Data Modeling basics

CHAPTER 1

INTRODUCTIONBusiness Statistics

Business Statistics, A First Course (4e) 2008 Pearson Education

LEARNING OBJECTIVESDefinition of StatisticsHow statistics is used in BusinessThe sources of data used in BusinessThe types of data used in Business


BASIC CONCEPTS OF STATISTICSStatistics is concerned with: Processing and analyzing dataCollecting, presenting, and transforming data to assist decision makers


APPLICATION OF STATISTICS IN VARIOUS AREASMarketing : Market survey, Sales performance, forecasting sales, market shares and demand for various types of industrial products.Economics : Economical problems, formulation of economic policiesFinance : Comparing the performances mutual funds and shares, calculating financial risk and comparing returns on investment in two or more companies and selection of portfolio of stocks etc.


Insurance : Insurance is based on the concept of probability and determining the premium of each customers etc.Operations : Sampling inspection, inventory management, statistical quality control, six sigma method and supply chain management.HR or Development : Evaluating the performances, developing rating systems, evolving compensatory rewards and training system etc.


Information Technology : problem solving, optimization of server time, assessing performance of a program and testing of the software.


KEY DEFINITIONSA population (universe) is the collection of all members of a groupA sample is a portion of the population selected for analysisA parameter(census) is a numerical measure that describes a characteristic of a populationA statistic is a numerical measure that describes a characteristic of a sample


POPULATION VS. SAMPLE a b c d ef gh i jk l m n o p q rs t u v w x y z

PopulationSample b c g i n o r u y

Measures used to describe a population are called parametersMeasures computed from sample data are called statistics


TWO BRANCHES OF STATISTICSDescriptive statisticsCollecting, summarizing, and presenting dataInferential statisticsDrawing conclusions about a population based only on sample data


DESCRIPTIVE STATISTICSCollect datae.g., SurveyPresent datae.g., Tables and graphsCharacterize datae.g., Sample mean =


INFERENTIAL STATISTICSEstimatione.g., Estimate the population mean weight using the sample mean weightHypothesis testinge.g., Test the claim that the population mean weight is 120 poundsChap 1-*Business Statistics, A First Course (4e) 2008 Pearson EducationDrawing conclusions about a population based on sample results.


COLLECTING DATASecondary SourcesData CompilationObservationExperimentationPrint or Electronic SurveyPrimary SourcesData Collection


TYPES OF DATAExamples:Marital StatusPolitical PartyEye Color (Defined categories)Examples:Number of ChildrenDefects per hour (Counted items)Examples:WeightVoltage (Measured characteristics)


PRIMARY SCALES OF MEASUREMENT


PRIMARY SCALES OF MEASUREMENTNOMINAL SCALEThe numbers serve only as labels or tags for identifying and classifying objects. When used for identification, there is a strict one-to-one correspondence between the numbers and the objects. The numbers do not reflect the amount of the characteristic possessed by the objects. The only permissible operation on the numbers in a nominal scale is counting. Only a limited number of statistics, all of which are based on frequency counts, are permissible, e.g., percentages, and mode.


ILLUSTRATION OF PRIMARY SCALES OF MEASUREMENT


PRIMARY SCALES OF MEASUREMENTORDINAL SCALEA ranking scale in which numbers are assigned to objects to indicate the relative extent to which the objects possess some characteristic. Can determine whether an object has more or less of a characteristic than some other object, but not how much more or less. Any series of numbers can be assigned that preserves the ordered relationships between the objects. In addition to the counting operation allowable for nominal scale data, ordinal scales permit the use of statistics based on centiles, e.g., percentile, quartile, median.


PRIMARY SCALES OF MEASUREMENTINTERVAL SCALENumerically equal distances on the scale represent equal values in the characteristic being measured. It permits comparison of the differences between objects. The location of the zero point is not fixed. Both the zero point and the units of measurement are arbitrary. Any positive linear transformation of the form y = a + bx will preserve the properties of the scale. It is not meaningful to take ratios of scale values. Statistical techniques that may be used include all of those that can be applied to nominal and ordinal data, and in addition the arithmetic mean, standard deviation, and other statistics.


PRIMARY SCALES OF MEASUREMENTRATIO SCALEPossesses all the properties of the nominal, ordinal, and interval scales.It has an absolute zero point. It is meaningful to compute ratios of scale values. Only proportionate transformations of the form y = bx, where b is a positive constant, are allowed. All statistical techniques can be applied to ratio data.


PRIMARY SCALES OF MEASUREMENT


DATA MEASUREMENT SCALESNominal scale : Nominal data is called as categorical data. Numbers are used to label an item or category. Example : we could record martial status using the following category :Single, Married , Divorced, Widowed.


Ordinal Scale or rank data : are used to rank objects and attributes. Ordinal data appear to be nominal, but their values are in order. For example, at the completion of most colleges and university courses, students are asked to evaluate the course, Suppose in particular college the values are poor, fair good, very good and excellent.


Interval Scale : Interval data is quantitative data that can be measured on the numerical scale. However, the zero point does not mean the absence of something. Examples are temperature, time etc.Ratio Scale : Ratio is the quantitative data can be measured on a numerical scale. Here, the zero point does mean the absence of what is being measured. Example, height, weight, production cycle time & no. of trucks sold etc.


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Data Modeling basics