Inferential Statistics - 1.5 pages

Organizing and Describing Data

Saturday, November 1st, 2014

Being unorganized really puts a damper on a work environment. Having no organization in the workplace can create a feeling of scatter-ness which is stressful and irritating. Becoming and staying organized in the work place is extremely effective and helps boost performance, making employees efficient and effective which creates a happy and healthy atmosphere. Being organized can include planning, time management, meeting deadlines, scheduling, coordinating, and general organizing of office/desk.

Organizational skills help with: time management, stress reduction, goal setting, and project planning. Employees with good organizational skills are known to be more productive which in return helps avoid mistakes such as forgetting appointments or even being late for appointments (time management). A well organized person can typically save a few hours during the work week which decreases their stress level, making work happier, less tiring, and leaving them feeling accomplished instead of rushed and beaten down.

Planning is a great tool used in the place of work as it is essential for determining when supplies are needed and how much is needed, arranging important documents, keeping important contact information, and especially work projects as it helps with dividing the project into various tasks by planning them ahead of time to create goals each day.

Time management is a great tool used in organizing. Time management allows you to prioritize your work and tasks that involve around it; it helps with remaining focused which is great for being more productive and getting more work done. To remain organized is to have great time management skills. Having the ability to be organized is an important skill to have to meet deadlines, prioritize tasks, and be productive.

Describing data

Describing data is important in statistics because it allows you to put it into easy understandable summaries. The summaries then are used to compare sets of numbers from various sources and to evaluate relationships. In statistics, it’s importance to construct measures to describe the data numerically first, then graphically examining the data last. Being organized (above reading explained it’s importance), is a great tool in statistics because it allows you to take a set of numbers in order to cont them and see how often each value occurs. An example would be to look at diagnosed prostate cancers and count how often in a 2-year period cancer is diagnosed as stage A, B, C, or D. For example, of 236 diagnosed cancers, 186 might be stage A, 42 stage B, six stage C, and two stage D. Because it is easier to understand these numbers if they are presented as percentages, we say 78.8% (186 of 236) are stage A, 17.8% (42 of 236) are stage B, 2.5% (six of 236) are stage C, and 0.9% (two of 236) are stage D (Sonnad, S. S., 2002). In that type of calculation, two definitions are important. By starting with describing and graphing of study data, better analysis and clear presentation of data will result; therefore, descriptive and graphic methods will improve communication of important research findings.

There are two main ways of describing data: mathematically and visually. Mathematical descriptions are typically measures of central tendency such as mean, median, and mode; and measures of dispersion such as a range and standard deviation. Descriptive data can include tables, charts, graphs, and numbers in order to summarize present raw data, and organize.

Describing data includes:

Mean: arithmetic average for a group of data.

Median: middle item in a group of data when the data are ranked in order of magnitude.

Mode: most common value in any distribution.

Histogram: Bar graph that represents a frequency distribution.

Frequency distribution: Table that shows the body of data grouped according to numeric statistics.

Frequency polygon: Graphic method of presenting a frequency distribution.

Inferential statistics: Use of sample statistics which infer characteristics about the population.

Nominal data: Data with items that can only be classified into groups. The groups cannot be ranked.

Normal distribution: A bell-shaped curve that describes the distribution of many phenomena.

Percentage distribution: A frequency distribution that contains a column listing the percentage of items in each class.

Quartile: Value below which 25% (lower quartile) or 75% (upper quartile) of data lie.

Sample: A subset of the population that is usually selected randomly. Measures that summarize a sample are called sample statistics.

Graphical descriptions have various ways of representing data such as simple and complex graphs like 3D graphs that can reveal structure static that other simple graphs cannot.

Frequency distribution

Organization is an essential step in frequency distribution because it allows you to put it into a meaningful form so a trend can form (if there is any to form), which would emerge and become easily seen from the data which has been organized. Frequency distribution allows researchers to take a look at the entire data easily and conveniently by showing if the observations are high, or, or if they are concentrated in a specific area across an entire scale.

Frequency distribution tables show various categories of measurement while also showing the different number of observations in each category. In order to properly make a frequency table, it’s important to have an idea of the values (maximum and minimum.

The four important characteristics of frequency distribution are: measure of central tendency (mean, median, and mode), measures of dispersion (range, variance, and standard deviation), flatness or peakednes (kurtosis), and extend of symmetry/asymmetry (skewness).

Reference:

Dantic, D. (n.d.). Public Health Biostatistics I. Retrieved November 3, 2014, from 

http://www.peoi.org/Courses/Coursesen/phbiostat1/contents/frame2.html

Manikandan, S. (2011, January). Frequency distribution. Retrieved November 3, 2014, from 

     http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3117575/#!po=83.3333

McQuerrey, L. (n.d.). Importance of Organizational Skills in the Workplace. Retrieved from 

     http://woman.thenest.com/importance-organizational-skills-workplace-14937.html 

Sonnad, S. S., PhD. (2002). Describing Data: Statistical and Graphical Methods. Retrieved November 

     3, 2014, from http://pubs.rsna.org/doi/pdf/10.1148/radiol.2253012154