 1 $ 30 $ 130 $ 50 2 32 80 50 3 29 150 50 4 31 100 50 5 33 200 50IF YOU ARE A BROKER IN THE STOCK MARKET WHICH ONE IS THE BEST FOR INVESTMENT? WHY?

DISTRIBUTION OF INCOMEINCOME NUMBER OF EMPLOYEES: A B

$ 300 399 6 8 400 499 8 10 500 599 11 10 600 699 13 13 700 799 8 11 800 - 899 4 8

CAN YOU FIND IN WHICH COMPANY HAS MORE EQUAL DISTRIBUTION OF INCOME? A OR B? WHY?

QUALITY CONTROLDISTRIBUTION OF LIFETIME 3 BRAND OF DRY BATTERIES PRODUCED BY ABC MANUFACTURE IN CAKUNG JAKARTA.

BRAND: A BRAND: B BRAND: C Xi 1800 H 1450 H 1500 H ( Xi X )2 150 H 60 H 160 H SAMPLE 15 UNITS 10 UNITS 11 UNITS (n)WHICH BRAND IS BETTER THAN THE OTHER?Use coefficient of variation formula

ABSOLUTE DISPERSIONTHE MOST COMMON MEASURES OF ABSOLUTE DISPERSION ARE: RANGE, MEAN DEVIATION, STANDARD DEVIATION AND VARIANCE.A. UNGROUPED DATA: ( POPULATION )VARIANCE = 2 = ( Xi u )2 / NSTD DEVIATION = SQUARE ROOT OF VARIANCE OR = ( Xi u ) 2 / NB. UNGROUPED DATA ( SAMPLE )VARIANCE = S2 = ( Xi X )2 /( n 1 )STD DEVIATION = S = SQUARE ROOT OF VARIANCE.

ABSOLUTE DISPERSIONGROUPED DATAVARIANCE = ( FM2 / N ) - ( FM/N )2STD DEVIATION = VARIANCE IT IS IMPORTANT TO NOTE THAT ABSOLUTE DISPERSION CAN NOT BE USED TO MAKE DECISION BECAUSE UNIT OF ACCOUNT IN EACH VARIABLE ARE NOT THE SAME. 1. EXAMPLE: STANDARD DEVIATION OF INCOME IN COMPANY A = $100, WHILE STANDARD DEVIATION OF HEIGHT = 5,3 INCHES (CAN WE EVEN LOGICALLY COMPARE $ AND INCHES? ).2. STD DEV OF WAGES IN COMPANY A = $ 100, STD DEV OF WAGES IN COMPANY B = $ 200. CAN YOU CONCLUDE THAT THE DISTRIBUTION WITH THE $200 STD DEV HAS TWICE THE VARIABILITY OF THE ONE WITH THE $ 100 STD DEV?3. WHAT IS NEEDED FOR COMPARISON PURPOSES IS A MEASURE OF THE DEGREE OF RELATIVE DISPERSION THAT EXISTS IN THE DISTRIBUTION BEING STUDIED.

RELATIVE DISPERSIONTHE MOST POPULAR MEASURE OF RELATIVE DISPERSION IS THE COEFFICIENT OF VARIATION ( CV ).

CV = ( STD DEV / MEAN ) x 100 %.

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