Define statistics
Statistics can be described as the study of gathering, organizing, analyzing, presenting and interpreting data. The use of statistics has been put to look into scientific or social problems that may take be affecting the society (Berenson, Levine, Szabat, & Krehbiel, 2012). The application of statistics will usually require the identification of the population in focus. This could be the ailing group in a scientific study or a group affected by war in a social problem study. The use of statistics has been identified as a means of providing observations, inferences and effects of manipulating of data. There are numerous statistics used in evaluating data collected including mean, median, mode and range. These various data evaluation techniques can be represented using graphs, tables and sometimes pie charts. There more ways statistical data can be represented including histograms and stem and leaf plots. The data collected is usually represented in a way that one value is related to another.
Contrast quantitative data and qualitative data
There are two types of data that are most common in any given research. These are the quantitative and qualitative data. Quantitative data is the data or information collected that represents a particular quantity. In this regard, quantitative data represents various measurements that can be written down in form of particular numeric values (Berenson, Levine, Szabat, & Krehbiel, 2012). The length, age, volume, size, weight and even time are some of the various examples of quantitative data that can be collected in a research. As a result, quantitative data is most commonly used in conducting statistical research. On the other hand, qualitative data represents data using its various qualities (Berenson, Levine, Szabat, & Krehbiel, 2012). The information collected that cannot be measured in terms of numeric data could be identified as qualitative. This may include various things such as emotions, beliefs, color and even perceptions. The qualitative data is usually believed to be descriptive information and as such are abstractive.
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Evaluate tables and charts used to represent quantitative and qualitative data
In a group of 72 patients who are 85 years and older a s 29 item functional disability test is used to rate the dependency or independence of the individuals. The items are rated as 0 for independent and 3 for dependant as the observed disability for the patients. The data is represented as follows.
| SMAF Score Interval | Frequency | Cumulative Frequency | Percentage | Cumulative Percentage |
0-4 | 16 | 16 | 22.2% | 22.2% |
5-9 | 13 | 29 | 18.1% | 40.3% |
10-14 | 13 | 42 | 18.1% | 58.3% |
15-19 | 8 | 50 | 11.1% | 69.4% |
20-24 | 10 | 60 | 13.9% | 83.3% |
25-29 | 4 | 64 | 5.6% | 88.9% |
30-34 | 2 | 66 | 2.8% | 91.7% |
35-39 | 3 | 69 | 4.2% | 95.8% |
40-44 | 1 | 70 | 1.4% | 97.2% |
45-49 | 2 | 72 | 2.8% | 100% |
Total | 72 |
Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/2976575
Through the above table it is evident the various classes in which the 72 patients belong. A lower class of between 0-4 and 20-24 for example demonstrate the high independence of a patient in the study. The study uses quantitative data as a means of describing the dependence or independence of the patient in a bid to understand the number of patients that require assistance in their day to day functioning.
Qualitative data can also be represented in tabular form. This may represent data of the qualification of different CEOs who have been identified as the best paid in a given country. The data may be represented as follows
Degree | Frequency | Relative Frequency | % Frequency |
None | 2 | 0.08 | 8 |
Bachelor | 11 | 0.44 | 44 |
Masters | 7 | 0.28 | 28 |
Doctorate | 5 | 0.20 | 20 |
Total | 25 | 1.00 | 100 |
Retrieved from http://web.thu.edu.tw/wenwei/www/Courses/statistics/ch2.1.pdf
The above data can be identified as the representation of how many CEOs have degrees from those with none. This study can conclude that in order one to become one of the best paid CEO one must have at least a bachelor’s degree qualification. This is despite there being a few who do not have any qualification.
Levels of data measurement
There are different levels of measuring data that can be used by a researcher in a study. These levels can be classified into four categories including nominal, ordinal, interval and ratio. These categories are identified as scale types that represent the measured dependent variable in the statistical analysis. A nominal scale represents responses in a single name or category. This is where gender, color, or preferred hand for writing can be used and therefore the scale is not in any significant nominal order (Osherson & Lane, 2016). Ordinal scales will usually be used to identify the degree of which two subjects possess the dependant variable. For instance, a researcher seeking to identify the satisfaction of customers after purchasing a television would specify their perceptions as “very satisfied”, “somewhat satisfied”, “neither satisfied nor dissatisfied”, “somewhat dissatisfied” or “very dissatisfied”. This represents the main difference between nominal and ordinal scales (Osherson & Lane, 2016).
In the case of an interval scale, the researcher uses numerical scales whereby the intervals have similar interpretation all through (Osherson & Lane, 2016). In an example, the difference between 20 kilograms and 30 kilograms represents the same difference as 70 kilograms and 80 kilograms. The interval between each 10 kilogram interval represents the same physical meaning in terms of weight of an individual. The ratio scale represents the fourth level of measurement in statistics where a measurement can have the value of zero (Osherson & Lane, 2016). Through this scale division between two points have equal distance and the rankings provided will usually determine their sizes.
Role of statistics in business decision-making
Statistics is an important tool in making business decisions. This is where it may be used in backing up judgments particularly where leaders may seek to provide employees to change practice into a different direction by providing accurate data. Statistics may be used to focus on a bigger picture where a decision was identified as accurate in identifying consumer or employee needs with potential benefits for the employer (Black, 2011). In an organization seeking to make improvements on its various management practices will usually require the use of statistics. This is where statistical data is used to represent production operations and how the organization can manage them minimize waste and costs incurred as seen in Lean manufacturing. Statistics can be used in making connections between two events in an organization (Williams, 2016). This could be seen in the case of incurred losses and customer dissatisfaction of the consumers. Research and development is an important aspect of business where an organization may seek to identify ways of improving their products to better satisfy customer needs.
Two business research questions
What are the main factors causing low production rate among employees?
What are the consumers’ behavior in purchasing a product?
References
Berenson, M., Levine, D., Szabat, K. A., & Krehbiel, T. C. (2012). Basic business statistics: Concepts and applications . Pearson Higher Education AU.
Black, K. (2011). Business statistics: for contemporary decision making . John Wiley & Sons.
Osherson, D. & Lane, D. M. (2016) Levels of Measurement . Retrieved from http://onlinestatbook.com/2/introduction/levels_of_measurement.html
Williams, J. T. (2016) The Importance of Statistics in Management Decision Making . Retrieved from http://smallbusiness.chron.com/importance-statistics-management-decision-making-4589.html
