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10/20/21, 9:10 PM1.1 Overview – Boundless Statistics for Organizations2/431.1 Overview1.1: Overview1.1.1: Collecting and Measuring DataThere are four main levels of measurement: nominal, ordinal, interval, and ratio.Learning ObjectiveDistinguish between the nominal, ordinal, interval and ratio methods of data measurement.
10/20/21, 9:10 PM1.1 Overview – Boundless Statistics for Organizations3/43Key TakeawaysKey PointsRatio measurements provide the greatest flexibility in statistical methods that can beused for analyzing the data.Interval data allows for the degree of difference between items, but not the ratio be-tween them.Ordinal measurements have imprecise differences between consecutive values, buthave a meaningful order to those values.Variables conforming only to nominal or ordinal measurements cannot be reasonablymeasured numerically, they are often grouped together as categorical variables.Ratio and interval measurements are grouped together as quantitative variables.Nominal measurements have no meaningful rank order among values.Key Termssamplingthe process or technique of obtaining a representative samplepopulationa group of units (persons, objects, or other items) enumerated in a census or from which asample is drawnExampleAn example of an observational study is one that explores the correlation betweensmoking and lung cancer. This type of study typically uses a survey to collect obser-vations about the area of interest and then performs statistical analysis. In this case,
10/20/21, 9:10 PM1.1 Overview – Boundless Statistics for Organizations4/43the researchers would collect observations of both smokers and non-smokers, per-haps through a case-control study, and then look for the number of cases of lungcancer in each group.There are four main levels of measurement used in statistics: nominal, ordinal, interval,and ratio. Each of these have different degrees of usefulness in statistical research. Datais collected about a population by random sampling .Nominal measurements have no meaningful rank order among values. Nominal data dif-ferentiates between items or subjects based only on qualitative classifications they be-long to. Examples include gender, nationality, ethnicity, language, genre, style, biologicalspecies, visual pattern, etc.Defining a populationIn applying statistics to a scientific, industrial, or societal problem, it is necessary to beginwith a population or process to be studied. Populations can be diverse topics such as “allpersons living in a country” or “all stamps produced in the year 1943”.

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