Online Statistics Part 1

Probability theory is an aspect that is applicable on an everyday basis. A particular situation in which I may use probability theory to reach a decision in public management is to investigate equal employment prospects within the organization. For instance, there is a need to assess whether the organization hires women in the same manner as men, especially in positions that do not necessitate certain gender traits. Therefore, in this case, probability theory can be beneficial in probability theory, specifically Bernoulli process. That is, a process can either generate two possible outcomes, which are male or female. Probability plays a pivotal part in this regard because the probability of a certain results is the proportion of times that outcome would take place in a long and extensive run of repeated observations. In this instance if men and women are equally represented in the labor force, then the probability of selecting a woman by chance (p) presumably equals .5, as does the probability of drawing a man (q). Therefore, in this regard, it becomes possible to make a sensible judgment regarding the hiring practices of the organization. It is imperative to note that guessing in this particular instance of decision making would not be correct or right because it would be misleading and give a wrong judgment regarding the hiring process of the organization (McNabb, 2015).

Part 2

There is a great deal of materials covered in the Online Statistics Education: A Multimedia Course of Study. The content begins by defining statistics and delineating its significance. It shows that statistics is imperative as it is included to incorporate credibility to an argument or recommendation. In addition, statistics provides tools that one requires so as to react logically to information one hears or reads. Furthermore, the content shows that statistics can be classified into descriptive statistics and inferential statistics. On the one hand, descriptive statistics encompass numbers that are employed...

They are solely descriptive and do not take into account generalizing past the data at hand. On the other hand, inferential statistics takes into account the mathematical processes whereby the information regarding the sample is converted into intelligent guesses regarding the population as a whole. This encompasses sampling strategies, which necessitates every member of the population to have a similar chance of being selected into the sample. There are other sampling strategies such as stratified sampling. This technique can be employed if the population has a different distinct "strata" or groups. In stratified sampling, the first step encompasses identifying members of your sample that belong to every group. Subsequently, the researcher randomly samples from each of those subgroups in such a manner that the sizes of the subgroups within the sample are relative to their sizes in the population.
Subsequently, the content outlines variables and the six different kinds of variables. To begin with, variables are defined as properties or characteristic of a particular event, object, or individual that can take on dissimilar values or amounts. There are independent and dependent variables. As a whole, independent variable is manipulated by the researcher and its effect on the dependent variable is measured. Secondly, there are qualitative and quantitative variables. Qualitative variables are those that express a qualitative characteristic for instance hair color, eye color, religion, favorite movie, gender, and others. It is imperative to note that the values of a qualitative variable do not denote a numerical arrangement. On the other hand, qualitative variables are now and again referred to as categorical variables. The inference of this is that quantitative variables are those variables that are measured with respect to numbers. Lastly, there is the classification of discrete and continuous variables. On the one hand, discrete variables are discrete points of scale, for instance the number…