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Introduction to Business Statistics
Transcript of Introduction to Business Statistics
sources of data,
estimation and inference
time series The final grade will consist of: Active class participation 25%
homework and assignments 25%
final exam 50% Statistics as scinece: collecting, organizing, presenting, analyzing, and interpreting numerical data to assist in making more effective decisions.
Statistics:parameter estimates from a sample Statistical techniques are used extensively by marketing, accounting, quality control, consumers, professional sports people, hospital administrators, educators, politicians, physicians, etc...
Who uses statistics? Population and sample A population is a collection of possible individuals, objects, or measurements of interest.
E.g. all exchange students at the KNU in spring 2010, all cars in S. Korea, trading days on the NYSE in 2009
A sample is a portion, or part, of the population of interest.
E.g. 15 exchange students students, 233 randomly selected cars, first 50 trading days on the NYSE in 2009
Parameters A parameter is a numeric quantity, usually unknown, that describes a certain population characteristic.
Parameters are normaly denoted with greek leters
The true “average” height of adult human males.
The number of individuals that went thrugh the red light near KNUnorth gate.
The median income in Japan.
The difference between average income of males and females in the USA
The correlation between work experience and productivity
Statistics A statistic is a quantity, calculated from a sample of data, used to estimate a parameter
Parameters are normaly denoted with lowcase latin leters
Examples: The “average” height of adult human males, calculated from a sample of 100 men.
The percentage of individuals that go thrugh the red light near KNUnorth gate, based on a five day sample.
The mean difference between average income of males and females in the US from a sample of 60 men and 55 women
Descriptive Statistics Methods of organizing, summarizing, and presenting data in an informative way EXAMPLE 1: A Gallup poll found that 29% of the people in a survey knew the name of the capital of Somalia. The statistic 29 describes the number out of every 100 persons who knew the answer.
EXAMPLE 2: According to Consumer Reports, Samsung washing machine owners reported 9 problems per 100 machines during 2009. The statistic 9 describes the number of problems out of every 100 machines. Inferential Statistics A decision, estimate, prediction, or generalization about a population, based on a sample.
EXAMPLE 1: TV networks constantly monitor the popularity of their programs by hiring Nielsen and other organizations to sample the preferences of TV viewers.
EXAMPLE 2: The accounting department of a large firm will select a sample of the invoices to check for accuracy for all the invoices of the company.
EXAMPLE 3: Wine tasters sip a few drops of wine to make a decision with respect to all the wine waiting to be released for sale.
Methodology Methodology is about the way you undertake research
You need to ask if your research is
Research need to be completed within time and cost constraints
If your research is repeated then you should expect the same kind of results
Your research should measure what you say it measures
Models Are a representation of real objects and real situations (e.g. levels of stock, levels of sales required to breakeven)
Provide an understanding of how things work
Focus on relevant variables
Attempt to identify possible relationships
Type of variables Qualitative or attribute variable: the characteristic or variable being studied is nonnumeric.
EXAMPLES: Gender, religious affiliation, type of automobile owned, state of birth, eye color. Quantitative variable: the variable can be reported numerically.
EXAMPLE: balance in your checking account, minutes remaining in class, number of children in a family. Quantitative variables can be classified as either discrete or continuous.
Discrete variables: can only assume certain values and there are usually “gaps” between values.
EXAMPLE: the number of bedrooms in a house. (1,2,3,..., etc...).
Continuous variables: can assume any value within a specific range.
EXAMPLE: The time it takes for the KTX train from Seoul to Dongdaegu Measurement Measurement is about assigning a value or score to an observation
Measurement can be categorized as nominal, ordinal, interval or ratio
Levels of measurement Defined by the way we may describe the difference between two units
Determines the parameters we may compute for the variable
Nominal level: Data that can only be classified into categories and cannot be arranged in an ordering scheme.
Possible parameters: frequency distribution, mode
EXAMPLES: eye color, gender, religious affiliation. Mutually exclusive: An individual or item that, by virtue of being included in one category, must be excluded from any other category.
Exhaustive: each person, object, or item must be classified in at least one category.
Ordinal level: involves data that may be arranged in some order, but differences between data values cannot be determined or are meaningless.
EXAMPLE: During a taste test of 4 colas, cola C was ranked number 1, cola B was ranked number 2, cola A was ranked number 3, and cola D was ranked number 4. Interval level: similar to the ordinal level, with the additional property that meaningful amounts of differences between data values can be determined. There is no natural zero point.
Parameter examples: arithmetic mean, standard deviation, correlation,…
EXAMPLES: calendar time, temperature on Celsius scale Ratio level: the interval level with an inherent zero starting point. Differences and ratios are meaningful for this level of measurement.
EXAMPLES: money, heights of NBA players.