Unit 1 - Fundamentals of Statistics

Abstract
To examine and analyze the results of a survey by focusing on the qualitative and quantitative data.

Introduction Data collection is extremely important. Many people use some form of data collection to help support an argument, for research, for planning purposes, etc. People use data collection for many different reasons. However when using data collection one must ensure that the data collected is accurate and unbiais.
Chosen Variables The variables that I chose to analyze was gender and intrinsic. This data shows job satisifaction ratings from the male’s point of view and the female’s point of view. As a female in corporate America I was very interested to see how much of a difference would there be when it comes to job satisfaction from each gender’s point of view. We often hear that sometimes men are giving a better advantage over women so I would like to analyze the data to see if there is a significant difference between the two genders.
Difference in variable types Qualitative variables and quantitative variables vary because of how it measured. Qualitative variables tend to have a greater influence on the outcome because of its ability to affect other parameters of the study. While quantitative variables are purely mathematical data or data that can be measured.. Qualitative research is typically suggested in earlier phases of research since the research may not know EXACTLY what he or she is looking for. Whereas in quantitive variables the research knows exactly what he or she is looking for and this usually used at the latter phases of research. Although the two differ in order to have an accurate study you must incorporate both variables within your study (wilderdom.com)
Descriptive statistics: Qualitative variable

|Gender |Age |Department |Position |Tenure |
|1 |1 |1 |1 |3 |
|1 |1 |2 |2 |1 |
|1 |1 |3 |1 |1 |
|1 |2 |2 |1 |1 |
|1 |2 |1 |1 |1 |
|1 |2 |3 |1 |2 |
|1 |2 |2 |2 |2 |
|1 |2 |3 |1 |1 |
|1 |3 |1 |2 |2 |
|1 |2 |2 |1 |1 |
|1 |1 |1 |1 |3 |
|1 |3 |3 |1 |1 |
|1 |1 |3 |1 |3 |
|1 |2 |2 |1 |1 |
|1 |1 |2 |1 |1 |
|1 |1 |2 |2 |2 |
|1 |3 |3 |1 |1 |
|1 |2 |1 |2 |3 |
|1 |2 |1 |1 |2 |
|1 |3 |3 |1 |2 |
|1 |2 |2 |1 |3 |
|1 |1 |3 |2 |1 |
|1 |2 |1 |2 |1 |
|1 |2 |1 |2 |1 |
|1 |2 |1 |1 |1 |
|1 |3 |3 |2 |2 |
|1 |2 |3 |1 |1 |
|1 |3 |2 |2 |1 |
|1 |1 |3 |1 |1 |
|1 |2 |2 |1 |1 |
|1 |3 |3 |1 |1 |
|1 |2 |1 |1 |1 |
|1 |1 |3 |1 |1 |
|1 |2 |2 |1 |1 |
|1 |2 |1 |1 |1 |
|1 |2 |3 |1 |2 |
|1 |2 |2 |2 |2 |
|1 |2 |3 |1 |1 |
|1 |3 |1 |2 |2 |
|1 |2 |2 |1 |1 |
|2 |3 |3 |1 |1 |
|2 |3 |3 |1 |1 |
|2 |2 |2 |1 |2 |
|2 |2 |2 |1 |3 |
|2 |1 |2 |2 |1 |
|2 |2 |3 |1 |1 |
|2 |2 |2 |2 |3 |
|2 |2 |2 |1 |2 |
|2 |1 |3 |2 |1 |
|2 |2 |2 |1 |2 |
|2 |3 |3 |1 |1 |
|2 |2 |2 |1 |2 |

Explanation of descriptive statistics The qualitative variables would be gender, age, department, position. All this information varies and influence each other. Based on this information you will get different results from the different gender, ages and positions. Also this information appears to be bias and unbalanced as we have more males polled than females. It would be an inaccurate reading to compare a large tested male gender and compare the results to a smaller tested female gender. I would suggest testing the same amount of females and males and ensure that you include the different positions to get a fair reading.
Descriptive statistics: Quantitative variable

|Job Satisfaction |Intrinsic |Extrinsic |Benefits |
|5.5 |5.5 |5.5 |5.4 |
|5.5 |5.3 |5.7 |2.3 |
|5.2 |4.7 |5.6 |4.5 |
|5.5 |5.5 |5.5 |5.4 |
|3.9 |2.1 |5.7 |2.3 |
|5.5 |5.4 |5.6 |5.4 |
|5.9 |6.2 |5.5 |6.2 |
|4.9 |5.2 |4.6 |6.2 |
|5.5 |5.3 |5.7 |2.3 |
|5.2 |4.7 |5.6 |4.5 |
|5.5 |5.5 |4.6 |6.2 |
|4.9 |5.2 |5.7 |6.2 |
|5.2 |4.7 |5.5 |6.2 |
|5.5 |5.5 |4.6 |2.3 |
|5.5 |4.7 |5.7 |2.3 |
|4.9 |5.3 |5.6 |4.5 |
|3.9 |4.7 |5.5 |2.3 |
|5.2 |4.7 |5.6 |4.5 |
|5.5 |5.5 |5.5 |5.4 |
|4.9 |5.2 |4.6 |6.2 |
|3.9 |2.1 |5.7 |2.3 |
|5.2 |4.7 |5.6 |4.5 |
|5.5 |5.4 |5.6 |5.4 |
|5.9 |6.2 |5.5 |6.2 |
|4.9 |5.2 |5.6 |2.3 |
|5.5 |5.3 |5.6 |4.5 |
|5.5 |5.4 |4.6 |6.2 |
|5.5 |5.2 |5.6 |4.5 |
|5.2 |4.7 |5.6 |4.5 |
|5.5 |5.5 |5.5 |5.4 |
|4.9 |5.2 |4.6 |6.2 |
|3.9 |2.1 |5.7 |2.3 |
|5.2 |4.7 |5.6 |4.5 |
|5.5 |5.5 |5.5 |5.4 |
|3.9 |2.1 |5.7 |2.3 |
|5.5 |5.4 |5.6 |5.4 |
|5.9 |6.2 |5.5 |6.2 |
|4.9 |5.2 |4.6 |6.2 |
|5.5 |5.3 |5.7 |2.3 |
|5.2 |4.7 |5.6 |4.5 |
|4.9 |5.2 |4.6 |6.2 |
|4.9 |5.2 |4.6 |6.2 |
|5.2 |4.7 |5.6 |4.5 |
|5.5 |5.4 |5.6 |5.4 |
|5.5 |5.3 |5.6 |5.4 |
|5.2 |4.7 |5.5 |5.4 |
|5.4 |6.2 |5.7 |6.2 |
|5.5 |5.3 |5.6 |5.4 |
|4.9 |4.7 |5.5 |6.2 |
|5.2 |4.7 |5.6 |4.5 |
|4.9 |5.2 |4.6 |6.2 |
|5.2 |4.7 |5.6 |4.5 |

Explanation of descriptive statistics These descriptive statistics represent job satisfaction, intrinsic, extrinsic and benefits. All these variables can be measured. For this particular study I chose the intrinsic however all of these variable on in this study can be measured mathematically. As stated before the data collection appears to be unfair as we have more males tested than females, therefore I would suggest testing the same amount of females as males to get a fair and balanced reading of job satisfaction when it comes to gender.

Chart/Graph for qualitative variable [pic]
Description of Chart This graph shows the qualitative variable selected which is gender. As you can see in the graph a larger percent of males was tested than females. The chart shows that the number of males test comes to approximately 40% while the number of females tested is only about 12%. This shows how the data collection could be viewed as bias since there was more males tested than females.

Chart/Graph for quantitative variable
[pic]
Description of Chart The chart above shows the intrinsic data for both male and female. After reviewing the male data for job satisfaction the males have an average of about 4.9% job satisfaction while the females have a 5.1% average from job satisfaction. I don’t believe that this information is accurate since there was more males polled than females. In order for the data collection to be fair and balanced I believe that the same amount of people in each gender should have been tested. I believe that this graph could give management a false reading when it comes to job satisfaction by gender since the same amount of people were not polled.
Explanation of standard deviation and variance Standard deviation is defined as the measure of how spread out numbers are using the formulas of the square root of the variance. The variance is defined as the average of the squared differences from the mean. In other words the standard deviation can help pin point what direction one may need to go in to accomplish a goal (robertniles.com).

Importance of charts and graphs When making a presentation one must ensure that they capture the attention of the audience. This is more so the case when dealing with numbers. Graphs, charts or any type of visual helps create a vivid picture for the audience to understand what how these numbers affect the subject at hand. For example conducting a budget meeting and telling management that their 30% over their budget doesn’t really drive the point across to the audience until they have the ability to see a visual aid on where the mark for the budget is and where they are currently. Visual aids simply assist with giving the audience a visual of the message your trying to convey.
Conclusion
In conclusion my advice to management would be to ensure that we test the same amount of people in each gender to have a more fair and accurate reading. This helps elimatinate the possibility of coming up with an inaccurate reading due to poor data collection.
References
(2011). Retrieved on June 30, 2012 from http://www.robertniles.com/stats/stdev.shtml/
(2010). Retrieved on June 30, 2012 from, http://wilderdom.com/research/QualitativeVersusQuantitativeResearch.html