Presentation on theme: "Section 1-2 Data Classification"— Presentation transcript:
1Section 1-2 Data Classification Objectives:Classify data as discrete or continuous; qualitative or quantitative; and the level of measurement
2IntroductionJust as animals can be classified into phylum and then further into species, data collected in a statistical study can be classified into different categories.The different categories group data based on the type of statistical analysis that can be performed on the data. Therefore, knowing the classification of a set of data is the first step in any statistical process.
3Qualitative vs. Quantitative Data Qualitative Data (aka Categorical Data)Quantitative DataConsist of labels or descriptions of traitsTypically non-numeric, but not a requirementExamples:Eye ColorGenderReligious PreferenceYes/NoHometownFavorite FoodID numbers (SS#, GHC#)Consist of counts or measurementsNumericalExamples:HeightsWeightsPulse RateAgeBody TemperaturesCredit HoursTest ScoresAverage rainfall
4ExamplesClassify the following data as either qualitative or quantitativeThe number of homes a bank repossesses in four randomly selected monthsFive hundred people are asked the frequency with which they eat chocolate (never, seldom, occasionally, or frequently)A McDonald’s quality control inspector counts the number of fries in 40 individual servingsThe license plate of a car
5Quantitative Variables can be furthered classified Discrete VariablesContinuous VariablesCan be assigned values such as 0, 1, 2, 3“Countable”Examples:Number of childrenNumber of credit cardsNumber of calls received by switchboardNumber of studentsCan assume an infinite number of values between any two specific valuesObtained by measuringOften include fractions and decimalsExamples:TemperatureHeightWeight
6ExamplesDetermine whether the following data are continuous or discrete:The number on the uniform of a football playerThe temperature in Celsius in Paris, FranceThe total weight of sugar imported by the United States each dayThe prices of 50 randomly selected new cars
7Data Quantitative Discrete Continuous* Qualitative Since continuous data is measured, answers are rounded tonearest given unit; however the boundaries (possible values)are understood to be
8Level of Measurement Four levels of measurement NominalOrdinalIntervalRatioThe higher the level of measurement, the more mathematical calculations that can be performed on that data.
9Measurement Scales Nominal Ordinal Classifies data into mutually exclusive (nonoverlapping) exhausting categoriesNo order or ranking can be imposedQualitativeNo calculations an be performed on Nominal dataExamples:GenderZip CodesPolitical AffiliationReligionClassifies data into categoriesUsually qualitativeRANKING (natural order), but precise differences between ranks do not exist (addition or division do not make sense)Examples:Letter grades (A, B, C, D, F)Judging contest (1st, 2nd , 3rd )Ratings (Above Avg, Avg, Below Avg, Poor)
10Measurement Scales Interval Ratio Quantitative data Ranks (orders) dataPRECISE DIFFERENCES between units of measure do exist and are meaningfulNo meaningful zero (position on a scale, but does not mean absence of something) Zero is simply a placeholderExamples:Temperature (0° does not mean no heat at all)IQ Scores (0 does not imply no intelligence)Calendar datesQuantitative dataRanks (orders) dataPrecise differences exist and are meaningfulTRUE ZERO exist (Zero means absence of something)Can add, subtract, multiply, and divide data valuesExamples:HeightWeightAreaNumber of phone calls receivedSalary
12ExamplesDetermine if the following qualitative or quantitative and then determine the level of measurementThe free throw shooting percentage of a basketball playerA survey response to “are you predominantly left-handed or right-handed?”The temperature in Fahrenheit in Atlanta, GeorgiaThe individual page numbers at the bottom of each page in this book