Statistical data can be presented in two types which limits the nature of information that can be inferred from each type. These types are; quantitative and qualitative data. Quantitative data is presented numerically while qualitative data is descriptive in nature and cannot be defined using numeric information. Examples of quantitative data includes the age of a respond, salary, and any other feature that can be represented using numbers. Quantitative data is then divided into two types; continuous and discrete. Continuous data can take any number values while discrete can take only specific numbers. Examples include age and shoe size respectively. On the other hand, qualitative data is used to present qualities hence their identification as categorical data. Such data types includes the gender of a respondent, and other variables that do not follow a natural ordering of their categories. Qualitative data are used to describe variables that cannot be measured using numbers or to express a respondent’s level of acceptance to certain situations. Commonly used categories to present qualitative data include strongly agree, agree, neutral, disagree, strongly disagree. Such levels of measurement are also applied in situations determining the significance of a qualitative element.
Data values indicate the data levels and measurement of a particular variable. Factors are measured and represented on various types of data values depending on what they present. For instance a variable measuring the number of time a person smokes daily cannot be measured using the same levels or represented using the same type of values as one measuring the mean salary of a responded. These levels or measurement are; ordinal, nominal, ratio, and interval scales.
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Nominal levels are used to represent dummy variables and the data values in this case are used to indicate whether an object belongs to a particular category by coding using numerals. Interval scale represents data values that measures objects from a point of reference. This includes variables such as the nearness of an object to a particular point. On the other hand, ratio scale present data values collected from no point of reference. This includes data such as age. Ordinal scale measures variables like in nominal scale but ranks them further largest to smallest. Variable types presented this way includes data on number of cigarettes smoked in a day. The present paper analyzes the data set “Heart Rate Data Set” to identify and determine the data types and values in each of its the variables.
There are three variables in the data set “Heart Rate Data Set.” These variables are; Gender, Heart rate before exercise, and heart rate after exercise. Gender variable is represented quantitatively but measured on a nominal scale through dummy decoding where 1 represents female gender and 0 represents male gender. The variable, therefore, ranges from 0 to 1 since it is coded quantitatively. The variable is used to indicate the gender of each of the respondent whose entries in the other variables are recorded.
The second variable, heart rate before exercise or heart rate while relaxing is represented using quantitative data of continuous type. The variable is measured on a ratio scale and ranges between 59.0 and 97.3. The variable’s data indicates each participant’s heart rate before they were involved in an exercise. The last variable, heart rate after exercise is also represented using quantitative data of continuous type. The data presented is measured using a ratio scale and ranges between 68.2 and 106.7. The data indicates the heart rate of a participant after they have been involved in an exercise. It shows how the heart rate changes from the previous readings when the participants were relaxing.
