Michael Freiberg
Statistics Mid-Term #2
1.)
A.) X2 Male=0; Female =1 X3 No PhD=0; Having a PhD=1
Benchmark Equation= Y=Bo+B1*(X1i)+Ei
B.) B1= With each additional year of experience on Average an instructors annual salary would increase or decrease by B1, HOLDING ALL ELSE CONSTANT B2=When instructors are females, on Average their annual salary would increase or decrease by B2 HOLDING ALL ELSE CONSTANT B3= When instructors have a PhD On average their annual salary would increase or decrease by B3, HOLDING ALL ELSE CONSTANT C.)

X4= X1*X3
Yi=Bo+B1*(X1i)+ B2*(X2i)+B3*(X3i)+B4*(X4i)+Ei
With a PhD(X3=1); Y=Bo+B1*(X1i)+B2*(X2i)+B3*(X3i)+B4(X3i*X1i)+Ei With NO PhD(X3=0); Y=Bo+B1*(X1i)+B2*(X2i)+ Ei
2.)
A) Rsquared= SSR/SST= 1.9683/1.9798= .99421
B) Adjusted Rsquared= 1-[(1-Rsquared)*((n-1)/(n-k-1))]
Adjusted Rsquared= 1-[(1-.9942)*((100-1)/(100-2-1))]= .99408 C) Residual Df= n-k-1=100-2-1 Residual Df=97
D) Regression F= MSR/MSE= .984152/.000118= 8340.27
E) t-Stat of X2 = Coefficient X2/Standard Error of X2= -.0396/.0028797= -13.75

3.)
A) B1= On average a dogs ranking according to urban inhabitants will go up by 1.98 units for every additional inch in height they are, HOLDING DOG WEIGHT CONSTANT B2= On average a dogs ranking according to urban inhabitants will go down by .0396 units for each additional pound they weigh, HOLDING DOG HEIGHT CONSTANT B) It is silly to interpret due to the fact that it is impossible for a dog to have a height of 0 inches and a weight of 0 pounds. In addition I would assume the values of 0,0 to be excluded from the relevant range from this made up set of data therefore making the equation inaccurate with these x, y value. 4.) Ho=B1=B2=0

Unrestricted= Yi=Bo+B1*(X1i)+B2*(X2i)+B3*(X3i)+ Ei
Restricted = Yi= Bo+ B3*(X3i)+Ei
A) Number of restrictions=M=2
B) Denominator degrees of freedom=n-k-1=200-3-1=196
C) RSquared Unrestricted= .9977
D) Rquared Restricted=.9848
E)...

...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 lists the average time (in...

...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" significant
•...

...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...

...Problems Chapter 7
1. A population of 1,000 students spends an average of $10.50 a day on dinner. The standard deviation of the expenditure is $3. A simple random sample of 64 students is taken.
a. What are the expected value, standard deviation, and shape of the sampling distribution of the sample mean?
b. What is the probability that these 64 students will spend a combined total of more than $715.21?
c. What is the probability that these 64 students will spend a combined total between $703.59 and $728.45?
ANS:
a. 10.5 0.363 normal
b. 0.0314
c. 0.0794
2. The life expectancy in the United States is 75 with a standard deviation of 7 years. A random sample of 49 individuals is selected.
a. What is the probability that the sample mean will be larger than 77 years?
b. What is the probability that the sample mean will be less than 72.7 years?
c. What is the probability that the sample mean will be between 73.5 and 76 years?
d. What is the probability that the sample mean will be between 72 and 74 years?
e. What is the probability that the sample mean will be larger than 73.46 years?
ANS:
a. 0.0228
b. 0.0107
c. 0.7745
d. 0.1573
e. 0.9389
3. A simple random sample of 8 employees of a corporation provided the following information.
Employee 1 2 3 4 5 6 7 8
Age 25 32 26 40 50 54 22 23
Gender M M M M F M M F
a. Determine the point estimate for the average age of all employees.
b. What is...

...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 use statistical 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; employment length was...

...Performance of Safety Incidents
StatisticalAnalysis of Safety Incident Rates
Table of Contents
Introduction 3
Part I. Graphical Descriptive Statistics 3
Part II. Binomial Probability Distribution 4
Part III. Inferential Statistics 5
Part IV. One Sample Hypothesis T-test 5
Part V. Two Sample Hypothesis T-test 6
Part VI. Paired (matched) Observation – Two Populations Hypothesis 6
Part VII. Linear Regression and Correlation Study 7
Part VIII. ANOVA – One-Way Test of Variance 7
Part XI. Chi-square Goodness-of-Fit Test 7
Part X. Chi-square Independence Test 7
Conclusion 7
Appendix A: 9
Introduction
The measurement of safety performance is a hot topic in most companies today. Safety performance measurements seek to answer such questions as how do we compare to others? Are we getting better or worse over time? Is our management of safety effective (doing the right things)? Safety differs from many areas measured by managers because success results in the absence of an outcome (injuries or ill health) rather than a presence. As the scope of safety is vast, many stakeholders who are not safety professionals do not see safety as in such a broad way. They see “no injury” as good and “injury” as bad and that where it ends. This is one reason why incident rates and worker’s compensations costs have moved their way to the forefront of safety metrics.
As an employee of a Facilities Engineering Command (a federal...

...
StatisticalAnalysis and Application of Charts
Presented To: Mam Ayesha IftikharPresented By: Hassan Bashir
Roll Number: bba02141016
Program : BBA
Semester : 2nd
Date: 19-Oct-2014
Research Questionnaire/ Objective:
Analysis of quantitative and qualitative data
Uses of appropriate charts under the specific/general scenario.
To ensure that statistical tools are the important for decision making.
Type of Data:
Quantitative Data
Qualitative data
Quantitative Data:
Quantitative data is data expressing a certain quantity, amount or range. Usually, there are measurement units associated with the data, e.g. meters, in the case of the height of a person.
Qualitative data:
Qualitative data is information about qualities; information that can't actually be measured. Some examples of qualitative data are the softness of your skin, the grace with which you run, and the color of your eyes. However, try telling Photoshop you can't measure color with numbers.
Charts:
Pie Chart
Line Chart
Histogram
Flow Chart
Time Line Chart
5518206524001. Pie Chart:
A pie chart is divided into sectors, illustrating numerical proportion. In a pie chart, the arc length of each sector (and consequently its central angle and area), is proportional to the quantity it represents. While it is named for its resemblance to a pie which has been sliced, there are variations on the way it can be presented.
No .of securities Volume...

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