The researchers conducted an analysis of H1-B visa approval data from 2007 to understand trends in applicants' industries, regions, and wages. They found:
1) The majority (33%) of applicants applied in the Northeast region, while the fewest (16%) applied in the Midwest.
2) The science and math industry had by far the most applicants (68.7%), likely due to foreign interest in fields where the US faces education gaps.
3) Health and business/finance industries saw the highest approval rates, with health applicants 1.4 times and business 1.87 times more likely to be approved than other fields.
4) Northeast regions saw the most applicants and had the highest
Medical devices market research Russia 2012-2014Roman Korablev
This document provides a summary of the medical devices market in Russia, including forecasts for 2015-2016. It discusses the challenging economic conditions in Russia in 2014 due to sanctions and low oil prices. This caused significant devaluation of the ruble. The document outlines several government programs that provide funding for medical device purchases and modernization of healthcare facilities. It also discusses the methodology used to analyze customs import data and segmentation of the market. Key factors like currency fluctuations and government regulations are noted as influencing the market.
The document appears to be a collection of random thoughts, jokes, and references to various topics including science, religion, nationality, and personal beliefs. It switches between serious and joking tones and includes a works cited list of Flickr images at the end.
[한국포스트휴먼학회 창립기념 공개강연회 l KAIST 휴머노이드로봇센터 오준호 교수] Robot Technology and the futureMINWHO Law Group
This document discusses robot technology and its future applications. It describes several types of intelligent robots including home service robots, medical robots, military robots, and rehabilitation robots. It also highlights several examples of robots such as ASIMO, BigDog, HAL, and surgical robots. The document then discusses the DARPA Robotics Challenge and its goals to generate research to allow robots to assist in disaster response situations. Finally, it emphasizes that as technology advances, robots will need to have increased autonomy, mobility, intelligence and ability to interact with humans.
This document provides specifications for a series of LED floodlights with varying wattages. It includes details on optical and electrical specifications, dimensions, installation methods, and compliance certifications. Key specifications outlined are luminous flux output, color temperature options, ingress protection rating, operating temperature range, and a lifespan of over 50,000 hours. Optional configurations include different mounting methods and control options like DALI and motion sensors.
The owl is a symbol of wisdom in ancient Athens and was associated with the goddess Athena. It appeared on Athenian coins as early as 520 BC, which were called "glaukes" meaning owl. This antique coin design featuring an owl and olive branch is still used for the contemporary 1-Euro Greek coin, depicting an old Athenian coin with its irregular outline, owl symbol, and olive branch.
The document summarizes and compares the terrorist attacks at the 1972 Munich Olympics and the 2013 Boston Marathon bombings. It analyzes both events through Down's five stage issue-attention cycle and Birkland's focusing event theory. The cycles show how each attack initially grabbed public attention but interest declined over time without full resolution. Both attacks were classified as "Type Two Focusing Events" that disrupted social norms and led to policy changes around security and counterterrorism. The document outlines the key details and aftermath of each attack based on the analytical frameworks.
Medical devices market research Russia 2012-2014Roman Korablev
This document provides a summary of the medical devices market in Russia, including forecasts for 2015-2016. It discusses the challenging economic conditions in Russia in 2014 due to sanctions and low oil prices. This caused significant devaluation of the ruble. The document outlines several government programs that provide funding for medical device purchases and modernization of healthcare facilities. It also discusses the methodology used to analyze customs import data and segmentation of the market. Key factors like currency fluctuations and government regulations are noted as influencing the market.
The document appears to be a collection of random thoughts, jokes, and references to various topics including science, religion, nationality, and personal beliefs. It switches between serious and joking tones and includes a works cited list of Flickr images at the end.
[한국포스트휴먼학회 창립기념 공개강연회 l KAIST 휴머노이드로봇센터 오준호 교수] Robot Technology and the futureMINWHO Law Group
This document discusses robot technology and its future applications. It describes several types of intelligent robots including home service robots, medical robots, military robots, and rehabilitation robots. It also highlights several examples of robots such as ASIMO, BigDog, HAL, and surgical robots. The document then discusses the DARPA Robotics Challenge and its goals to generate research to allow robots to assist in disaster response situations. Finally, it emphasizes that as technology advances, robots will need to have increased autonomy, mobility, intelligence and ability to interact with humans.
This document provides specifications for a series of LED floodlights with varying wattages. It includes details on optical and electrical specifications, dimensions, installation methods, and compliance certifications. Key specifications outlined are luminous flux output, color temperature options, ingress protection rating, operating temperature range, and a lifespan of over 50,000 hours. Optional configurations include different mounting methods and control options like DALI and motion sensors.
The owl is a symbol of wisdom in ancient Athens and was associated with the goddess Athena. It appeared on Athenian coins as early as 520 BC, which were called "glaukes" meaning owl. This antique coin design featuring an owl and olive branch is still used for the contemporary 1-Euro Greek coin, depicting an old Athenian coin with its irregular outline, owl symbol, and olive branch.
The document summarizes and compares the terrorist attacks at the 1972 Munich Olympics and the 2013 Boston Marathon bombings. It analyzes both events through Down's five stage issue-attention cycle and Birkland's focusing event theory. The cycles show how each attack initially grabbed public attention but interest declined over time without full resolution. Both attacks were classified as "Type Two Focusing Events" that disrupted social norms and led to policy changes around security and counterterrorism. The document outlines the key details and aftermath of each attack based on the analytical frameworks.
Measuring Inconsistency in meta-analysesJuan Rubio
This document discusses a new method for quantifying heterogeneity, or inconsistency, between studies in a meta-analysis called I2. I2 describes the percentage of variation between studies that is due to heterogeneity rather than chance. The document provides examples of I2 values from published meta-analyses to demonstrate its use. I2 can help researchers investigate the causes of heterogeneity by comparing values between subgroups of studies. Quantifying inconsistency with I2 provides a better assessment than significance tests, which are often underpowered and do not measure the size of heterogeneity effects.
Father Paul Lostritto is the parish priest of New York City’s St. Francis of Assisi Church. Formerly the director of the Franciscan Food Center at the St. Anthony Shrine and Ministry Center in Boston, Father Paul Lostritto currently serves as codirector for the St. Francis Breadline, now known Franciscan Bread for the Poor. This nonprofit organization provides the underprivileged population of New York City with the following necessities.
The document discusses food types, food tests, digestion, enzymes, and the small intestine. It provides information on the main types of foods like carbohydrates, fats, and proteins. It describes tests that can identify starch, sugars, proteins, and fats in foods. It explains that digestion breaks down foods so the body can absorb nutrients, with enzymes breaking down carbohydrates, proteins, and fats. Finally, it states the small intestine absorbs glucose from digestion and passes other nutrients to the large intestine.
This document provides an overview of using I/O ports, timers, analog-to-digital converters, input capture, output compare, serial peripheral interface, and inter-integrated circuit functions on the PIC24HJ256GP210 microcontroller. It includes descriptions of the relevant control registers and examples of code that demonstrate how to configure and use the different peripherals. The examples provided are intended to help understand how to interface with common external devices using various communication protocols.
CompTIA exam study guide presentations by instructor Brian Ferrill, PACE-IT (Progressive, Accelerated Certifications for Employment in Information Technology)
"Funded by the Department of Labor, Employment and Training Administration, Grant #TC-23745-12-60-A-53"
Learn more about the PACE-IT Online program: www.edcc.edu/pace-it
This document contains information about heat transfer and changes of state. It discusses how heat flows from warm to cold areas through conduction, convection and radiation. Conduction involves heat transfer through a solid. Convection occurs when a heated fluid rises and transfers heat. Radiation can transfer heat through a vacuum by electromagnetic waves. The document also explains phase changes like melting, freezing, evaporation and condensation in terms of particle theory.
Cognos and Tableau Visualization for Business Report ImplementationNan Wang
This is a course project for conducting descriptive、predictive and prescriptive analysis with data visualization skills in IBM Cognos and Tableau.
The analysis is about salary and H1B application condition of data-position foreign employees in USA.
This document outlines 3 assignments for a Certified Recruitment Analyst project, each analyzing the job requirements for a different position - Payroll Assistant, Clinical Support Nurse, and Program Manager. For each assignment, the document describes the job description, fundamental and motivational analysis, job element analysis, required knowledge and skills, behavioral traits, and sample interview questions. The purpose of the project is to develop comprehensive job specifications that accurately reflect the qualifications and skills needed for each role.
The document discusses various aspects of the US staffing industry, including:
1) It describes the key players in the US staffing industry such as clients, account managers, candidates, VMS, and recruiters.
2) It notes that the US staffing industry has created more jobs than any other industry after the 2009 recession due to its resilience during economic fluctuations and rising demand for contractual workers.
3) It provides an overview of the recruitment process in US staffing, from understanding requirements to submitting candidate resumes.
2HIIT 102 Health Care Delivery SystemsMulti-Phase Research P.docxrhetttrevannion
2
HIIT 102 Health Care Delivery Systems
Multi-Phase Research Project Overview
Total Points: 160
This is a three-phase research project that will be submitted at different points throughout the semester. The purpose of the project is to demonstrate an understanding of the U.S. health care delivery system and its various components, including financing, delivery, and reimbursement. These components must be viewed from the political, social, and economic atmosphere in the U.S. and from factors such as cost, access, and quality. To complete this project, students will:
1. demonstrate the ability to locate quality HIM sources;
2. look closely at who provides health care, how it is delivered, and who has access;
3. examine how health care is financed;
4. analyze reimbursement and payment systems;
5. identify health care legislation that impacts health care settings; and
6. explore health care delivery in rural areas of the U.S.
As part of the project, you will choose another country from a provided list and compare/contrast that country with the U.S. on a given set of key research topics. You will also choose a state from a provided list and examine health care delivery in rural areas of the U.S.
Phase 1 of the project is a completed research documentation chart, Phase 2 is a U.S./other country comparison-contrast chart, and Phase 3 is a final written paper. Many guides will be provided to assist you with each phase.
Phase 1 Research – 50 pts.
Due with Module 5 assignments – refer to course calendar for date.
Begin this phase by reading the Library Instruction Guidelines in the Module 1 folder. Then, using the Key Research Topics document and the Research Data Collection chart, collect information that you will use in Phase 2 U.S./other country comparison-contrast chart and Phase 3 Written Paper. The Key Research Topics document guides you to major subjects you will need to cover in this project and provides driving questions to help focus your research. You will need to gather information for each topic for both the U.S. and the country that you choose to explore. You may choose either Canada, France, or Great Britain.
You will also need to gather data on rural access to health care for a state that you choose. You may choose either North Carolina, Arizona or West Virginia. For this section, you will want to look at federal and state data. Health and Human Services (hhs.gov) is an excellent source as well as the official state government website. Once on the official state website, search for the state’s health plan and information on rural health care delivery. Also, look at the most recent census data to identify the 3 poorest counties in the state based on income. This can be determined by counties where the average income is at or below the federal poverty level based on the HHS guidelines. You will need information to answer the following: 1) Why are rural counties in poorer health? and 2) Why are rural areas d.
The document discusses key aspects of the US staffing industry recruitment process. It describes how a staffing agency brings together clients, vendors, and job candidates to fill vacant roles quickly. The recruitment process involves understanding job requirements, formulating search strings, screening candidates by phone, and submitting qualified resumes to clients. Common tax structures for contractors like W-2, 1099 and Corp-to-Corp are also summarized.
Inside This Issue:
DCR National Temp Wage Index
I-Squared: Spoiling or Saving the U.S. Economy?
Economic Recovery brings Optimism to Temp Employment
Unemployment Crisis: Unearthing the Facts Behind Official Claims
Public or Private: Which Type of Job to Opt for?
This document provides an overview of training content for US staffing and talent acquisition. It covers topics such as human resource management, the US staffing industry, recruitment process outsourcing, US work permits, taxation terms, the recruitment process, and interview questions. The training covers HR functions like staffing, compensation, and development. It also details the size and services of the US staffing industry, common work permits like H1B and L1, and tax classifications like W2 and 1099.
H-1B visas are granted to those who meet specific qualifications. The required qualifications include that the applicant have at least a bachelor’s degree from a U.S. institution and have a job offer from a U.S. employer that requires the H-1B Candidate to hold at least a bachelor’s degree.
This document discusses using analytics to predict the success rate of university research grant applications. It analyzes a dataset of grant applications from the University of Melbourne from 2005-2010. Several machine learning models are trained on features like researcher characteristics and grant details. The best model predicts 1338 of 2176 applications from 2009-2010 would be successful, exceeding actual prior success rates. Key factors identified as increasing success included prior successful grants, applications by researchers with PhDs, and applications submitted early in the year when more budget is available. Insights can help universities strategize grant submissions and better understand success determinants.
Pilot Study 2 on Processes for Determining the Accuracy of Credit Bureau Info...Luigi Wewege
This study examines accuracy in consumer credit reports using a nationally representative sample of consumers with credit histories. Participants in the study examined their credit reports from the three national credit reporting agencies and identified potentially material errors. Participants disputed any identified errors using the FCRA dispute process and were provided with new credit reports and credit scores. The original reports were then compared with the new reports and if any modifications were made as a result of the disputes, the impact of errors on the consumer’s credit score was determined. Overall, 20% of consumers disputed errors and had modifications made to at least one credit report. In many cases, the change to the credit report had no effect on credit score; 13% of consumers experienced a change in score due to their dispute. When focusing on changes in score that could impact a consumer’s credit risk classification, the study found that 5% of consumers had errors on their credit report that may be affecting the likelihood of receiving credit or the terms of credit received.
Measuring Inconsistency in meta-analysesJuan Rubio
This document discusses a new method for quantifying heterogeneity, or inconsistency, between studies in a meta-analysis called I2. I2 describes the percentage of variation between studies that is due to heterogeneity rather than chance. The document provides examples of I2 values from published meta-analyses to demonstrate its use. I2 can help researchers investigate the causes of heterogeneity by comparing values between subgroups of studies. Quantifying inconsistency with I2 provides a better assessment than significance tests, which are often underpowered and do not measure the size of heterogeneity effects.
Father Paul Lostritto is the parish priest of New York City’s St. Francis of Assisi Church. Formerly the director of the Franciscan Food Center at the St. Anthony Shrine and Ministry Center in Boston, Father Paul Lostritto currently serves as codirector for the St. Francis Breadline, now known Franciscan Bread for the Poor. This nonprofit organization provides the underprivileged population of New York City with the following necessities.
The document discusses food types, food tests, digestion, enzymes, and the small intestine. It provides information on the main types of foods like carbohydrates, fats, and proteins. It describes tests that can identify starch, sugars, proteins, and fats in foods. It explains that digestion breaks down foods so the body can absorb nutrients, with enzymes breaking down carbohydrates, proteins, and fats. Finally, it states the small intestine absorbs glucose from digestion and passes other nutrients to the large intestine.
This document provides an overview of using I/O ports, timers, analog-to-digital converters, input capture, output compare, serial peripheral interface, and inter-integrated circuit functions on the PIC24HJ256GP210 microcontroller. It includes descriptions of the relevant control registers and examples of code that demonstrate how to configure and use the different peripherals. The examples provided are intended to help understand how to interface with common external devices using various communication protocols.
CompTIA exam study guide presentations by instructor Brian Ferrill, PACE-IT (Progressive, Accelerated Certifications for Employment in Information Technology)
"Funded by the Department of Labor, Employment and Training Administration, Grant #TC-23745-12-60-A-53"
Learn more about the PACE-IT Online program: www.edcc.edu/pace-it
This document contains information about heat transfer and changes of state. It discusses how heat flows from warm to cold areas through conduction, convection and radiation. Conduction involves heat transfer through a solid. Convection occurs when a heated fluid rises and transfers heat. Radiation can transfer heat through a vacuum by electromagnetic waves. The document also explains phase changes like melting, freezing, evaporation and condensation in terms of particle theory.
Cognos and Tableau Visualization for Business Report ImplementationNan Wang
This is a course project for conducting descriptive、predictive and prescriptive analysis with data visualization skills in IBM Cognos and Tableau.
The analysis is about salary and H1B application condition of data-position foreign employees in USA.
This document outlines 3 assignments for a Certified Recruitment Analyst project, each analyzing the job requirements for a different position - Payroll Assistant, Clinical Support Nurse, and Program Manager. For each assignment, the document describes the job description, fundamental and motivational analysis, job element analysis, required knowledge and skills, behavioral traits, and sample interview questions. The purpose of the project is to develop comprehensive job specifications that accurately reflect the qualifications and skills needed for each role.
The document discusses various aspects of the US staffing industry, including:
1) It describes the key players in the US staffing industry such as clients, account managers, candidates, VMS, and recruiters.
2) It notes that the US staffing industry has created more jobs than any other industry after the 2009 recession due to its resilience during economic fluctuations and rising demand for contractual workers.
3) It provides an overview of the recruitment process in US staffing, from understanding requirements to submitting candidate resumes.
2HIIT 102 Health Care Delivery SystemsMulti-Phase Research P.docxrhetttrevannion
2
HIIT 102 Health Care Delivery Systems
Multi-Phase Research Project Overview
Total Points: 160
This is a three-phase research project that will be submitted at different points throughout the semester. The purpose of the project is to demonstrate an understanding of the U.S. health care delivery system and its various components, including financing, delivery, and reimbursement. These components must be viewed from the political, social, and economic atmosphere in the U.S. and from factors such as cost, access, and quality. To complete this project, students will:
1. demonstrate the ability to locate quality HIM sources;
2. look closely at who provides health care, how it is delivered, and who has access;
3. examine how health care is financed;
4. analyze reimbursement and payment systems;
5. identify health care legislation that impacts health care settings; and
6. explore health care delivery in rural areas of the U.S.
As part of the project, you will choose another country from a provided list and compare/contrast that country with the U.S. on a given set of key research topics. You will also choose a state from a provided list and examine health care delivery in rural areas of the U.S.
Phase 1 of the project is a completed research documentation chart, Phase 2 is a U.S./other country comparison-contrast chart, and Phase 3 is a final written paper. Many guides will be provided to assist you with each phase.
Phase 1 Research – 50 pts.
Due with Module 5 assignments – refer to course calendar for date.
Begin this phase by reading the Library Instruction Guidelines in the Module 1 folder. Then, using the Key Research Topics document and the Research Data Collection chart, collect information that you will use in Phase 2 U.S./other country comparison-contrast chart and Phase 3 Written Paper. The Key Research Topics document guides you to major subjects you will need to cover in this project and provides driving questions to help focus your research. You will need to gather information for each topic for both the U.S. and the country that you choose to explore. You may choose either Canada, France, or Great Britain.
You will also need to gather data on rural access to health care for a state that you choose. You may choose either North Carolina, Arizona or West Virginia. For this section, you will want to look at federal and state data. Health and Human Services (hhs.gov) is an excellent source as well as the official state government website. Once on the official state website, search for the state’s health plan and information on rural health care delivery. Also, look at the most recent census data to identify the 3 poorest counties in the state based on income. This can be determined by counties where the average income is at or below the federal poverty level based on the HHS guidelines. You will need information to answer the following: 1) Why are rural counties in poorer health? and 2) Why are rural areas d.
The document discusses key aspects of the US staffing industry recruitment process. It describes how a staffing agency brings together clients, vendors, and job candidates to fill vacant roles quickly. The recruitment process involves understanding job requirements, formulating search strings, screening candidates by phone, and submitting qualified resumes to clients. Common tax structures for contractors like W-2, 1099 and Corp-to-Corp are also summarized.
Inside This Issue:
DCR National Temp Wage Index
I-Squared: Spoiling or Saving the U.S. Economy?
Economic Recovery brings Optimism to Temp Employment
Unemployment Crisis: Unearthing the Facts Behind Official Claims
Public or Private: Which Type of Job to Opt for?
This document provides an overview of training content for US staffing and talent acquisition. It covers topics such as human resource management, the US staffing industry, recruitment process outsourcing, US work permits, taxation terms, the recruitment process, and interview questions. The training covers HR functions like staffing, compensation, and development. It also details the size and services of the US staffing industry, common work permits like H1B and L1, and tax classifications like W2 and 1099.
H-1B visas are granted to those who meet specific qualifications. The required qualifications include that the applicant have at least a bachelor’s degree from a U.S. institution and have a job offer from a U.S. employer that requires the H-1B Candidate to hold at least a bachelor’s degree.
This document discusses using analytics to predict the success rate of university research grant applications. It analyzes a dataset of grant applications from the University of Melbourne from 2005-2010. Several machine learning models are trained on features like researcher characteristics and grant details. The best model predicts 1338 of 2176 applications from 2009-2010 would be successful, exceeding actual prior success rates. Key factors identified as increasing success included prior successful grants, applications by researchers with PhDs, and applications submitted early in the year when more budget is available. Insights can help universities strategize grant submissions and better understand success determinants.
Pilot Study 2 on Processes for Determining the Accuracy of Credit Bureau Info...Luigi Wewege
This study examines accuracy in consumer credit reports using a nationally representative sample of consumers with credit histories. Participants in the study examined their credit reports from the three national credit reporting agencies and identified potentially material errors. Participants disputed any identified errors using the FCRA dispute process and were provided with new credit reports and credit scores. The original reports were then compared with the new reports and if any modifications were made as a result of the disputes, the impact of errors on the consumer’s credit score was determined. Overall, 20% of consumers disputed errors and had modifications made to at least one credit report. In many cases, the change to the credit report had no effect on credit score; 13% of consumers experienced a change in score due to their dispute. When focusing on changes in score that could impact a consumer’s credit risk classification, the study found that 5% of consumers had errors on their credit report that may be affecting the likelihood of receiving credit or the terms of credit received.
Canada's healthcare claims management market is likely to grow at a CAGR of 23.4% from a market size of $2.13 Bn in 2022 to $11.48 Bn in 2030. The rise in research and development expenditure in healthcare along with the new technological advancements and the increasing trend of automation in healthcare acts as a growth factor for the market. To get a detailed report, contact us at - info@insights10.com
Cn global partners provides EB-3 Visa Workerscurtispoling
Meet an important part of your hiring needs, providing hard working and reliable unskilled/skilled immigrant workers who are committed to the job for a minimum of 1 full year and more.
CN Global provide workers from Asia, including China, South Korea, Vietnam, Indonesia, USA(foreigner) where we have multiple agents in place. The cost of foreign labor is competitive as EB-3 visa workers are responsible for their arrival in costs to the US.
Part 1 Interest RatesMacroeconomic factors that influence inter.docxssuser562afc1
Part 1: Interest Rates
Macroeconomic factors that influence interest rates in general
The variables influencing microfinance interest rates for MFIs can be characterized into two general gatherings: 1) interior – the components MFIs can impact: for example work costs, specialized help, creations; or 2) outer – political risks, full scale factors, authoritative risk, and four fundamental parts reflected in the microfinance interest rates: working costs, cost of assets, advance misfortune costs, and benefit. Working expenses speak to around 60 % of the all out MFI costs and generally rely upon the credit size, age, area and customer's appraising, and so on.
Macroeconomic factors is your industry most sensitive
Like most businesses, the carrier business is affected by the monetary cycle's pinnacles and troughs. The present development in created economies—like the U.S. that is driven by the extricating money related strategy—has brought about an ascent in business certainty, mechanical creation, and universal exchange.
Impacts on the interest rates experienced within your chosen industry
In any industry, the economy assumes a urgent job that incorporates the general development of the division, and common flight, with the ever-developing interest, is no special case. To give a major picture, Airbus GMF 2016 evaluations the 20-year interest for new traveler and cargo airplane to be a little more than 33,000 airplane comprising a market estimation of over USD $5.2 trillion underlining and setting up the effect of market development.
Part 2: Stock Valuation, Risk and Returns
Stock Valuation. As indicated by the Bureau of Economic Analysis (or BEA), the genuine total national output (or GDP) expanded 4% every year in 2Q14 in the wake of diminishing 2.1% in 1Q14. With financial and modern development, work rates have expanded. This has prompted higher genuine extra cash.
From Video
My company doesn't have stocks right now, so I'll use Costco Wholesale as an example to explain the stock valuation. Future Costco Wholesale Corp stock predictions formula:
P0 = Div1 / (r – g)
P0 = Stock Price;
Div1= Estimated dividends for the next period;
r = Required Rate of Return;
g = Growth Rate
In this formula, we need to know the value of estimated dividends for the next period; required rate and return as well as growth rate. Let’s get each number individually.
g: Growth Rate = Retention Ratio x ROE
0.52 x 0.24 = 0.1248
r: Required Rate of Return.
R = D / P0 + g
0.65 / 296.09 + 0.1248 = 0.1269
Div1: Estimated dividends for the next period is 65c. Therefore, the future Costco Wholesale Corp stock predictions are:
P0 = Div1 / (r – g)
0.65 / 0.0021 = $309.52
The present stock worth and the assessed stock worth utilizing the Dividend Discount Model is higher on account of the contenders are attempting to get into the membership segment showcase. Likewise, Amazon and Sam's club have improved their online store distribution centers. So all in all, financing an organi.
Part 1 Interest RatesMacroeconomic factors that influence inter.docxkarlhennesey
Part 1: Interest Rates
Macroeconomic factors that influence interest rates in general
The variables influencing microfinance interest rates for MFIs can be characterized into two general gatherings: 1) interior – the components MFIs can impact: for example work costs, specialized help, creations; or 2) outer – political risks, full scale factors, authoritative risk, and four fundamental parts reflected in the microfinance interest rates: working costs, cost of assets, advance misfortune costs, and benefit. Working expenses speak to around 60 % of the all out MFI costs and generally rely upon the credit size, age, area and customer's appraising, and so on.
Macroeconomic factors is your industry most sensitive
Like most businesses, the carrier business is affected by the monetary cycle's pinnacles and troughs. The present development in created economies—like the U.S. that is driven by the extricating money related strategy—has brought about an ascent in business certainty, mechanical creation, and universal exchange.
Impacts on the interest rates experienced within your chosen industry
In any industry, the economy assumes a urgent job that incorporates the general development of the division, and common flight, with the ever-developing interest, is no special case. To give a major picture, Airbus GMF 2016 evaluations the 20-year interest for new traveler and cargo airplane to be a little more than 33,000 airplane comprising a market estimation of over USD $5.2 trillion underlining and setting up the effect of market development.
Part 2: Stock Valuation, Risk and Returns
Stock Valuation. As indicated by the Bureau of Economic Analysis (or BEA), the genuine total national output (or GDP) expanded 4% every year in 2Q14 in the wake of diminishing 2.1% in 1Q14. With financial and modern development, work rates have expanded. This has prompted higher genuine extra cash.
From Video
My company doesn't have stocks right now, so I'll use Costco Wholesale as an example to explain the stock valuation. Future Costco Wholesale Corp stock predictions formula:
P0 = Div1 / (r – g)
P0 = Stock Price;
Div1= Estimated dividends for the next period;
r = Required Rate of Return;
g = Growth Rate
In this formula, we need to know the value of estimated dividends for the next period; required rate and return as well as growth rate. Let’s get each number individually.
g: Growth Rate = Retention Ratio x ROE
0.52 x 0.24 = 0.1248
r: Required Rate of Return.
R = D / P0 + g
0.65 / 296.09 + 0.1248 = 0.1269
Div1: Estimated dividends for the next period is 65c. Therefore, the future Costco Wholesale Corp stock predictions are:
P0 = Div1 / (r – g)
0.65 / 0.0021 = $309.52
The present stock worth and the assessed stock worth utilizing the Dividend Discount Model is higher on account of the contenders are attempting to get into the membership segment showcase. Likewise, Amazon and Sam's club have improved their online store distribution centers. So all in all, financing an organi ...
Hiring in the Software & Data Science Sector - D.C. Metro AreaChristopher Conlan
This presentation discusses the dynamics of the data science job market in the Washington D.C. Metro Area.
Presented by John Collins of Aerotek at the Bethesda Data Science Meetup. Brought to you by Conlan Scientific.
Auto-Enrollment Retirement Plans For The People Choices And Outcomes In Oreg...Sheila Sinclair
The document summarizes early results from OregonSaves, a state-run auto-enrollment retirement savings program. Key findings include:
- Over 40,000 individuals have accumulated over $22.7 million in combined assets through contributions to OregonSaves.
- As of June 2019, approximately 24,000 contributing participants deposited an average of $110 per month, or about 5% of their pay, which is the default savings rate.
- OregonSaves has provided workplace retirement plan access for employees of small to mid-sized firms, with an average firm size of 36 employees and average employee earnings of $2,182 per month.
Phase 1 - Research Data CollectionName Points.docxkarlhennesey
Phase 1 - Research Data Collection
Name:
Points: 50
States:
Due: Week 5
Country:
Source
Topics Covered (bulleted list)
Citation Information (MLA Style)
1.Journal articles
(Press enter if you reach the bottom of the cell and need more space.)
· Financial aspect to the health care services and delivery.
· Shortages of health professionals.
· Main source of finance to health care services
· Linkage with other organization.
(Press tab to move to next row.)
Reagan, Michael D. The accidental system: health care policy in America. Routledge, 2018.
2.Videos
· Health care delivery checks on the personnel and facilities available for use.
· Poor teamwork in the health sector by health providers.
· Health professionals at the delivery of services.
Khan Academy. “Healthcare system overview | Health care system | Heatlh & Medicine | Khan Academy” Youtube. Retrieved at https://www.youtube.com/watch?v=LMHxxvbzFqc
3.Government Data
· Environmental impact on the health care services delivery.
· Details on the socio-economic and political aspect to health care system.
· Marginalize areas in terms of health provision.
United States Census Bureau. “Health Insurance Coverage in the United States” 2017
4.Insurance Data
· Payment mode in private and public health care system.
· Implementation of laws in the health sectors
· Need for worker compensation.
United States Census Bureau. “Health Insurance Coverage in the United States” 2017
5.Related articles of professionals organizations
· Rural access to health care services systems.
· Outdoor-Community health care.
· Good road networks to allow health services reach.
· Challenges of the health care delivery in rural areas.
Osman, Ferdous Arfina, and Sara Bennett. "Political Economy and Quality of Primary Health Service in Rural Bangladesh and the United States of America: A Comparative Analysis." Journal of International Development (2018).
6. Shi and Singh textbook
· Health services financing.
· Health care delivery policies and priorities.
· Proper health organization management.
. Shi, Leiyu. Delivering Health Care in America : a Systems Approach. Sudbury, Mass. :Jones & Bartlett Learning, 2012.
Phase 2:
Comparison-contrast chart
Name Adedotun Adereti
The U.S. / UK comparison-contrast chart.
U.S
U.K
· In U.S Healthcare payment is catered for highly by government initiated programmes for example Medicaid.
· In the U.K healthcare is funded highly by taxation through the National Health Services.
· Here there is no shortage of health staff as there are adequate doctors, nurses, and other medical practitioners.
· There is a shortage of health workers in the UK a thing that has led to the vast advertising of job opportunities for health practitioners.
· The medical practitioners are highly train ...
1_2012 Rhode Island Hospital Payment Study FinalKim Paull
This study analyzes patterns of hospital payment variation in Rhode Island. It finds substantial variation in payments for similar care, with commercial plans generally paying hospitals higher rates than Medicaid or Medicare. Payments also varied significantly between hospitals, with larger hospitals like Lifespan and Care New England receiving higher payments from commercial plans. The study aims to inform policies around rising healthcare costs by examining a key driver of spending - the price of hospital care.
1. An Explorative Study of H1-B Visas
Rosy Garcia-Rivas, Kevin Huang, Macaria Robinson, Meredith Valenzuela
May 2015
Abstract
The H-1B visa is a non-immigrant visa in the US that allows US employers to temporarily employ
foreign workers in specialty occupations. Our group conducted a population-based assessment of H-1B
visa approval ratings to predict whether or not a visa would be approved to an applicant, based on many
different factors. We found that applicants in math/science industries were the highest proportion of
applicants for H-1B visas. Furthermore, we found that people do not solely follow areas of highest pay,
as people in the health and math/science fields tended to move to the Northeast, while the Midwest and
West coast pay higher rates. What we can extrapolate from this information is that people in different
fields tend to flow to their particular regions of interest for different reasons.
1
2. Contents
I Introduction 3
II Questions 3
III Description of the Sample and Data Collection 3
IV Variables of the Study and How They Were Measured 4
V Statistical Methods 5
1 Logistic Regression 5
2 Contingency Tables 5
VI Summary of Findings 6
VII Conclusions Drawn From the Study 8
VIII Shortcomings 9
IX Recomendation for Future Research 9
X R Code 9
List of Tables
1 Table of Main Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2 Logistic Regression: Wage Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
3 Logistic Regression: Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
4 Applicants, Wage by Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
5 Applicants by Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
6 Proportion of Certified Applicants by Region . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
7 Proportion of Certified Applicants Applying by Industry . . . . . . . . . . . . . . . . . . . . . 8
8 Two Way Table of Median Wage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
List of Figures
1 Map of Employed H1-B Visa Workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 Barplot of Industries by Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2
3. Part I
Introduction
The H-1B visa is a non-immigrant visa in the United States that allows US employers to temporarily employ
foreign workers in specialty occupations. The minimum requirements for obtaining this visa classification
are:
(1) the applicant must have a US employer to sponsor him/her
(2) the job that the applicant is applying must be requiring bachelor’s degree or higher
(3) the job duties and the applicant’s education/work experience must be correlated
(4) the job must pay at least the prevailing wage in the area for that service
More than ten percent of the undergraduate population at the University of California, Los Angeles
are international students. The H-1B visa is important tomany of these students because it is one avenue
for them to remain in the US after graduation. Therefore, our group wanted to investigate the likelihood
of, and significant contributors to, obtaining an H-1B visa. Certain industries, such as the technology and
finance industries, congregate in specific locations around the US, for example San Francisco and New York.
Moreover, we have observed that there seems to be a higher proportion of international students in certain
academic fields compared to others at UCLA. Therefore, we expected the strongest contributing factors of
whether an H-1B visa was approved or not to be:
(a) industry the petitioner would be employed in
(b) geographic location of the job
(c) the interaction between industry and geographic location
Unfortunately, our data for showed a 99% approval rate for H-1B visa approvals and was not represen-
tative of the actual acceptance rate in the US in 2007, which meant attempting to determine the strongest
contributing factors of H-1B visa approval and predicting whether a visa would be approved or not would
be meaningless with our data. Therefore, we chose to focus on finding extracting information about the
applicants of approved H-1B visas, focusing mainly on their chosen industry, academic field, and the geo-
graphic location of their employment. Given that the US is a hub for cutting edge research and technology
in health, biotechnology, and business/finance, and is also a strong force in pop culture and the arts, we
thought this endeavor would be the most informative, and give us insight on why people wanted to move
to the US, and subsequently where the US stands in the eyes of outsiders. Furthermore, we hope that a
deeper understanding of approved visas will provide future applicants with a better idea of which locations
and industries to consider.
Part II
Questions
The following are the questions that we hope to answer through our analysis of the H1-B Visa dataset.
• Is the approval likelihood higher for people working in certain fields than other fields?
• Is the likelihood higher for people applying for an H-1B visa in different regions?
• Is the mean prevailing wage for people applying for the H-1B within the industries higher in certain
regions than others?
• Can we predict whether a person’s H-1B visa will get approved based on certain variables?
3
4. Part III
Description of the Sample and Data Collection
The H1-B data used in this report was provided to ETA by employers who submitted foreign labor cer-
tification applications for the year 2007. The original data set has 426,597 observations and 39 variables,
although we chose to limit our study to H1-B visas that were approved. The sample includes information
about the employer’s location, the job position, and salary, but does not offer any information about the
applicants themselves, such as gender, age, or country of origin. It is important to note that the sample
data was not supplied by H1-B applicants themselves, but rather by their employers to the Bureau of Labor
Statistics’ Occupational Employment Statistics survey. The Bureau of Labor Statistics’ (BLS) Occupational
Employment Statistics program provides estimates used to assist in setting the wage levels in the Foreign
Labor Center (FLC) wage library.
Figure 1: Map of Employed H1-B Visa Workers
Map based on location. Color shows detail about Industry. Size shows median wage. Data excludes Alaska
and Hawaii. BLUE = Education. ORANGE= Engineering and Computer Sciences. GREEN= Finance and
Business. RED= Health. PURPLE= Science and Math.
Figure 1 shows the distribution of H1-B workers by industry and wage. From this graph we see that most
applicants are focused on the coasts, with a significant portion spread out throughout the Midwest. The
mountainous regions in the west are more sparsely populated. Applicants in the health and science/math
industries appear to be the highest proportion of, and richest, approved visa applicants across the country.
Moreover, this figure shows that health and education jobs are distributed in a way that appears to follow
the US population’s overall distribution. This is in stark contrast to the distribution of business/finance and
science/math applicants, who seem to cluster around big cities.
4
5. Part IV
Variables of the Study and How They Were
Measured
Variables of interest for this study centered around industry type, geographical location, and salary. The
variables Industry and Region were created from the variables Job Title and State, respectively. The levels of
Industry are Finance and business, Health, Education, Science/Math, and Engineering and Computers, while
the levels of Region are Midwest, Northeast, South, and West. We chose to categorize Industry by these fields
because we felt these professions were encompassing of the careers of H1-B visa workers. Additionally, Case
Status initially included four different levels: Certified, Denied, Hold, and Pending. Since we are primarily
concerned whether a candidate will be approved or not we subsetted the data to include only certified and
denied cases.
Variable Description Measurement
Industry Industry of Employment Categorical
Region Region of Employment Categorical
Case Status Approval status- certified or denied Categorical
Wage_Rate_From_1 Employer’s proposed wage rate Numerical
Table 1: Table of Main Variables
Part V
Statistical Methods
1 Logistic Regression
Our initial focus for this research was to classify whether or not a candidate’s visa application would be
accepted or denied, and to determine which factors are most important to being approved. Because our
response was binary (“Accepted” vs. “Denied”), we decided to run a logistic regression. Logistic regression
would allow us to classify the visa petitions while steering clear of other complex models that could possibly
overfit the data.
To begin, we wanted to ensure we had a portion of the data to test our model with, so we cut 30% of the
cases into a testing dataset and used the remaining 70% of the cases to build our model. We built our logistic
regression model using backward selection on the training dataset to see which model provided the lowest
Akaike Information Criterion (AIC). The AIC punishes models for complexity, thus preventing a model from
overfitting the data that it was constructed with. From there we used cross-validation techniques to see
which of the models with the lowest AIC also provided the lowest test mean square error. When tabling our
success rate, we learned that our model performed well when predicting an approved visa but performed very
poorly when predicting a denied visa. To investigate why our model’s accuracy was highly skewed we tabled
the data and saw that the data itself was significantly skewed towards approved visas, where approved visas
made up 99% of the observations. Moving forward, we then decided to construct a simple logistic regression
models with each predictor to decide which of the levels of each variable were above the 95% significance
level. The simple logistic regression provided insight that we applied to our next method, contingency tables.
2 Contingency Tables
Due to the limitations of our data (99% of cases are certified), we used contingency tables to provide deeper
analyses of the relationships between region, industry, and wage. Contingency tables were beneficial in com-
paring the frequencies of different combinations of industries and location. Getting a better understanding
5
6. of the popularity of industries in different locations allowed allows us to create a profile of characteristics of
H1-B visa workers and employers, and hopefully will allow us to inform our peers and others of ideal places
to work.
Part VI
Summary of Findings
After building our logistic regression models we found that most of the models we obtained gave a successful
prediction rate of around 99%. Since 99% of the applicants in our data set were “certified”, the logistic
regression by itself was not appropriate to answer our questions.
All the variables, except for the wage, were statistically insignificant. See table 2. Therefore, we ran
three simple logistic regression models to inform us of which levels of factors were potentially significant for
industry and region, and then used contingency tables to analyze the prominence of those factors.
For every one unit increase in wage, the likelihood of certification increases by 1. Looking at the con-
tingency tables, we can see that applicants that have applied for a H1-B visa in the health industry, versus
the business & finance industry, changes the odds by 1.4. On the other hand, . . . . (Can we add another
example here because I’m still not understanding the table of odds) We see that the health and business &
finance industries have the greatest positive impacts on certification.
We then investigated region through the use of contingency tables. We began by looking at the candidates
in the Northeast region of the United States. The majority (33%) of candidates applied in the Northeast
while only 16% applied in the Midwest (see table 3). After investigating the candidates by industry, we found
that the science & math industry dominates in popularity, with the vast majority of immigrants (68.7%)
applying for work in these fields (see table 3). It is our belief that the United State’s gap in science & math
education - in comparison to other developed countries - is one reason for the popularity of the industry.
Although there is a relatively small percentage of US natives seeking careers in science and math there is a
great interest of foreign individuals wanting to obtain visas to work in the U.S.’s in these fields.
Variable Coefficient p-value
Intercept -3.18 <2e-16
Wage_Rate_From_1 -2.46e-5 < 2e-16
Odds
0.04
1.00
yi = 3.18 + WageRateF rom1 ⇤ xi
Table 2: Logistic Regression: Wage Rate
For every one unit increase in wage, the likelihood of certification increases by 1.
Variable Coefficient p-value
Intercept -5.99 <2e-16
Industry:Education -0.1359 0.4495
Industry:Health 0.3960 0.0313
Industry:Science & Math -0.3169 0.0165
Odds
0.003
0.87
1.4
0.72
yi = 5.99 + Industry ⇤ xi
Table 3: Logistic Regression: Industry
Having applied for a H1-B visa in the Health industry, versus the Business & Finance industry, changes
the odds by 1.4. We see that the Health and Business & Finance industries have the greatest positive impacts
on certification.
We then investigated region through the use of two by two tables. We began by looking at the candidates
in the Northeast region of the United States. The majority (33%) of candidates applied in the Northeast
6
7. while only 16% applied in the Midwest region (see table 3). After investigating the candidates by industry,
we found that the Science and Math industry dominates in popularity, with the vast majority of immigrants
(68.7%) applying for work in these fields (see table 3). It is our belief that the United State’s gap in
science/math education - in comparison to other developed countries- is one reason for the popularity of the
industry. Although there is a relatively small percentage of U.S. natives seeking careers in science and math
there is a great interest of foreign individuals wanting to obtain visas to work in the U.S.’s in these fields.
Northeast West Midwest South
Number of Applicants 101,865 (33.3%) 66,304 (21.7%) 51,110 (16.7%) 86,547 (28.3%)
Median Prevailing Wage $58,000 $70,620 $55,000 $55,000
Table 4: Applicants, Wage by Region
Business&Finance Education Health Science & Math
41,487 (13.5%) 35,231 (11.5%) 19,206 (6.27%) 210,329 (68.7%)
Table 5: Applicants by Industry
Figure 2: Barplot of Industries by Region
Because we are only interested in the characteristics of the applicants that get certified, we subsetted the
data by removing all denied cases.
Looking at Table 7, we are able to see that in the business & finance industry, 38.9% of the people that
are certified reside in the Northeast, while only 11.50% of the people certified in business & finance reside in
the Midwest. One possible explanation of these results is that the low population density and small number
of big cities causes immigrants in the business & finance industry to avoid the Midwest. Interestingly enough,
only 21.27% of the applicants are certified to work in the West. We expect this is because the Northeast
contains NYC and therefore Wall Street, which carries a big name and is the central hub for finance in the
US, drawing people away from the Midwest and West coast. Interestingly, 39.5% of the certified foreigners
in the education industry (Table 7) are migrating to the South. Looking at the two-way table of the median
7
8. start wage by region and by industry in conjunction with Figure 2, we see that all of the regions pay the
highest in the health industry except the Northeast, yet the majority of approved applicants are moving to
the Northeast in spite of this fact.
West Midwest Northeast South
Business & Finance 0.1336 0.0933 0.1584 0.1339
Education 0.0959 0.1226 0.0835 0.1607
Health 0.0522 0.0782 0.0638 0.0599
Science and Math 0.7181 0.7057 0.6941 0.6453
Table 6: Proportion of Certified Applicants by Region
West Midwest Northeast South
Business & Finance 0.2317 0.1149 0.3892 0.2796
Education 0.1806 0.1779 0.2415 0.3950
Health 0.1805 0.2084 0.3393 0.2703
Science and Math 0.2263 0.1714 0.3361 0.2654
Table 7: Proportion of Certified Applicants Applying by Industry
West Midwest Northeast South
Business & Finance $61,000 $57,600 $62,000 $53,672
Education $47,500 $45,000 $47,000 $42,500
Health $85,000 $65,000 $56,650 $71,760
Science and Math $75,000 $55,000 $58,240 $58,000
Table 8: Two Way Table of Median Wage
To summarize, only the business & finance industry followed the region that pays the most. For the
health industry, the West pays the H-1B applicants the most; however, 33.9% of the applicants in health are
migrating to the Northeast while only 18% are certified in the West. The same is true of the science & math
industry: 33% of certified H-1B foreigners are migrating to the Northeast, even though the West pays 1.28
times more. Also, looking at the map (Figure 1), we see that the jobs in the health and education industries
are distributed in a similar way to the distribution of the US population, indicating an evenly growing
demand throughout the US for education and health workers. On the other hand, the jobs in business &
finance and science & math are more clustered in bigger US cities, indicating a need for large populations
for these industries and possibly suggesting a need for faster communication in these industries.
Part VII
Conclusions Drawn From the Study
According to Table 4 the Midwest, when compared to the Northeast is a popular location for immigrant
workers granted H1-B visas status for the Education, Engineering/ Comp. Sciences, Finance and Business,
and Science and Math but the Health industry. It also appears as though the Northeast, when compared to
the South, is a less popular location of work for those same industries, excluding Health.
After analyzing all the contingency tables, we discovered that in both Business & Finance industry and
for the Education industry, the highest proportion of applicants for the H-1B visa tend to follow the regions
where the prevailing wage is the highest. However, for the Health industry, the Midwest actually pays the
most; however, 33.9% of the applicants in Health are migrating to the Northeast instead, while only 20% are
migrating to the Midwest. The same happens for Science and Math; 33.6% of certified H-1B foreigners are
8
9. migrating to the Northeast, even though the West pays 1.19 times more. We recommend that individuals
seeking H1-B visa status in the field of:
• Health to move to the Midwest
• Business and Finance to move to the west, but slightly less chances of getting certified science
• Math are wanted in all regions but to get the highest pay to move to the west.
Part VIII
Shortcomings
The shortcomings of our research are due to a non-representative sample of H1-B visa applicants. Given
a sample with a more rate of visa approval more consistent with reality would have allowed us to create a
more ideal logistic regression model. Moreover, a lack of information about applicants, i.e. gender, country
of origin, networth, etc., kept us from drawing more comprehensive conclusions about H1-B applicants and
the likelihood of certification. Because of this, we have too many unknown covariates to account for.
Working with a dataset that was highly skewed was problematic but at this time in the project we had
already ran into issues. We were initially assigned an education dataset. However, given that we had already
worked with similar data in another class we felt it best to gain exposure in something different. Since all of
our groupmates were interested in criminal data we found a dataset that surveyed prisoners. Unfortunately,
this dataset had a lot of missing information and asked over three thousand questions. Thus, we ultimately
moved toward the dataset of H-1B visas which appealed to our entire group and was rich with information.
Part IX
Recomendation for Future Research
We recommend doing a more comprehensive analysis using both employer data and applicant data. The
country of origin of the applicant or the gender of the applicant might have a more significant impact on
visa approval than location of employment or industry
Part X
R Code
load("~/Downloads/H1B.RData")
# Getting Only Cases that are Certified or Denied
c=which(h1b$Case_Status=="Certified")
d=which(h1b$Case_Status=="Denied")
k=c(c,d)
hb=h1b[k,]
#Getting to know the data
#Dealing with dates for the glm
#What time of the year is the petition submitted?
library(lubridate)
hb$timeyr=month(as.POSIXlt(hb$Submitted_Date, format="%d/%m/%Y"))
#Gets the month that it was submitted
#Breaks down into what part of the year
hb$timeoyr=rep("End",length(hb$timeyr))
hb$timeoyr[which(hb$timeyr<9)]="Middle"
hb$timeoyr[which(hb$timeyr<5)]="Beginning"
#Variable the time the visa would last?
9
10. hb$visa_last=as.numeric(hb$End_Date-hb$Begin_Date)/364.25
hb$visa_last=round(as.numeric(hb$visa_last),1)
#Cleaning up State Variable and Creating Regions
#States were sorted according to Regions using the Census data
hb$State=as.character(hb$State)
temp=nchar(hb$State, type = "chars", allowNA = FALSE)
st.rm=which(temp>2)
hb$State[which(hb$State=="Newton")]="MA"
hb$State[which(hb$State=="Seattle")]="WA"
hb$State[which(hb$State=="Chantilly")]="VA"
hb$State[which(hb$State=="New York")]="NY"
#Create a Region Variable to get rid of all the levels in State
hb$Region<-rep("",dim(hb)[1])
NE<-c("CT","PA","NJ","NY","RI","NH","VT","ME","MA")
MW<-c("ND","SD","NE","KS","MO","IA","MN","WI","IL","IN","OH","MI")
S<-c("MD","DE","DC","VA","WV","KY","TN","GA","AL","MS"
,"FL","AR","LA","OK","TX","NC","SC")
W<-c("WA","ID","MT","WY","CO","UT","AZ","NM","NV","CA","OR","AK","HI")
hb$Region[which(hb$State %in% NE)]="NorthEast"
hb$Region[which(hb$State %in% MW)]="MidWest"
hb$Region[which(hb$State %in% S)]="South"
hb$Region[which(hb$State %in% W)]="West"
#Recoding Occupational Code by Industry
hb$Industry=rep(NA,dim(hb)[1])
hb$Job_Title=as.character(hb$Job_Title)
hb$Job_Title<-iconv(enc2utf8(hb$Job_Title),sub="byte")
hb$Job_Title=lapply(hb$Job_Title,tolower)
#Engineers and Computer Related
archit<-grep("archit",hb$Job_Title)
eng<-grep("engin",hb$Job_Title)
tech<-grep("tech",hb$Job_Title)
comp<-grep("com",hb$Job_Title)
prog<-grep("progr",hb$Job_Title)
dev<-grep("developer",hb$Job_Title)
sysans<-grep("systems analyst",hb$Job_Title)
sysan<-grep("system analyst",hb$Job_Title)
soft<-grep("software",hb$Job_Title)
CE<-c(archit,eng,tech,comp,prog,dev,sysans,sysan,soft)
hb$Industry[CE]="Engineering & Computer"
dat<-grep("data",hb$Job_Title)
math<-grep("math",hb$Job_Title)
stats<-grep("statist",hb$Job_Title)
chem<-grep("chemis",hb$Job_Title)
SM<-c(dat,math,stats,chem)
hb$Industry[SM]="Science & Math"
med<-grep("medic",hb$Job_Title)
clinic<-grep("clinic",hb$Job_Title)
phys<-grep("physic",hb$Job_Title)
physo<-grep("physio",hb$Job_Title)
dentist<-grep("denti",hb$Job_Title)
dental<-grep("dental",hb$Job_Title)
pathol<-grep("pathol",hb$Job_Title)
pharm<-grep("pharm",hb$Job_Title)
sci<-grep("scientist",hb$Job_Title)
10
12. ########## Following R Code utilizes the cleaned data set we submitted
##########
#********************************BEGIN USE OF CLEANED DATA SET***********************************#
hb$Industry<-relevel(as.factor(hb$Industry),"Engineering & Computer")
#splitting into training and testing or
set.seed(7)
test=sample(1:dim(hb)[1],(dim(hb)[1]*.3))
train=hb[-test,]
testing=hb[test,]
#######MODELING ATTEMPTS######################################
######t<-glm(Case_Status~visa_last,data=hb,family="binomial")
######tt<-glm(Case_Status~timeoyr,data=hb,family="binomial")
######ttt<-glm(Case_Status~Nbr_Immigrants,data=hb,family="binomial")
######t4<-glm(Case_Status~Prevailing_Wage_1,data=hb,family="binomial")
######t5<-glm(Case_Status~Region,data=hb,family="binomial",subset=train)
######total<-glm(Case_Status~visa_last+Region+timeoyr+Program.Designation+
######Nbr_Immigrants+Wage_Rate_From_1+Wage_Rate_Per_1+Part_Time_1+
###### Prevailing_Wage_1,data=train,family="binomial")
######step(total,direction="backward")
######sb1<-glm(formula = Case_Status ~ visa_last + timeoyr + Program.Designation +
###### Wage_Rate_From_1 + Wage_Rate_Per_1 + Part_Time_1 + Prevailing_Wage_1,
###### family = "binomial", data = train)
#AIC 9549
######FUll<-glm(Case_Status~visa_last+timeoyr+Wage_Rate_From_1+Wage_Rate_Per_1+Part_Time_1+
###### Prevailing_Wage_1+Withdrawn+Industry,data=train,family="binomial")
#AIC 9580
######step(FUll,direction="backward")
######AIC(FUll)
#full with Visa last interactions/ AIC 9356
######FwithvlI<-glm(Case_Status~visa_last+timeoyr+Wage_Rate_From_1+Wage_Rate_Per_1+Part_Time_1
###### Prevailing_Wage_1+Withdrawn+Industry+visa_last*timeoyr+visa_last*Wage_Rate_From_1+
###### visa_last*Wage_Rate_From_1+visa_last*Wage_Rate_Per_1+visa_last*Part_Time_1+
###### visa_last*Prevailing_Wage_1+visa_last*Withdrawn+visa_last*Industry,data=train,family="binomial")
#full with visa_last & time of year interaction/AIC 9287.995
######F2I<-glm(Case_Status~visa_last+timeoyr+Wage_Rate_From_1+Wage_Rate_Per_1+Part_Time_1+
###### Prevailing_Wage_1+Withdrawn+Industry+visa_last*timeoyr+visa_last*Wage_Rate_From_1+
###### visa_last*Wage_Rate_From_1+visa_last*Wage_Rate_Per_1+visa_last*Part_Time_1+
###### visa_last*Prevailing_Wage_1+visa_last*Withdrawn+visa_last*Industry+
###### timeoyr*Wage_Rate_From_1+timeoyr*Wage_Rate_Per_1+timeoyr*Part_Time_1+
###### timeoyr*Prevailing_Wage_1+timeoyr*Withdrawn+timeoyr*Industry,data=train,family="binomial")
#AIC=58633
######F3I<-glm(Case_Status~visa_last+timeoyr+Wage_Rate_From_1+Wage_Rate_Per_1+Part_Time_1+
###### Prevailing_Wage_1+Withdrawn+Industry+visa_last*timeoyr+visa_last*Wage_Rate_From_1+
###### visa_last*Wage_Rate_From_1+visa_last*Wage_Rate_Per_1+visa_last*Part_Time_1+
###### visa_last*Prevailing_Wage_1+visa_last*Withdrawn+visa_last*Industry+
###### timeoyr*Wage_Rate_From_1+timeoyr*Wage_Rate_Per_1+timeoyr*Part_Time_1+
###### timeoyr*Prevailing_Wage_1+timeoyr*Withdrawn+timeoyr*Industry+
###### Wage_Rate_From_1*Wage_Rate_Per_1+Wage_Rate_From_1*Part_Time_1+
###### Wage_Rate_From_1*Prevailing_Wage_1+Wage_Rate_From_1*Withdrawn+Wage_Rate_From_1*Indu
###### data=train,family="binomial")
######F4I<-glm(Case_Status~visa_last+timeoyr+Wage_Rate_From_1+Wage_Rate_Per_1+Part_Time_1+
###### Prevailing_Wage_1+Withdrawn+Industry+visa_last*timeoyr+visa_last*Wage_Rate_From_1+
###### visa_last*Wage_Rate_From_1+visa_last*Wage_Rate_Per_1+visa_last*Part_Time_1+
###### visa_last*Prevailing_Wage_1+visa_last*Withdrawn+visa_last*Industry+
12
13. ###### timeoyr*Wage_Rate_From_1+timeoyr*Wage_Rate_Per_1+timeoyr*Part_Time_1+
###### timeoyr*Prevailing_Wage_1+timeoyr*Withdrawn+timeoyr*Industry+
###### Wage_Rate_From_1*Wage_Rate_Per_1+Wage_Rate_From_1*Part_Time_1+
###### Wage_Rate_From_1*Prevailing_Wage_1+Wage_Rate_From_1*Withdrawn+Wage_Rate_From_1*Indu
###### Part_Time_1*Prevailing_Wage_1+Part_Time_1*Withdrawn+Part_Time_1*Industry
###### ,data=train,family="binomial")
#COmpletely full with all possible Interaction Plots
###### CFI<-glm(Case_Status~visa_last+timeoyr+Wage_Rate_From_1+Wage_Rate_Per_1+Part_Time_1+
###### Prevailing_Wage_1+Withdrawn+Industry+visa_last*timeoyr+visa_last*Wage_Rate_From_1+
###### visa_last*Wage_Rate_From_1+visa_last*Wage_Rate_Per_1+visa_last*Part_Time_1+
###### visa_last*Prevailing_Wage_1+visa_last*Withdrawn+visa_last*Industry+
###### timeoyr*Wage_Rate_From_1+timeoyr*Wage_Rate_Per_1+timeoyr*Part_Time_1+
###### timeoyr*Prevailing_Wage_1+timeoyr*Withdrawn+timeoyr*Industry+
###### Wage_Rate_From_1*Wage_Rate_Per_1+Wage_Rate_From_1*Part_Time_1+
###### Wage_Rate_From_1*Prevailing_Wage_1+Wage_Rate_From_1*Withdrawn+Wage_Rate_From_1*Indu
###### Part_Time_1*Prevailing_Wage_1+Part_Time_1*Withdrawn+Part_Time_1*Industry+
###### Prevailing_Wage_1*Withdrawn+Prevailing_Wage_1*Industry+Withdrawn*Industry
###### ,data=train,family="binomial")
######step(CFI,direction="backward")
####### Semi full model, missing some interaction plots
######Csf<-glm(Case_Status~visa_last+timeoyr+Wage_Rate_From_1+Wage_Rate_Per_1+Part_Time_1+
###### Prevailing_Wage_1+Withdrawn+Industry+visa_last*timeoyr+visa_last*Wage_Rate_From_1+
###### visa_last*Wage_Rate_From_1+visa_last*Wage_Rate_Per_1+visa_last*Part_Time_1+
###### visa_last*Prevailing_Wage_1+visa_last*Withdrawn+visa_last*Industry+
###### timeoyr*Wage_Rate_From_1+timeoyr*Wage_Rate_Per_1+timeoyr*Part_Time_1+
###### timeoyr*Prevailing_Wage_1+timeoyr*Withdrawn+timeoyr*Industry+
###### Wage_Rate_From_1*Wage_Rate_Per_1+Wage_Rate_From_1*Part_Time_1+
###### Wage_Rate_From_1*Prevailing_Wage_1+Wage_Rate_From_1*Withdrawn+Wage_Rate_From_1*Indu
###### Part_Time_1*Prevailing_Wage_1+Part_Time_1*Withdrawn+Part_Time_1*Industry,
###### data=train,family="binomial")
######step(Csf,direction="backward")
######phony<-glm(Case_Status~Wage_Rate_From_1+Part_Time_1+
###### Prevailing_Wage_1+Withdrawn+visa_last*Wage_Rate_From_1+visa_last*Part_Time_1+
###### visa_last*Prevailing_Wage_1+visa_last*Withdrawn
###### ,data=train,family="binomial")
################################################################
#Simplify the Industry Data
table(hb$Industry)
hb$newIndustry <- NULL
hb$newIndustry[hb$Industry %in% c("Engineering & Computer", "Science & Math")] <- "Science &
Math"
hb$newIndustry[hb$Industry %in% c("Finance and Business")] <- "Business and Finance"
hb$newIndustry[hb$Industry %in% c("Health")] <- "Health"
hb$newIndustry[hb$Industry %in% c("Education")] <- "Education"
#removing the levels of Case_status
hb$Case_Status <- as.character(hb$Case_Status)
hb$Case_Status <- factor(hb$Case_Status)
#disregard all other observations that are not paid by year
newhb <- hb[hb$Wage_Rate_Per_1 %in% c("Year"),]
#then we are disregarding all other observations
newhb <- newhb[newhb$newIndustry %in% c("Science & Math", "Business and Finance", "Health",
"Education"),]
newhb2 <- newhb
# subset to use for Tableau
13
14. h1bTab= subset(hb1, Case_Status=="Certified", select=c(State, Region, Industry, Wage_Rate_From_1,
Zip_Code))
write.csv(h1bTab, "h1bTab.csv")
# Contingency Table of Certified Cases
table(newhb$Region, newhb$Industry, newhb$Case_Status)
#Contingency tables
table(newhb2$Case_Status)
table(newhb2$newIndustry)
table(newhb2$Region[!newhb2$Region==""])
tapply(newhb2$Wage_Rate_From_1[!newhb2$Region==""], newhb2$Region[!newhb2$Region==""], me-
dian)
tapply(newhb2$Wage_Rate_From_1, newhb2$newIndustry, median)
tapply(newhb2$Wage_Rate_From_1[!newhb2$Region==""],
list(newhb2$newIndustry[!newhb2$Region==""],
newhb2$Region[!newhb2$Region==""]), median)
#The Median Starting Wage in Business and Finance is Highest in the NorthEast
#The Median Starting Wage in Education is Highest in the South
#The Median Starting Wage in Science and Math is Highest in the West
#The Median Starting Wage in Health is Highest in the West
#Distribution of People who are certified
table(newhb$newIndustry[newhb$Case_Status=="Certified"])
table(newhb$Region[newhb$Case_Status=="Certified"])
table(newhb$Region[newhb$Case_Status=="Certified"],
newhb$newIndustry[newhb$Case_Status=="Certified"])
prop.table(table(newhb$Region[newhb$Case_Status=="Certified"],
newhb$newIndustry[newhb$Case_Status=="Certified"]), 1)
#In all of the Regions, we see that the highest percentage of people that are
#getting denied are all from Science and Math Industry.
#The lowest in the Midwest denying rate are in the Business and Finance Industry
#The lowest in the Northeast denying rate are in the Education and Health
#The lowest in the Westt denying rate are in the Education
#The lowest in the South denying rate are in the Health Industry
prop.table(table(newhb$Region[newhb$Case_Status=="Certified"],
newhb$newIndustry[newhb$Case_Status=="Certified"]), 2)
#In the Business and Finace Industry the lowest denying rate is from the NorthEast Region
#In the Education Industry the lowest denying rate is from the South Region
#In the Health Industry the lowest denying rate is from the Midwest Region
#In the Science and the Math Industry the lowest denying rate is from the NorthEast Region
## ANOTHER MUCH SIMPLE LOGISTIC REGRESSION
#Creating training and testing data. 70% Training and 30% Testing data
set.seed(55555)
train <- newhb2[sample(nrow(newhb2), nrow(newhb2)*.70), ]
test <- newhb2[!(row.names(newhb2) %in% row.names(train)),]
#All simple models are statistically significant
glm1 <- glm(Case_Status~Region, data=train, family="binomial")
summary(glm1)
glm2 <- glm(Case_Status~newIndustry, data=train, family="binomial")
summary(glm2)
glm3 <- glm(Case_Status~Wage_Rate_From_1, data=train, family="binomial")
summary(glm3)
#2 factors+interaction model+covariate model
glm.model <- glm(Case_Status~Region+factor(newIndustry)+
Wage_Rate_From_1+Region:newIndustry, data=train, family="binomial")
summary(glm.model)
14
15. #REGIONS ARE NOT STATISTICALLY SIGNIFICANT
#Only the WAGE IS STATISTICALLY SIGNIFICANT
#THE TYPE OF INDUSTRY IS NOT SIGNIFICANT
#NONE OF THE INTERACTIONS ARE SIGNIFICANT.
#CHECKING PREDICTION ERROR
pred.vals=predict(glm.model, test, type="response")
#
pred=ifelse(pred.vals >median(pred.vals), "Denied", "Certified")
table(pred, test$Case_Status)/(length(pred.vals))
error <- 1-sum(diag(table(pred, test$Case_Status)/(length(pred.vals))))/
sum(table(pred, test$Case_Status)/(length(pred.vals)))
error
#NULL DEVIANCE TEST
glm.model$null.deviance
glm.model$df.null
pchisq(glm.model$null.deviance-glm.model$deviance, 1, lower=FALSE)
#Very small pvalue, therefore, we reject the null hypothesis indicating that we have
#statistically significance to show that the slope of the logistic regression line is not equal to zero
#RESIDUAL DEVIANCE TEST
pchisq(glm.model$deviance,glm.model$df.residual,lower=FALSE)
#VERY large pvalue. we fail to reject our null hypothesis.
15
16. Appendix
Variable Description
Submitted_Date Date the application was submitted
Program.Designation Types of H-1B Visas
Employer_Name Employer’s name
Address_1 Employer’s address
City Employer’s city
State Employer’s state
Zip_Code Employer’s postal code
Nbr_Immigrants Number of job openings
Begin_Date Proposed begin date
End_Date Proposed end date
Job_Title Job title
DOL_DecisionDate Date certified or denied
Certified_Begin_Date Certification start date
Certified_End_Date Certification end date
Occupation_Code Three digit occupational group
Case_Status Approval status- certified or denied
Wage_Rate_From_1 Employer’s proposed wage rate
Wage_Rate_Per_1 Unit of pay for proposed wage rate
Wage_Rate_To_1 Maximum proposed wage rate
Part_Time_1 Y = Part time; N = Full time position
Work_City_1 Work city (location of the job opening)
Work_State_1 Work_State_1
Prevailing_Wage_1 Prevailing wage rate
Prevailing_Wage_Source_1 Collective bargining; SESA; Other
Year_Source_Published_1 Year that the prevailing wage data was published
Other_Wage_Source_1 Year that the prevailing wage data was published
Other_Wage_Source_2 Description of the Other wage source
16