Statistical Analysis
Use Statistical Analysis to Measure Your Survey Results
Using statistical analysis is an easy way to understand your survey results, and make sure you’re on the right track. Mean, median, and mode will give you easy indicators for your rating scale questions allowing you to see where people fall on your scale in a few different ways. If you ask customers to rate their satisfaction with your product on a scale of 1-5, with one being very dissatisfied and five being very satisfied, it’s easy to see from a mean of 4 that you have happy customers. If you wanted to see how satisfied the majority of people were, you could check the mode to see that of 200 responses, 150 people said they were satisfied or a “4” on the rating scale.

Standard deviation and range both help measure spread of your survey responses. Use standard deviation to determine spread around the mean. For example, if the mean to our satisfaction question above was a 4 and the standard deviation was 0, you would know that all respondents had a satisfaction of 4. If the standard deviation was 1.5, you would know that the answers were more variable.

Use the confidence interval and the confidence level to determine the probability that the result in the population actually lies within the given interval. For example, if your confidence interval is (3.64-4) and your confidence level is 95%, you can say that you are 95% certain that results in the population will fall between 3.64 and 4.

...Elementary Concepts in Statistics. In this introduction, we will
briefly discuss those elementary statistical concepts that provide the necessary
foundations for more specialized expertise in any area of statistical data analysis. The
selected topics illustrate the basic assumptions of most statistical methods and/or have
been demonstrated in research to be necessary components of one's general
understanding of the "quantitative nature" of reality (Nisbett, et al., 1987). Because of
space limitations, we will focus mostly on the functional aspects of the concepts
discussed and the presentation will be very short. Further information on each of those
concepts can be found in the Introductory Overview and Examples sections of this
manual and in statistical textbooks. Recommended introductory textbooks are:
Kachigan (1986), and Runyon and Haber (1976); for a more advanced discussion of
elementary theory and assumptions of statistics, see the classic books by Hays (1988),
and Kendall and Stuart (1979).
• What are variables?
• Correlational vs.
experimental research
• Dependent vs. independent
variables
• Measurement scales
• Relations between variables
• Why relations between
variables are important
• Two basic features of every
relation between variables
• What is "statistical
significance" (p-value)
• How to determine that a
result is "really"...

...(211)---STATISTICAL TECHNIQUES FOR RISK ANALYSISStatistical Techniques for Risk AnalysisStatistical techniques are analytical tools for handling risky investments. These techniques, drawing from the fields of mathematics, logic, economics and psychology, enable the decision-maker to make decisions under risk or uncertainty.
The concept of probability is fundamental to the use of the riskanalysis techniques. Hoe is probability defined? How are probabilities estimated? How are they used in the risk analysis techniques? How do statistical techniques help in resolving the complex problem of analyzing risk in capital budgeting? We attempt to answer these questions in our posts.
Probability defined
The most crucial information for the capital budgeting decision is a forecast of future cash flows. A typical forecast is single figure for a period. This referred to as “best estimate” or “most likely” forecast. But the questions are: To what extent can one rely this single figure? How is this figure arrived at? Does it reflect risk? In fact, the decision analysis is limited in two ways by this single figure forecast. Firstly, we do not know the changes of this figure actually occurring, i.e. the uncertainty surrounding this figure. In other words, we do not know the range of the forecast and the chance or the probability...

...MBA Business Statistics
Homework 1
Reminders:
1. Due date: Jan-14-2012 (Saturday) in class.
2. Please submit only the hardcopy.
3. Please show the names and ID numbers of all your group members on the cover page. Please also
indicate your session (DSME5110W).
1.
Problem 2.1 (p. 33)
The file P02_01.xlsx indicates the gender and nationality of the MBA incoming class in two
successive years at the Kelley School of Business at Indiana University.
a. For each year, create tables of counts of gender and of nationality. Then create column charts of
these counts. Do they indicate any noticeable change in the composition of the two classes?
b. Repeat part a for nationality, but recode this variable so that all nationalities that have counts of 1
or 2 are classified as Other.
2.
Problem 2.5 (p. 33)
The file DJIA Monthly Close.xlsx contains monthly values of the Dow Jones Industrial Average
from 1950 through 2009. It also contains the percentage changes from month to month. (This file will
be used for an example later in this chapter.) Create a new column for recoding the percentage
changes into six categories: Large negative (< -3%), Medium negative (< -1%, ≥ -3%), Small
negative (< 0%, ≥ -1%), Small positive (< 1%, ≥ 0%), Medium positive (< 3%, ≥ 1%), and Large
positive (≥ 3%). Then create a column chart of the counts of this categorical variable. Comment on its
shape.
3.
Problem 2.6 (p. 55)
The file P02_06.xlsx...

...and E. Yücesan, eds.
STATISTICALANALYSIS OF SIMULATION OUTPUT DATA:
THE PRACTICAL STATE OF THE ART
Averill M. Law
Averill M. Law & Associates
4729 East Sunrise Drive, #462
Tucson, AZ 85718, USA
ABSTRACT
One of the most important but neglected aspects of a simulation study is the proper design and analysis of
simulation experiments. In this tutorial we give a state-of-the-art presentation of what the practitioner really needs to know to be successful. We will discuss how to choose the simulation run length, the warmup-period duration (if any), and the required number of model replications (each using different random
numbers). The talk concludes with a discussion of three critical pitfalls in simulation output-data analysis.
1 INTRODUCTION
of fact, a very common mode of operation is to make a single simulation run of somewhat arbitrary length
samples from probability distributions are typically used to drive a simulation model through time, these
estimates are just particular realizations of random variables that...

...Assignment #3
Constructing a Methodology for StatisticalAnalysis
Christian Diener
998029324
Anthony Chum
Research Problem
As pollution continues to rise in our cities due to various human activities, the incidence of cancer also seems to be increasing. The study of cancer is important because it is a major health problem in today’s society. Not only is it a significant contributor to deaths all around the world but it also affects our economy as a whole. It affects the economy because people who develop cancer are forced to take off work in order to go through the many treatments of radiation and chemotherapy, which in turn has a severe impact on the economy due to the millions of dollars lost in productivity. It is also a financial burden on people due to them losing their job which makes it harder for them to pay the bills and provide themselves with basic needs. So, overall cancer affects a lot of aspects in people’s lives and not just their own lives. Therefore it is an important issue to study and my research question therefore is: Is there significant difference in health related problems, specifically cancer, amongst residents in different regions of the Greater Toronto Area?
Specific Research Questions
The following are more specific questions based on my general research topic:
Is there a significant difference in the prevalence of cancer between men and women?
* Null Hypothesis: There is no significant difference in...

...StatisticalAnalysis of Experimental Density Data
Abstract
Introduction:
The purpose of the lab was to learn how to calculate density of a material by taking it mass and volume. By performing the experiment, important statistical concepts will be learned, for example standard deviation, mean and random error in order to understand the errors that are committed at taking measurements.
Material and methods:
To measure volume and mass were used a burette and balance. The methods used in this experiment were very basic, the instructions show how to read a burette and a balance; and tells how many significant figures have to use for each device. In order to take mass there was necessary to use a beaker to place the glass beads (the object that was going to be measured), and in order to don’t affect the mass or volume of the glass beads there was used forceps. Another methods used in this lab were how to calculate average, standard deviation, confidence intervals and error in density.
Results:
At taking the average for mass and volume of the beads, the results were 3.3048 g (mass) and 1.325 mL (volume). The density was calculated from those results, which was 2.49 g/mL. Standard deviation of mass was 0.01176 g and for volume 0.013 mL. The Confidence Intervals for the mean were obtained. The 95% CI for mean were ±0.01871 g (mass), ±0.021 mL...

...Gender Discrimination: A StatisticalAnalysis
Gender discrimination, or sex discrimination, may be characterized as the unequal treatment of a person based solely on that person's sex. .
It is apparent that gender discrimination is pervasive in the modern workplace, however, its presence and effects are often misrepresented and misunderstood. Statistical testing plays an important role in cases where the existence of discrimination is a disputed issue and has been used extensively to compare expected numbers of members of a protected group, to the actual number of members of that protected group that have been involved in a significant employment action. This paper will usestatistical testing and analysis, including a multiple regression model, to estimate the effects that various independent variables have upon the dependent variable, salary level.
This analysis utilized a data sample consisting of 46 employees and variables relating to each of those employees. These variables include: gender, age, level of education, length of employment, job type, and weekly salary. Each of these variables is further broken as follows: gender was divided between males and females; age was listed as the age of the employee; education was broken down to reflect the last level of education obtained by the employee, some high school, high school, college, and graduate school;...

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