Coursework
This handout provides information about the module’s second coursework. Below, you will find the coursework as well as information about the marking scheme. * The coursework requires you to engage with regression analysis by performing various regressions in Eviews and by commenting on the main results. * The aim of the coursework is to test your ability to handle datasets with the use of a specialist software and to provide critical and informative comments on the outcome of the analysis. You are expected to use Eviews for your analysis. The use of any other alternative software should be negotiated with the lecturer; * This coursework is an individual piece of work and accounts for 20 marks * The deadline is: Friday 16th December

* Submission: your report should be typed with all tables, graphs and text included in one single doc or pdf file. You can submit the file via Blackboard or you can submit a hardcopy to the reception office. Notice that if you submit online the deadline will be 11.59pm on Friday 16th December while if you submit a hardcopy the deadline is 4pm on the same day. * Marking scheme

* A first class mark is awarded to the work that:
* Contains well drawn and clear graphs, tables and regression output; * The analysis is clear, informative, detailed and makes references to economic theory and to technical aspects of regression analysis; * All questions are addressed in a comprehensive way with the use of appropriate graphs and tables. While answering each individual question, the good student will also appreciate that each question is part of the overall investigation of the determinants of labour supply; * The coursework is well presented, written in good English with an accurate use of referencing to external resources * A grade B is awarded to the work that broadly meets the requirements above but that shows some inaccuracies in the analysis and in the...

...Rape Statistics
Working at a Historic Black College/University (HBCU) for the past three (3) years, I just recently came in contact with a rape victim. I’ve seen the shows but never thought, I would be a person to come in contact with one. Talking to the different individuals involved with the student case, I decided to do my research paper on rap victim.
Specifically, I am looking at how the likelihood of being the victim of a violent crime in the United States is related to gender and race of the victim. The raw data is readily available from the US department of Justice min cooperation with the US Department of Health and Human Services. The Bureau of Justice Statistics and the Centers for Disease Control and Prevention issued their preliminary study in June of 2001. Thomas Simon, Ph.D. and James Mercy, PhD preformed the research, both are scientists at the CDC in association with Craig Perkins, a BJS statistician.
In order to make such a study possible, the researchers have to look at a large collection of data, sometimes incomplete that will vary from doctor to doctor. One of the first problems that arise is the underreporting of rape in the United States. It is widely believed in the United States that only one (1) out of three (3) rapes are reported to authorities. The second problem is that different doctors will characterize the injuries differently among no injury, severe injury, and minor injury. For example, a...

...caused by the other Validation of root causes is made only when: o There is a statistically relevant relationship between the root cause and its effect o Knowledge of the process confirms this causal relationship The more common statistical tools are the regression analysis and the chi-square test. But they are not the only tools the team can use…. attivaRes
Regression analysis (scatter plot)
It is a mathematical diagram using Cartesian coordinates. The scatter plot would give a visual comparison of the two variables in the data set, and would help to determine what kind of relationship there might be between the two variables. o It is, as Ishikawa diagram, one of the seven basic tools for quality (Ishikawa, Deming) o It is a simple statistic tool
o
Dependent variable on Vertical axis
Independent variable or control parameter On horizontal axis
attivaRes
Chi-square test
Chi-square is a statistical test commonly used to compare observed data with data we would expect to obtain according to a specific hypothesis. o The chi-square test is always testing null hypothesis, which states that there is no significant difference between the expected and observed result. o That is, chi-square is the sum of the squared difference between observed (o) and the expected (e) data (or the deviation, d), divided by the expected data in all possible categories.
o
Let’s try it!
attivaRes
5 W’s + 1 H approach
how
Workshop technique
Play...

...Statistics 1
Business Statistics
LaSaundra H. – Lancaster
BUS 308 Statistics for Managers
Instructor Nicole Rodieck
3/2/2014
Statistics 2
When we hear about business statistics, when think about the decisions that a manager makes to help make his/her business successful. But do we really know what it takes to run a business on a statistical level? While some may think that businessstatistics is too much work because it entails a detailed decision making process that includes calculations, I feel that without educating yourself on the processes first you wouldn’t know how to imply statistics. This is a tool managers will need in order to run a successful business. In this paper I will review types of statistical elements like: Descriptive, Inferential, hypothesis development and testing and the evaluation of the results. Also I will discuss what I have learned from business statistics.
My description of Descriptive statistics is that they are the numerical elements that make up a data that can refer to an amount of a categorized description of an item such as the percentage that asks the question, “How many or how much does it take to “ and the outcome numerical amount. According to “Dr. Ashram’s Statistics site” “The quantities most commonly used to measure the dispersion of the values about...

...criterion we get the best model: y = -124.382 + 0.296X1 + 0.048X2 + 1.306X3 + 0.5198X4. This model contains all four predictor variables X1, X2, X3 and X4. This model is selected as best model by the MaxR criterion because it has the largest R-Square 0.9629, which is larger than 0.9615(model containing 3 variables), 0.9330(model containing 2 variables) and 0.8047(model containing 1 variable).
Below is a SAS output of the MaxR criterion.
Obviously, the “best” model obtained from MaxR criterion differs from that obtained from Stepwise and Backward Elimination Method. It is not hard to understand this phenomenon: Since for the Stepwise/Backward Elimination method, F-statistic plays an important role in selecting a variable: the F-statistic for a variable to be added must be significant at the SLENTRY level, the F-statistic for a variable to be removed must be significant at the SLSTAY level. While the MaxR method selects variables depending on which variable or variable combination can produce the largest R square. MaxR makes the switch that produces the largest increase in R square.
Appendix |
Code:
data job;
infile "C:\Users\sandra\Desktop\CH09PR10.txt";
input y x1 x2 x3 x4;
run;
proc reg data=job;
model y=x1 x2 x3 x4/selection=stepwise slstay=.10 slentry=.05;
title "Stepwise Selection";
run;
proc reg data=job;
model y=x1 x2 x3 x4/selection=adjrsq;
run;
proc reg data=job;
model y=x1 x2 x3...

...CLICK TO DOWNLOAD
BUS 308 STATISTICS FOR AMANAGERS
BUS 308 Week 1 DQ 1 Language
Numbers and measurements are the language of business.. Organizations look at results, expenses, quality levels, efficiencies, time, costs, etc. What measures does your department keep track of ? How are the measures collected, and how are they summarized/described? How are they used in making decisions? (Note: If you do not have a job where measures are available to you, ask someone you know for some examples or conduct outside research on an interest of yours.)
BUS 308 Week 1 DQ 2 Levels
Managers and professionals often pay more attention to the levels of their measures (means, sums, etc.) than to the variation in the data (the dispersion or the probability patterns/distributions that describe the data). For the measures you identified in Discussion 1, why must dispersion be considered to truly understand what the data is telling us about what we measure/track? How can we make decisions about outcomes and results if we do not understand the consistency (variation) of the data? Does looking at the variation in the data give us a different understanding of results?
BUS 308 Week 1 Problem Set Week One
Problem Set Week One. All statistical calculations will use the Employee Salary Data set (in Appendix section).
Using the Excel Analysis ToolPak function Descriptive Statistics, generate descriptive statistics for the salary data. Which variables...

...
Simply use statistics as a tool. You will be given a data. (Next year you will not be given data, you will gather data yoruself).
1. Data: one of the variables is dependent and other dependent. Can be multiple. Then do regression analysis. ANOVA for overall significance and Regression equation. And write based on ANOVA there is a significance or not.
2. Some comments on correlation: volume vs. horse power etc.
3. Hypothesis test of one population. I assume that the mean is etc etc. Small paragraph analysis below the results of the test. ANOVA for small, large and medium size businesses for example.
Simply use statistics as a tool. You will be given a data. (Next year you will not be given data, you will gather data yoruself).
1. Data: one of the variables is dependent and other dependent. Can be multiple. Then do regression analysis. ANOVA for overall significance and Regression equation. And write based on ANOVA there is a significance or not.
2. Some comments on correlation: volume vs. horse power etc.
3. Hypothesis test of one population. I assume that the mean is etc etc. Small paragraph analysis below the results of the test. ANOVA for small, large and medium size businesses for example.
Simply use statistics as a tool. You will be given a data. (Next year you will not be given data, you will gather data yoruself).
1. Data: one of the variables is dependent and other dependent. Can be...

...H5 was ruled out for the alpha being greater than the P-value for the age at which the first claim occurred.
The P-value for H1 and H3 both have an alpha level which is less than the P-value. However, we have compared how age of the patient affects H1 & H3. We have created two subsets using our independent variables. The patients in H6 are ages 0-39. H7 contains the same independent variables, however, the patients are over the age of 40. We have found that for patients aged 0-39 have a greater increase in days in the hospital as drug count and visit count increase. In comparison, patients age 40 and over spend a less number of days in the hospital.
The remainder of our analysis will focus on H6 & H7.
Methods: Descriptive Statistics
1. Demographics of Data Collection and Operationalization of the Variables
The study population consists of:
PEOPLE AGES (at the first time of service):
* 0-39 years old
DRUG COUNT: Number of Drugs Administered at First Service
VISIT COUNT: Number of hospital visits in Year 1
(See Appendix A.)
Data Syntheses: There is a definite relationship between the independent variables and the hypotheses tested. We tested a sample size (n= 8356) of patients who volunteered their information. We concluded that the strongest correlation exists between the visit count and the number of days spent in the hospital.
For H7 the P-value for visit count is .000. The P-value for drug count is .000.
Results: (n=8356)...

...Syllabus for Statistics
Course No. 21090024
Period：54
Credit：3
Course Nature：Compulsive
Assessment: Usually 10%, Group Work 20%, Final Exam70%
Textbook：
Statistics(3rd Edition)，
Junping Jia，Xiaoqun He，Yongjin Jin，China Renmin University Press，2007
Reference：
Statistics for Business and Economics(7th Edition)
Anderson, D.R., & Sweeney, D.J. & Williams, T.A.
1.Introduction
Statistics is a core curriculum for students in finance and economics major, which is a science method that starts with data to study the status and development of the society economic phenomenon. This course mainly tells us the skill how to collect and collate information and the methods how to do with quantitative analysis and comprehensive evaluation. Specific content include: statistical design, statistical research, aggregate indexes, relative indexes, average indexes, sign variability indexes, time series prediction, statistics indexes, sample inferred, correlation analysis, aggregate indexes for the national economy etc.
2. Proportion of Course Hours
Chapter
Proportion
Chapter1 General Introduction and Statistical Data
2
Chapter2 Introduction of Descriptive Statistical Data: Tabular and Graphical Methods
2
Chapter3 Statistical Data summarize and Related cases
2
Chapter4 Introduction of Statistical Sampling and Sampling Distribution
6
Chapter5 Parameter Estimation
4
Mid-term Questions and Discussion
2
Chapter6 Hypothesis...