ISTANBUL STOCK EXCHANGE (BIST) FELL 6 POINTS IN AVERAGE TODAY THE UNITED STATES DOLLAR (USD)...
Transcript of ISTANBUL STOCK EXCHANGE (BIST) FELL 6 POINTS IN AVERAGE TODAY THE UNITED STATES DOLLAR (USD)...
ISTANBUL STOCK EXCHANGE (BIST) FELL 6 POINTS IN AVERAGE TODAY
THE UNITED STATES DOLLAR (USD) APPRECIATED BY 4 PERCENT LAST WEEK AGAINST TURKISH LIRA (TRL). AT THE 95% CONFIDENCE LEVEL, IT IS
ESTIMATED THAT THE EXCHANGE RATE WILL BE BETWEEN _____ AND ____.
THE LATEST SURVEY INDICATES THAT THE PRESIDENT`S APPROVAL RATING
NOW STANDS AT 60 PERCENT
THE PRICE OF KOC HOLDING STOCK WILL BE HIGHER IN SIX MONTH THAN IT IS NOW
THE PRICE OF KOC HOLDING STOCK IS LIKELY TO BE HIGHER IN SIX MONTH THAN IT IS NOW
THE STAGES FOR STATISTICAL THINKING ARE:
1- DEFINE THE PROBLEM
2- DETERMINE WHAT DATA IS NEEDED
3- SELECT A SAMPLE
4- COLLECT DATA
5- SUMMARIZE AND ANALYZE DATA
6- MAKE INFERENCES AND DECISIONS BASED ON INFORMATION
The Journey to Making Decisions
Begin Here:Identify the
ProblemDATADATA
INFORMATION
KNOWLEDGE
DECISIONMAKING
Descriptive Statistics, Probability, Computers
Experience, Theory, Literature
Inferential Statistics, Computers
DATA: Specific observations of measured numbers.
INFORMATION: Processed and summarized data yielding facts and ideas.
KNOWLEDGE:Selected and organized information that providesunderstanding, recommendations, andthe basis for decisions.
Descriptive Statistics include graphical and numerical procedures that summarize and process data and are used
to transform data into information
Descriptive Statistics include graphical and numerical procedures that summarize and process data and are used
to transform data into information
Inferential Statistics provide the bases for predictions, forecasts, and estimates that are to transform information to knowledge
Descriptive Statistics include graphical and numerical procedures that summarize and process data and are used
to transform data into information
Descriptive Statistics include graphical and numerical procedures that summarize and process data and are used
to transform data into information
POPULATION: A complete set of individuals, objects or
measurements having common observable characteristics.
Examples of Populations
- Names of all registered voters in TURKEY
- Incomes of all families living in ANKARA
- Annual return of all stocks traded on the ISTANBUL STOCK EXCHANGE
- Grade Point Averages of all the students in your University - BILKENT
SAMPLE: A subset or part of a population
Examples of Samples
- Names of 50.000 registered voters in TURKEY
- Incomes of 10.000 families living in ANKARA
- Annual return of 150 stocks traded on the ISTANBUL STOCK EXCHANGE
- Grade Point Averages of 500 students from different departments in your University - BILKENT
Example:
Imagine that a public opinion polling firm has been contracted to conduct a study concerning the percentage of the state`s registered voters who approve of nuclear power as an energy source. As part of the polling process, 750 individuals are randomly selected from the voter registration list and carefully interviewed.
Elements?
Random Sample ?
Variable of Interest?
Data?
Statistic?
Population?
Below AverageBelow Average Above AverageAbove Average Above AverageAbove Average AverageAverage Above Average Above Average AverageAverage Above AverageAbove Average
Average Average Above AverageAbove Average Below AverageBelow Average PoorPoor Excellent Excellent Above AverageAbove Average AverageAverage
Above AverageAbove Average Above AverageAbove Average Below AverageBelow Average PoorPoor Above Average Above Average AverageAverage
Frequency DistributionFrequency Distribution Example: Marada InnExample: Marada Inn
Sample of Parts Cost($) for 50 Tune-Sample of Parts Cost($) for 50 Tune-upsups
Frequency DistributionFrequency Distribution
Example: Hudson Auto RepairExample: Hudson Auto Repair
Dot PlotDot Plot
5050 6060 7070 8080 9090 100100 1101105050 6060 7070 8080 9090 100100 110110
Cost ($)Cost ($)
Tune-up Parts CostTune-up Parts Cost
Example: Hudson Auto RepairExample: Hudson Auto Repair
Stem-and-Leaf DisplayStem-and-Leaf Display
55
66
77
88
99
1010
2 72 7
2 2 2 2 5 6 7 8 8 8 9 9 92 2 2 2 5 6 7 8 8 8 9 9 9
1 1 2 2 3 4 4 5 5 5 6 7 8 9 9 91 1 2 2 3 4 4 5 5 5 6 7 8 9 9 9
0 0 2 3 5 8 90 0 2 3 5 8 9
1 3 7 7 7 8 91 3 7 7 7 8 9
1 4 5 5 91 4 5 5 9
a stema stema leafa leaf
Example: Hudson Auto RepairExample: Hudson Auto Repair
Note: Data is in ascending order.Note: Data is in ascending order.
525 530 530 535 535 535 535 535 540 540540 540 540 545 545 545 545 545 550 550550 550 550 550 550 560 560 560 565 565565 570 570 572 575 575 575 580 580 580580 585 590 590 590 600 600 600 600 610610 615 625 625 625 635 649 650 670 670675 675 680 690 700 700 700 700 715 715
Measures of LocationMeasures of Location
MeanMean
MedianMedian
ModeMode PercentilesPercentiles QuartilesQuartiles
Weighted MeanWeighted Mean
Sample Mean Sample Mean xx
Number ofNumber ofobservationsobservationsin the samplein the sample
Sum of the valuesSum of the valuesof the of the nn observations observations
ixx
n ix
xn
Population Mean Population Mean
Number ofNumber ofobservations inobservations inthe populationthe population
Sum of the valuesSum of the valuesof the of the NN observations observations
ix
N
ix
N
Weighted MeanWeighted Mean
Denominator:Denominator:sum of thesum of the
weightsweights
Numerator:Numerator:sum of the weightedsum of the weighted
data valuesdata values
i i
i
wxx
w
i i
i
wxx
w
where:where:
xxii = value of observation = value of observation ii
wwi i = weight for observation = weight for observation ii
Weighted MeanWeighted Mean
Example: Construction WagesExample: Construction Wages
Ron Butler, a home builder, is looking over Ron Butler, a home builder, is looking over the expenses he incurred for a house he just the expenses he incurred for a house he just built. For the purpose of pricing future projects, built. For the purpose of pricing future projects, he would like to know the average wage ($/hour) he would like to know the average wage ($/hour) he paid the workers he employed. Listed below he paid the workers he employed. Listed below are the categories of worker he employed, along are the categories of worker he employed, along with their respective wage and total hours with their respective wage and total hours worked.worked.
Worker Wage ($/hr) Total Hours
Carpenter 21.60 520Electrician 28.72 230
Laborer 11.80 410Painter 19.75 270Plumber 24.16 160
Weighted MeanWeighted Mean
Example: Construction WagesExample: Construction Wages
31873.720.0464 $20.05
1590i i
i
wx
w
31873.7
20.0464 $20.051590
i i
i
wx
w
Worker x i wi wi x i
Carpenter 21.60 520 11232.0 Electrician 28.72 230 6605.6
Laborer 11.80 410 4838.0 Painter 19.75 270 5332.5 Plumber 24.16 160 3865.6
1590 31873.7
FYI, equally-weighted (simple) mean = FYI, equally-weighted (simple) mean = $21.21$21.21
8080thth Percentile Percentile
ii = ( = (pp/100)/100)nn = (80/100)70 = 56 = (80/100)70 = 56
Averaging the 56Averaging the 56thth and 57 and 57thth data values: data values:
80th Percentile = (635 + 649)/2 = 64280th Percentile = (635 + 649)/2 = 642
Note: Data is in ascending order.Note: Data is in ascending order.
Example: Apartment RentsExample: Apartment Rents
525 530 530 535 535 535 535 535 540 540540 540 540 545 545 545 545 545 550 550550 550 550 550 550 560 560 560 565 565565 570 570 572 575 575 575 580 580 580580 585 590 590 590 600 600 600 600 610610 615 625 625 625 635 649 650 670 670675 675 680 690 700 700 700 700 715 715
525 530 530 535 535 535 535 535 540 540540 540 540 545 545 545 545 545 550 550550 550 550 550 550 560 560 560 565 565565 570 570 572 575 575 575 580 580 580580 585 590 590 590 600 600 600 600 610610 615 625 625 625 635 649 650 670 670675 675 680 690 700 700 700 700 715 715
8080thth Percentile Percentile
““At least 80% of theAt least 80% of the items take on aitems take on a
value of 642 or less.”value of 642 or less.”
““At least 20% of theAt least 20% of theitems take on aitems take on a
value of 642 or more.”value of 642 or more.”
56/70 = .8 or 80%56/70 = .8 or 80% 14/70 = .2 or 20%14/70 = .2 or 20%
Example: Apartment RentsExample: Apartment Rents
QuartilesQuartiles
Quartiles are specific percentiles.Quartiles are specific percentiles. First Quartile = 25th PercentileFirst Quartile = 25th Percentile
Second Quartile = 50th Percentile = MedianSecond Quartile = 50th Percentile = Median Third Quartile = 75th PercentileThird Quartile = 75th Percentile
A wholesaler sold 575, 410 and 520 microwave ovens at prices (in USD) 75, 125 and 100 respectively. What is the mean price of the ovens sold?
Example:
The following data represent the duration (in days) of Space Shuttle voyages for the years 1992-1994. (18 values)
8,9,9,14,8,8,10,7,6,9,7,8,10,14,11,8,14,11
Q: Find The Mode
MONTHLY STARTING SALARY (In TRL)
Graduate Monthly Starting Salary
1 2,850
2 2,950
3 3,050
4 2,880
5 2,755
6 2,710
7 2,890
8 3,130
9 2,940
10 3,325
11 2,920
12 2,880
TOTAL: 35,280