Develop a 1,050-word response addressing each of the following prompts: - Define
ID: 3351632 • Letter: D
Question
Develop a 1,050-word response addressing each of the following prompts:
- Define statistics with citation and reference.
- Contrast quantitative data and qualitative data. Use two peer reviewed references.
- Evaluate tables and charts used to represent quantitative and qualitative data.
- Describe the levels of data measurement.
- Describe the role of statistics in business decision-making.
- Provide at least two business research questions, or problem situations, in which statistics was used or could be used.
Use two references.
Explanation / Answer
The Concepts of Statistics
Statistics is the study of data collection, analysis, interpretation, organization, and presentation. It is a broad subject with a large application in many fields. It can also be defined as a methodology for collecting, analyzing, interpreting and concluding from the information (Freedman, Pisani, & Purves, 1978). Scientists and mathematicians evaluate accumulated data and make conclusions from them. The concept of data collection is very crucial for any person collecting data as it is the basis for statistics (Isotalo, et al. 2015).
In statistics, there are two general types of data: quantitative data and qualitative data. Despite both being methods of data collection, some aspects make them different. Quantitative data is all about quantities, which is information that can be measured and written down in the form of numbers (Davison & Sharma, 1988). For instance, when one determines his weight, height, the size of the shoe they wear, distance from home to the workplace, the information is recorded in the form of numbers. Such numbers can be added and the common mean determined for the sake of analysis. Data are gathered through measuring method and analyzed through statistical inferences and numerical comparisons.
On the other hand, qualitative data is the information about qualities. Unlike in quantitative data that can be measured, qualitative data cannot be measured. Some examples of qualitative data include the softness of soil, the mental capability of a person, accuracy level, service quality in the banking industries among other features that cannot be measured (“Socialresearchmethods”, 2016). Observations and interviews are the methods used to collect qualitative data, and themes analyze the data.
Both quantitative and qualitative data can be represented using graphs and charts. It is because such representations make it easy to analyze and make interpretations. For quantitative data the following charts and graphs can be used to represent the data:
Histograms are used to represent quantitative data whereby bars are contained in figures. A range of values, known as classes is listed at the bottom. For classes with greater frequencies taller bars are used while those with smaller rates are represented with shorter bars. The stem and left plot split each value of quantitative data set in pieces. This approach provides a way that lists all information in a compact form. A dot plot is also another hybrid method that contains both the features of the leaf and stem plot and histogram method.
Qualitative data are represented using bar graphs. A bar graph includes a bar for each category of set qualitative data. The data is represented in the form of bars whereby important groups are stressed. Pie charts are also majorly used to represent data in the form of circles that are divided into slices. The different slices are divided concerning degrees to represent some qualities.
There are four basic methods of data measurement. The levels include:
Normal scale: also referred to as the grouping or categorization level is the lowest level of analysis and most often used with variable that are categorical in nature rather than quantitative (Davison & Sharma, 1988). Variables are classified in categories for this level.
An ordinal scale is the second level of measurement. For this level, it possesses a relatively small level of the property of magnitude. Objects are ordered according to whether they possess more, less or the same amount of the variable being measured.
The third level is the interval scale which combines the aspects of ranking and categorization. It allows arithmetic computation of the data collected in statistical analysis. The interval level does not have the absolute zero point.
The last level comprises of a ratio scale. It is the most powerful level as it has a unique zero origin and incorporates all the properties of the above three sizes (Davison & Sharma, 1988). It allows comparability, for example, the income of an item.
The concepts learned in statistics are very crucial for business application. Performance management is among the areas that require knowledge and concept in statistics. A manager gathers data about the performance of employees about productivity. Such data is collected using the statistical methods. The manager can again analyze the data to and determine how productivity can be improved. Another application of statistics includes decision making in alternative scenarios. For instance, resources in commercial enterprises are mostly limited, and decisions have to be made to determine the type investments to undertake. Knowledge in statistics can assist to collected data and evaluate the viable opportunities that a business manager should prioritize. Decision making also requires skills in research and development. A survey may justify resource allocation for a product development.
Research in commercial and non-commercial enterprises is very crucial. There are very any business research problem situations or questions that have been conducted or could be performed using the concepts of statistics. For example, ‘the impacts of taxation to small and medium enterprises in the United States of America is research that requires skills in statistics to be accomplished. One has to gather data to be able to draw conclusions from such an analysis. Another research question would be ‘the impacts of technology on business performance and growth.'
In conclusion, statistics concepts are widely applicable to many situations. People do not blindly make decisions but consider a variety of variations to arrive at the best choices. The most successful firms are the ones that allocate resources for the purpose of statics so that they can enhance growth and profitability.
References
Davison, M. L., & Sharma, A. R. (1988). Parametric statistics and levels of measurement. Psychological Bulletin, 104(1), 137-144. doi:10.1037/0033-2909.104.1.137
Freedman, D., Pisani, R., & Purves, R. (1978). Statistics. New York: Norton.
Isotalo, Jarkko, Simo Puntanen, and George P. H. Styan. (2015) "A Useful Matrix Decomposition And Its Statistical Applications In Linear Regression". Communications in Statistics - Theory and Methods 37.9 (2008): 1436-1457. Web.
Socialresearchmethods.net. (2016). Levels of Measurement. [online] Available at: http://www.socialresearchmethods.net/kb/measlevl.php [Accessed 21 Nov. 2016].
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