Organizing and Visualizing Data: Key Concepts in Statistics

1 Organize and Visualize Data Tanya Groomes Doctor of Business Administration, National University BUS-7105: Statistics I Dr. Fred Rispoli March 17, 2024

2 Organize and Visualize Data Concept of a Random Variable and Importance in Learning Statistics This paper discusses how to organize and visualize data related to conducting research. As the basis for statistical immersion of those random processes is defined, numerical representations of their outcomes are expressed in random variables. Fundamentally, probabilities are words that exactly describe several individual consequences of a random event (Frank & Klar, 2016). Similarly, while rolling the die, there is no way to do so, but the number on both sides of the die is a random variable. These variables come in two main types: unreal and real. Given Context: One of the ways that VR can positively impact the gaming industry is by enhancing the level of interactivity and immersion (Leinhardt & Wasserman, 1979). Discrete variables may take on different values, such as the count of children in an example of a family. Contrastingly, variables that differ randomly are continuous, taking any given value within a specific range, such as height or weight. Additionally, this paper will provide the following four chart types: pie, bar, scatterplot, and histogram, using the data sets provided. Importance of Variables in Learning Statistics These variables are the core of statistical research, built on the foundation of quantifying, measuring, and statistical analysis of various phenomenon components. Variables interact and change closely to make useful discoveries regarding circadian trends and links in data (Li, 2016). The other variables facilitate hypothesis testing, prediction, and decision-making in economics, psychology, sociology, and natural sciences. These infer the reliability of the population by taking samples; they detect and link variations and then predict future circumstances .

3 Comparing and Characters of Continuous and Discrete Variables and Challenges in Calculating Statistics Using Discrete Variables The variable world of personal and professional relationships involves many vital variables in data analysis (Li, 2016). Nominal variables, which can be numeric or archival, are sufficient grounds for both settings. Employees like managers, accountants, or technicians may typify nominal variables in a workplace environment. These categories do not have any hierarchical order built in over them, but they help classify employees based on their respective roles in the organization (Leinhardt & Wasserman, 1979). Equally, the favorite colors of different people can be described by nominal variables such as blue, red, or green. These colors reflect personal preferences without ranking them in any specific order, which is why a particular color can be a favorite choice of one person. In contrast, another person has a different favorite color. For instance, sequential variables are present here in professional and private domains with categories like usually five carefully placed steps that indicate the order of the operations but not interval sizes. For example, in a business setting, customer satisfaction levels ranging from "very pleased" to "very dissatisfied" represent the characteristic of the ordinal variables. Thus, these dimensions create a satisfaction index that does not equalize the mentioned factors but still indicates diversity (Li, 2016). There is a correspondence between this situation and personal life in which holding the educational status of a diploma, bachelor's degree, or master's degree signifies an ordinal variable showing gradual achievement in the academic world. Ratio variables might be seen in both aspects of life - personal and professional, where both theoretical and practical aspects can be reflected. However, the ratio variables have the same problem: the meaningful zero points and the equal interval between values. Income, per se, is a ratio variable in formal settings where the comparison or measure of income is unequivocal

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4 (Leinhardt & Wasserman, 1979). Age also presents the typical class of constant ratios, measuring the time since birth. These factors provide valuable insights into the financial status and behavior change in their demography, which could lead to critical decision-making and analysis of the problem in multiple sectors. Levels of Measurement Nominal: Nominal variables present categories with no inherent ranking or order among various items such as gender, race, and marital status (Li, 2016). The purpose of these categories is to organize the data between themselves, but it does not mean there is any specific relationship between them. Interval: Unlike interval variables, ordinal variables arrange the categories in a definite ranking or order, though the intervals between the ranking results are not always equal. An example might include educational level, from high school through college to graduate levels, which is an ordinal variable showing a process of continuation in academic achievement (Tiemann, 2010). Ordinal: Likewise, socioeconomic status falls into the ordinal type, where individuals are ordered without any assumption of equal intervals between the ranks. Rather than prior consideration of what the statistical signifies, the level of observation to be used should be chosen in light of several variables, such as the data's characterization and research objectives. Ration: In most cases, the interval or ratio levels are proper, as they are more accurately approximated and can be calculated (Tiemann, 2010). Only those scientific challenges that were brought along determined the data collection methods (Leinhardt & Wasserman, 1979). While evaluating the characteristics of the data and the level of measurement that can map their

5 analytical goals, researchers should also consider the nature of the variables they are considering for this research. Continuous and Discrete Variables A world of differences separates the behavior of a continuous and a discrete variable correlated to the application context. "Continuous variables" refers to a combination of values given in a particular range and displayed as a continuous scale (Tiemann, 2010). The class of the continuous variables can be demonstrated by exhibiting that we have height, weight, and temperature. These variables are the realms of infinite possibilities, which offer an unlimited data source between any two points and allow for evaluation and analysis. Sodis plays the role of discrete variables as the only values they can assume are distinct and separated, and these are often counted rather than measured. Discrete variables are represented by the number of students in a class, the number of cars in a parking lot, and goals scored by teams in a soccer game (Tiemann, 2010). Discrete variables differ from continuous variables in that the former can only be counted in nature and cannot take on noncountable/irregular values. In contrast, continuous variables take any value from their range regardless of whether it is a number or a letter (Frank & Klar, 2016). Although they have diversity in their natures, both variables are important in the statistical analysis procedure, as continuous variables give a variable with a certain level of accuracy and flexibility compared with their discrete counterparts, which is a collection of distinct or enumerated things. Examples of Variables from Professional and Personal Life In both cases, personal and professional spaces of the data, but whatever variables you deal with in understanding and analyzing the data are essential. In both cases, we have a case of

6 variables that are free of charge (Tiemann, 2010). They are widely used in the realm of categorical variables with nominal data. In a given organization, job positions like manager, accountant, or technician would be the nominal factors that would classify employees (Leinhardt & Wasserman, 1979). This chart has no predetermined sequence of roles since the employees are neatly sorted according to their position in the organization. Likewise, it could be the case that blue, red, or green colors that we prefer serve as nominal variables as well, categorizing the preferences of individuals but not arranging them in a particular order. Interval variables, defined by equal intervals between the values but which do not feature an actual zero point, are, arguably, as applicable to personal scenarios like voting during elections as they are to professional contexts (Frank & Klar, 2016). An example of a ratio scale consonant with most situations is when a temperature is measured on a Celsius or Fahrenheit- degree scale. Whether measuring weather conditions during one's profession or manipulating the thermostat at home on given days, temperature as an ordered variable is a matter of course, as the consistent difference of values determines its value interval. Rate factors hold a unique space in the home and work life, characterized by their key zero points and equal space intervals between the numbers. Take income, for illustration, as a characteristic used to rank employees' competencies based on earnings levels, which enables the calculation and monitoring of wages (Leinhardt & Wasserman, 1979). Likewise, various variables ranging from years to months serve as the age ratio variable for personal life. This can help define a continuously changing world of time that progresses from birth. These variables render helpful information regarding financial well-being and demographics that many decision- makers and analysts in different sectors find helpful.

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7 Data Charts Figure 1 This pie chart represents the Marital Status of the total test population Note: This pie chart shows that half of the test population is widowed; those who are single, married, or separated make up the rest of the population. Figure 2 The Bar Chart represents the Age of employees by Year Note: This bar chart shows the age of employees by their age in Years. It appears that most employees range from ages 27-46

8 Figure 3 This histogram chart shows the level of education of the target population. Note: This histogram states that most of the population has a high school diploma, followed by those with an Associate Degree, then a Bachelor's, then a Master. Figure 4 This scatterplot chart represents the earnings of employees by the age of the employee. Note: The scatterplot chart shows the income level of the employees by age in years. It appears that the older you are the higher your salary.

9 Conclusion This paper discussed how to analyze variables and why they differ. It also discussed the four levels of measurement and talked about discrete variables. It ended with formulating various charts using the data provided.

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10 References Davis, S., & Davis, E. (2015). Data analysis with SPSS software : Data types, graphs, and measurement tendencies . Momentum Press. Frank, J., & Klar, B. (2016). Methods to test for equality of two normal distributions. Statistical Methods & Applications, 25(4), 581-599. https://search.ebscohost.com/login.aspx? direct=true&AuthType=sso&db=bth&AN=119456507&site=eds- live&scope=site&custid=natuniv Leinhardt, S., & Wasserman, S. S. (1979). Exploratory data analysis: An introduction to selected methods. Sociological methodology , 10 , 311-365. Li, J. C. H. (2016). Effect size measures in a two-independent-samples case with nonnormal and nonhomogeneous data. Behavior research methods , 48 , 1560-1574. Salcedo, J., & McCormick, K. (2020). SPSS statistics for dummies . For Dummies. Tiemann, T. K. (2010). Introductory business statistics . BCcampus.

BUS-7105 Groomes T- Week 2 Assignment

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