DSCI 2710 1st Edition Lecture 1Outline of Last Lecture I. SyllabusOutline of Current Lecture II. Population vs. SampleIII. Descriptive vs. Inferential StatsIV. Sample DataV. Nominal vs. Interval DataCurrent LecturePopulation vs. SamplePopulation (size is N)Ex: all students at UNT- All students that own a car- All registered voters- All production workers at Motorolao You measure or count something for each of these. That is the population.Sample (size is n)Usually selected randomly- Called a simple random sample- Random samples provide a better representation of the populationDescriptive & Inferential StatisticsDescriptive Statistics: use descriptions to group statsInferential Statistics: take data and make inferences*Prescriptive: tell you what to doSample DataThese notes represent a detailed interpretation of the professor’s lecture. GradeBuddy is best used as a supplement to your own notes, not as a substitute.Can be discrete or continuous - Discrete: when you’re counting something- Continuous: when you’re measuring somethingo Level of Measurement “weak” data vs. “strong” data Weakest- Nominal (a category or label)- Ordinal (ranks)- Interval (differences are meaningful) Strongest- Ratio (the word twice makes sense)Nominal vs. Interval DataNominal data are the weakest data and represent a category- Ex: gender, ethnicity, hair color- Only discuss proportions here o Ex: In a sample population we night find that 58% of the people are female and 42% maleInterval Data Difference between values is meaningful- i.e.