This document provides a reflection on analyzing statistics. It discusses how statistics impact daily life and decision making. There are two primary types of research: quantitative and qualitative. Quantitative research uses numerical data that can be statistically analyzed to identify patterns and test hypotheses. Qualitative research uses methods like interviews and observations. The document then discusses a case study involving a company survey and analyzing the results to test hypotheses about employee morale and turnover. It identifies different variable relationships and the need for a larger sample size to draw accurate conclusions.

1. Running head: Analyzing Statistics 1
Reflection of Analyzing Statistics
Laura Babin, Rose Krohn, Talena Peterson, Preston Stamper, Ashley Walters
Learning Team C
QNT/351
September 13, 2012
Margaret Martin

2. ANALYZING STATISTICS 2
Reflection of Analyzing Statistics
Statistics is a word commonly used in daily discussion. The presence of statistics follows
society in almost every decision a person can make. For example, what college to attend based
on how well previous students have done, or betting on a particular horse during a race because
he is said to win (Lind, D. A., Marchal, W. G., … 2011).. Even on Wall Street financial experts
are constantly checking the numbers related to stocks such as the Dow Jones or Nasdaq. Reading
the numbers and percentages off of a board is the easy part; converting raw data into a numerical
order or placing a value to qualify the significance is where it gets tricky. Understanding what
statistics do to impact decision making is extremely prevalent in daily life; however, deciding
what values to assign to particular categories or what graph best suits the information provided is
where the challenge begins.
There are two types of primary research: quantitative data collection and qualitative data
collection. Quantitative data collected is based on numbers. For example collecting data related
to peoples age or income. The information collected can be analyzed after the collection using
various statistical techniques. This analysis helps the researcher create meaningful patterns and
take a deeper dive into the data. The most important use of quantitative data is in hypothesis
testing and can help researchers in reaching conclusions. The main methods used to collect
quantitative information are using questionnaires that require input of the user’s response. The
survey can be distributed using different means, such as mail, phone, and websites. Qualitative
data can be collected using personal interviews, focus groups, and observations carried out for
the purpose of research (Siddharth, 2011). Although personal interviews can help researchers
dig into greater details, they are time consuming and expensive.

3. ANALYZING STATISTICS 3
Available data from the Ballard Integrated Managed Systems (BIMS) employee survey
afforded us the opportunity to quantify results and make an inference as to the possible reasons
for the high turnover and low morale cited by management. As such, we formulated our initial
hypothesis, that poor communication was a major contributing factor. To further test our
hypothesis, our consulting firm made the recommendation to continue with the approach of using
an employee survey instrument after removing or refining any ambiguous questions. A quarterly
collection of data will allow us to reassess the data across consistent elements while attempts are
made to increase response rate, thus giving us a better sample of data to derive conclusions from.
Ordinal data is a scale of measurement in which any given class is higher or lower in rank
than the other. For example in the given questionnaire, five is better than four, four is better than
three, three is better than two, and two is better than one. Question A, C, and D represents
nominal level data. This data can be represented in any order. There is no natural order for
these. For instance, for the gender question one can either report males first or the females.
Question B is quantitative in nature and can be classed as ratio level data. In this level of data
the zero point is meaningful because the time spent being employed at BIMS cannot be a
negative number. A person could have worked for a year but not negative one year.
Team C consultants have already delivered two responses based on the survey. The first
response was the sample size was too small. BMIS need a much higher participation rate to gain
meaningful evidence of the root causes of the problem. The second response addressed the low
scores on communication. While BMIS continues administering the survey, Team C consultants
can test the hypothesis of communication being a critical problem within the company.
Team C consultants have compared the means of the answers to question nine on the
BMIS survey. Of all 78 responses only one could not be identified as food, housekeeping, or

4. ANALYZING STATISTICS 4
maintenance. Of the remaining responses 32 came from food, 36 came from housekeeping, and
nine came from maintenance. The mean responses were 2.03, 2.33, and 2.33 respectively. This
shows the food division has the lowest scores on communication, and has the most room for
improvement.
There are several different kinds of relationships between variables. Before drawing a
conclusion, you should first understand how one variable changes with the other. This means you
need to establish how the variables are related - is the relationship linear or quadratic or inverse
or logarithmic or something else? Relationships between variables need to be studied and
analyzed before drawing conclusions based on it. In natural science and engineering, this is
usually more straightforward as you can keep all parameters except one constant and study how
this one parameter affects the result under study.
One variable that must be considered and compared is the response rate among the
divisions. The overall response rate of 17% translates to each division where no division carried
a response rate above 30%. This also supports the response of needing a larger sample size. A
larger sample will help draw more accurate and complete hypotheses and conclusions.

5. ANALYZING STATISTICS 5
References
Siddharth, K. (2011). Relationship between variables. Retrieved 13 Sep. 2012 from Experiment
Resources: http://www.experiment-resources.com/relationship-between-variables.html
Lind, D. A., Marchal, W. G., & Wathen, S. A. (2011). Basic statistics for business and
economics (7th ed.). New York, NY: McGraw-Hill/Irwin.
McClave, J. T., Benson, P. G., & Sincich, T. (2011). Statistics for business and economics (11th
ed.). Boston, MA: Pearson-Prentice Hall.