Unit 3 Discussion – Business Statistics
Quantitative variables include those variables in statistics, whose values are an outcome of measuring or a process involving counting something. On the other hand, a qualitative variable is that with attributes that are not measurable, or rather the values are not a result of measuring or counting process. Qualitative variables can only be identified by a name, or a category (Afzal & Rizwi, 2013). Examples of quantitative variables include weight, height, balance in a bank account, number of siblings or children, number of pets owned, among others. The crucial factor, in this case, involves determining whether the values of the variable can be added. On the contrary, qualitative variables include the hair colour (for example blond, brunette, black), religion, profession, political party, race (for example, black, Asian, Latino, etc.), type of hat, favourite author or books, type of pet owned, among others. Therefore, the values of qualitative variables cannot be added due to categorical nature.
A discrete random variable takes on either a finite or at the best a determined infinite set of discrete values for instance the integers. Thus, the probability distribution is a mapping of each value of the random variable to a certain probability. For example, values obtained after rolling a die or scores received on a certain total possible mark. On the other hand, a continuous random variable takes all values carrying within a given interval of numbers (Mirabella, 2011). For example, selecting numbers between the interval of 0 and 1. Furthermore, the cumulative distribution function of a random continuous variable is continuous.
Nominal variables are used to define or measure characteristics that lack quantitative value or rather lack any form of implications regarding the order (Afzal & Rizwi, 2013). For example, gender, colour, etc. The differences between the values held by such variables are only arbitrary. Ordinal variables majorly put significance on the order of values, which however the differences between two distinct values are not known. For example, ranking or using Likert scales for a response. Interval variables use numeric scales whose differences in values and order is significant. For example, the temperature in Celsius is presented in an interval measurement. Ratio variables hold values whose measurement has an absolute zero point, for instance, income. In this case, the ratio scale is meaningful since the income of a person has a true zero.
References
Afzal, M., & Rizwi, F. (2013). Biostatistics and Data Types. Journal of Islamabad Medical & Dental College (JIMDC), 2(2), pp.103-103.
Mirabella, J. (2011, October 1). Business Statistics. (Savant Learning Systems) Retrieved from Bethel University, College of Professional Studies: https://www.betheluniversityonline.net/cps