Should Athletes Gain An Unfair Advantage By Using...

The health risks that come along with using performance enhancing drugs are one reason why they should be illegal. Along with them being very dangerous, the health effects are also not very good on the body. â€Å"Diuretics are drugs that change your body s natural balance of fluids and salts (electrolytes) and can lead to dehydration† (mayo clinic). Some athletes use stimulants to arouse the central nervous system and increase heart rate and blood pressure. â€Å"Stimulants can: improve endurance, reduce fatigue, suppress appetite and Increase alertness and aggressiveness† (mayo clinic) Stimulants have side effects that can weaken athletic performance which make it hard to concentrate in whatever sport you may play. Athletes gain an unfair advantage by using PEDS. The use of PEDS is cheating because it violates constitutive rules of the activity. Cheating is wrong and one should be removed from the game if caught. This assumption is proven through a simple and straightforward argument. â€Å"Cheating is the deliberate, knowing, and voluntary violation of certain constitutive rules in order to gain a competitive advantage† (Athletes). The athlete is enhanced physically, but the value of fair play and sportsmanship is diminishing. Doping is fundamentally contrary to the spirit of sport. PEDs reflect an obsession with perfection. Athletes take PED’s for a short cut, while those competing against them are working hard the right way to improve and excel at what they do fairly. Since theShow MoreRelatedPerformance Enhancing Drugs For Professional Sports1703 Words   |  7 Pagesthe use of performance enhancing drugs. The debate on whether or not performance enhancing substances should b e allowed in professional sports has been going on for years, decades even. Many believe that using steroids and other performance enhancers should automatically disqualify an athlete from ever being able to be a member of the Hall of Fame, in sports in general, not just in Major League Baseball. However, there is an argument to be made to make the use of performance enhancing drugs legal inRead MoreUse Of Performance Enhancing Drugs1338 Words   |  6 PagesResearch Paper Final Draft The Use of Performance-Enhancing Drugs in Sports According to NPR.com, the argument over the use of performance-enhancing drugs by professional athletes has been at the center of an international ethical debate for many years (Katz). Many people argue that these drugs should be allowed, while others argue that these drugs should be banned from professional sports. Professional sports athletes should avoid the use of performance-enhancing drugs so that the integrity of sportsRead MoreDoping : Doping And Doping1216 Words   |  5 Pagesplayed competitive sports, they have sought to gain a cutting edge against their enemies. Uncommonly, there are records of the use of enhancing drugs that goes back to historical times. Doping is questionable the most talked about in today’s sports. Doping basically refers to the illegal use of drugs, mostly steroids, which are aimed at improving the performance of athletes. Doping has proved to be quite a setback in sporting competitions since athletes who do not deserve medals and some c ompetitiveRead MoreThe Ethics of Cognitive Enhancement1312 Words   |  6 Pagesthe Joneses, we want to surpass them. In the pursuit of excellence, some people will take drugs as an enhancement for their cognitive abilities. What makes this path to excellence ethically questionable? There are two large issues to using cognitive enhancements: fairness and the pressure to use them. While there may be nothing intrinsically wrong with using cognitive enhancers, the use of these drugs will likely have major side-effects on society which need to be taken into consideration. CognitiveRead MorePro Doping in Sports Debate825 Words   |  4 Pagesassume risks that we think are worth taking, shouldnt athletes have the same freedom as anyone else? In particular, if athletes prefer the gains in performance allegedly provided by the use of steroids, along with the increased risk of harm to the alternative of less risk and worse performance, what gives anyone the right to interfere with their choice? After all, if we should not forbid smokers from risking their health by smoking, why should we prohibit track stars or weightlifters from taking risksRead MoreUse of Steroids by Athletes Essay1538 Words   |  7 Pages   Ã‚  Ã‚   A survey was presented to 198 U.S athletes with the following scenario. You are offered a banned performance enhancing substance that comes with two guarantees: 1) You will not be caught. 2). You will win every competition you enter for the next five years and then you will die from the side effects of the substance. Would you take it? More than half the athletes said yes. As we can infer from the above survey, a large number of professional athletes are willing to risk their lives for theRead MoreEthical Dilemmas1304 Words   |  6 Pagesof the players are using a new performance enhancing drug. Paul’s teammates are always reminding him that it is not a banned substance, and the coach has turned a blind eye to the whole issue. Paul told me that his coach is making some changes, and he may lose his starting position. He is starting to think about actually taking this drug so he does not lose his spot. The dilemma is that Paul could just take the drug, and get to keep his position as a starter. Since the drug is not listed as bannedRead MoreThe Use Of Anabolic Steroids And How Athletes Are Cheating1482 Words   |  6 PagesThe athletic sports world has drastically evolved since the beginning of the creation of sports. Athletes are becoming bigger, faster and stronger. The competitive edge has started to increase and guys are looking for ways to enhance their performances. Many turn to repetitive practices and healthier diets, while some turn to protein powder. No matter the method the average athlete is trying their best to propel his or her efforts past previous marks. Most stick to natural remedies, but there areRead MorePerformance Enhancing Drugs in Sports1686 Words   |  7 PagesPerformance Enhancing Drugs in Sports Athletes use performance enhancing drugs to boost their game. The professionals who use these drugs are ruining the integrity of the game. Many people don’t understand why professional athletes would go to such extreme measures to be better when they have already proven themselves. Athletes are just taking away from their natural ability by using these dangerous drugs. The risk of using performance enhancing drugs is a lot greater than the reward, because anRead MorePerformance Enhancing Drugs Should Be Allowed2071 Words   |  9 Pagesis whether performance enhancing drugs should be allowed. According to the free dictionary, performance enhancing drugs is defined as â€Å"Any drug used to gain an advantage in sports. Such drugs may improve endurance or strength or accelerate healing after injury†. Performance enhancing drugs was first acknowledged as a problem in professional sports in 1967 when the international Olympic committee created a medical board due to the h eightened use. Today there are seven types of performance enhancers

Identify and Respond to Children and Young People at Risk...

1. List the observations that together indicate possible ‘risk of significant harm’ Risk of harm is the indication that the child may suffer physical, emotional, and psychological harm because of neglect by the parent or guardian responsible for his care. In Jamelle’s case, indication includes physical signs like; †¢ Various large bruises †¢ Smelling strong urine stale †¢ Dirty and unwashed skin †¢ Lack of fresh changing clothes †¢ Vomiting †¢ Bloodshot eyes †¢ Psychological l problems such as timidness 2. Response to the situation in preparation of drawing a report The response will include investigating the factor that can lead to risk of harm to the child, including social isolation of the child with other children, and family on†¦show more content†¦You should not ignore the risk of harm indicator and exercise bias or favouritism motives. Your decisions should be guided by crucial standards, legislations, and professional ethics code. Case 2 1. List the observations that together indicate possible ‘risk of significant harm’ †¢ Aggression and fighting with peers †¢ Loneliness †¢ Aggression towards staff members †¢ Disrespect to fathers †¢ Wearing heavy attire on warmer days †¢ Red welts †¢ Burn scars on the stomach and at back area 2. Response to the situation in preparation of drawing a report Tajs aggressiveness, social isolation, changes in dressing and injuries shows that he perceives psychological complications. His actions are just but coping mechanism to stress. It is evident that he was exposed to a lot of abuse during the holiday that has altered her psychological reactions. Helping the Taj will require providing him with stress management support. As my duty, I will draw her close, get the source of his problem and offer counselling lessons. The actions or work practices to be taken As a caregiver I will ensure that there are valid grounds and current concern that Taj is at risk. I will intervene by discussing the concerns with the supervisor especially from the evidence of injuries. I will try to get Taj close, in case he might be ready to share his predicament. The child response will beShow MoreRelatedCwdc Standards 1 Essay1368 Words   |  6 PagesStandard 1 Understanding the principles and values essential to fostering children and young people. Principles Values: A Early experiences significantly impact later life. Children learn from birth. The most significant brain development happens in the early years. Early education results from interactions between children and all adults who serve as their caregivers, including parents, relatives, baby sitters, teachers, and foster carers. Values inform or influence choices and action acrossRead MoreIdentify the current legislation ,guidelines ,policies ad procedure for safeguarding the welfare of children and young people including e.safety935 Words   |  4 Pagesthey are at risk of significant harm. 1. be healthy 2. stay safe 3. enjoy and achieve 4. make a positive contribution 5. achieve economic well-being Child abuse , harm or the likelihood of harm from physical ,emotional or sexual abuse,neglect and failure the thrive not based on illness ,or bullying and harassment. All in setting for children and young people in England and Wales qre the result of legislation passed in parliament,including ,England and Wales, the children Act 1989Read MoreContribute to Children and Young Peoples Health and Safety (Cu1512)1354 Words   |  6 PagesC U 1 5 1 2 Contribute to children and young people’s Health and Safety 1.1 Outline the health and safety policies and procedures of your work setting. My work setting follows the laws and standards set by the Health and Safety Act 1974:- * Ensure that the environment is safe, secure with high quality of hygiene practices for staff, children and their families and any member of the public while using the premises. * Impose a total ban of smoking in the nursery indoor/ outdoorRead MoreChild Abuse Prevention And Treatment Act1400 Words   |  6 Pagesinjury, death, emotional harm or risk of serious harm to a child. According to The Federal Child Abuse Prevention and Treatment Act, as amended by the CAPTA Reauthorization Act of 2010, at a minimum, child abuse and neglect is defined as, Any recent act or failure to act on the part of a parent or caretaker which results in death, serious physical or emotional harm, sexual abuse or exploitation; or An act or failure to act which presents an imminent risk of serious harm (Children s Bureau, 2010).Read MoreHow Child Sexual Exploitation Is Understood By Social Workers And Other Professionals Essay1393 Words   |  6 Pagesby Dr Sophie Hallett, called ‘An uncomfortable Comfortableness’: ‘Care’, child protection and child sexual exploitation. The reason I have chosen her article is because she is social policy academic, lecturer and an expert within the field of children, young adults and social care; specialising within the field of youth and child sexual exploitation (Sophie Hallett, no date). My aim is to critically evaluate Hallett’s article; firstly, highlighting the limitations of her arguments and then the strengthsRead MoreExplain Why It Is Important to Recognise and Respond to Concerns About Children and Young Peoples Development, Give Examples 2.2730 Words   |  3 Pagesimportant to recognise and respond to concerns about children and young peoples development, give examples 2.2 Childrens and young peoples development is affected by a wide range of factors, their background, health and the environment which they are growing up in all have an impact on their development. It is important to have some understanding of these factors in order to be able to respond and recognise to any concerns that may arise. Children and young people may come from a wide rangeRead MoreCurrent Legislation, Guidelines And Policies Inform Safeguarding1208 Words   |  5 Pagesoutlines responsibilities for all practitioners. Children Act 1989 Allocates duties to local authorities, courts, parents and other agencies in the UK to ensure children are safeguarded and their welfare is promoted. It focuses on the idea that children are best cared for by their own families, however it also makes provisions for parents and families who do not cooperate with the professional bodies. The Data Protection Act 1989 All those involved with children are likely to hold personal information aboutRead Morecommuntiy service assignemnt Essay1544 Words   |  7 Pages Task 1 In your own words, answer the following questions. 1. What does the term ‘risk of significant harm’ mean? Answer q1. If the circumstances that are causing concerns for the safety welfare and wellbeing are present to significant extent. 2. What are the types of child abuse? Answer for q2. 1.physical 2.sexual 3.neglect 4.phychological 3. Identify three possible indicators or signs for each type of child abuse/neglect. Answers q3 1 physicalRead MoreDiscrimination and Child1723 Words   |  7 Pages SCMP3-1.1 How current and relevant legislation and policy affects work with children and young people. Children’s individual needs Quality of care Choice of service Management staffing Complaints protections Plan to support child, working in partnership with social worker and adhere to policies. Individual needs are met. To maximise the chance of positive outcomes for children. All of the policies and procedures by which I work are defined by The Childrens Act 1989 which legislates for EnglandRead MoreSafeguarding Tasks1444 Words   |  6 Pagesminimum upon which every child and young person should be able to depend.Protection of Children in England ‘A progress report 12th March 2009’ | 1. Make a booklet†¢ an outline of current legislation, guidelines, policies and procedures within own UK Home Nation affecting the safeguarding of children and young people†¢ an analysis of how national and local guidelines, policies and procedures for safeguarding affect day to day work with children and young people†¢ an explanation of how the processes

Analysis Business Market of The Ventura Boats †MyAssignmenthelp.com

Question: Discuss about the Analysis Business Market of The Ventura Boats. Answer: Introduction: Through the analysis of the Ventura Boats Case study, the reports develops the understanding regarding challenges present in business market. The nature of business of Ventura and review of business process, challenges and opportunities increase the knowledge regarding strength and weakness of the company. The analysis of the threat and opportunities with support frommarketing theories also helps in recommending alternative strategies that Ventura Boats can take in the future to revive their business. SWOT analysis of Ventura Boats: Strength: Ventura Boats mainly deals with designing high quality small-sized luxury boats. However, their specialty is that they build the boats in traditional hand-build ways from traditional materials. Their main strength is that as their business have prospered till now, they have a strong reserve of cash. Strong cash reserve is likely to provide positive implications in the area of long-term strategic growth and future market share. It is also indicative of strong performance of the company till now (Enqvist, Graham and Nikkinen 2014). Weakness: One of the major weak point of Ventura Boat is that despite the availability of newer and efficient technologies in materials and manufacturing processes, the company has still not embraced new technology. The technology used for manufacturing the boat is very outdated and this is the major reason for decline and low demand of many of its product in the Australian market. Secondly, the dependence on traditional methods and materials to build boats is likely to hinder their growth in overseas market too. Opportunity: The possibility of minimum restrictions imposed by overseas government on the use of traditional boats is an advantage for Ventura boats. They can use this opportunity to expand their traditional business in overseas market. This will help them to overcome the challenges of domestic markets. As there is a prediction that Australian government legislation is likely to restrict the use of some of their traditional boats, they can seize the opportunity of fewer restrictions on traditional boats in overseas market. This will help them to expand their business as well as legalized their traditional products for many more years to come. The other advantage of doing business overseas is that the company may offer unique products that may not be available in that region and the product may become a high demand products among new base of customers. This is likely to mitigate threats in business. Secondly, shifting business to overseas might give great exposure to the company which will eventu ally benefit their business (Hohentha et al. 2014). Threats: Considering the decline in many of the traditional boats produced at Ventura Boats, the company could have used their experience in industry to adapt new and efficient technologies to make innovative products. However, this move of Ventura Boats can be threatened by the illegalization of many of their current products. Thus means a decline and loss in business not just for Ventura, but also for all traditional boat manufacturing companies too. The research by Habtay. and Holmn (2014) also suggest that antecedents of disruption can differ based on the type of technology or market driven business model used in firms. Threats and Opportunities Analysis: By conducting the SWOT analysis for Ventura Boats, the main factor that has been identified to be a major threat in its business is the lack of application of advanced and efficient technologies in materials and manufacturing process for boats. This is indeed a great threat for the company because technology can provide both tangible and intangible benefits to business that influences customer demand. If a company remains updated with the use of technology in its services, it can positively influence business operations. If the Company is capable enough to offer new products with the use of advanced manufacturing material and technology, it is likely to facilitate value creation of the firm too (Jaakkola and Alexander 2014). However, dependence on traditional methods and no efforts towards innovation in business process can hinder the cash flow as well as shrinkage of service or products in target market. Ventura Boats have not yet manufactured any new boats with advanced technology and this is likely to affect customer utility and customers demand for product too. This is because product innovation is found to be directly linked with customers demand and engagement with a company. Traditional methods of manufacturing products or service cannot the capture the depth of customer responses and it affects the creation of loyal customers too (Cui and Wu 2016). Customer satisfaction and customer loyalty are two important elements ofmarketing theory and practice. A customers loyalty towards products can be maintained if the company is updated with current demands of customers. This is in relevance with the customers demand theory which states the relationship between customers demand for products and their prices (Jahanshani et al. 2014). Hence, more the effort is paid towards increasing the consumers demand towards a product, the more likely is the opportunities of business growth and prof itability. In the context of Ventura Boats, lack of application of technology is a major disadvantage for them. They need to understand that the capacity for new technology is likely to accelerate economic growth and reach new customers in a new way too. This approach would instead support Ventura Boats to further strengthen their business and find ways by which they could penetrate newer markets by the production of new models. They can also use lesser restrictions of traditional boats in overseas government to sustain competitive advantage in business. This is particularly important for business manager of Ventura Boats because recognizing the appropriate opportunity in business can help them to take more targeted approach towards fulfillment of business goals. Ventura Boast can use the opportunity of lower restriction of traditional boats overseas to boost sale in overseas market (Achtenhagen, Melin and Naldi 2013). Recommendations for strategic alternative for Ventura Boats in the future The dependence on traditional products and little application of advanced manufacturing technology and materials is the major limitation that can affect the business of Ventura Boats in the future. A strategic alternative business options recommended for Venture Boats to save their products form decline in the Australian market is that they must focus on investing on technological infrastructure to contribute to manufacturing innovation. Adaption of new processed in marketing and shifting to advanced methods in producing boats can also give them the advantage of producing high quality goods and minimizing cost expense too. It can also increase their ability to become more competitive by realizing efficient business operations through the use of technology. Focusing on existing technology in boat manufacturing can help them to fulfill unmet needs that earlier led to the decline of the products. Integration of technology in both products and business processed is a step towards competi tiveness and value creation (Adner and Kapoor 2010). Another recommendation for Venture Boats is that they must engage in market analysis of target overseas country to protect their traditional product. In this case too, use of technology will support them to enter untapped market and stay ahead in the competition. The knowledge regarding business practice, cultural factor of target country and demand of specific features in product can help companies to successfully conduct business internationally. However, moving business to international market is always associated with risk and business decisions should be made by considering about culture, legal and regulatory barriers, Government assistance for business and economic feasibility of the new market. This strategy can help Ventura Boats to conquer untapped markets and increase the demand of their products too (Cavusgil et al.2012). Conclusion: The report summarized the strength and weakness present in the Ventura Boats company. The analysis of the weakness and threats present in the company with support from marketing theories and concept has helped in recommending effective alternative strategy for company. Role of technology and tactical moves to conduct business in overseas market can be an effective step for Ventura Boats to shift its business and protect the quality and demand of their boats in domestic as well as international market. Reference Achtenhagen, L., Melin, L. and Naldi, L., 2013. Dynamics of business modelsstrategizing, critical capabilities and activities for sustained value creation.Long range planning,46(6), pp.427-442. Adner, R. and Kapoor, R., 2010. Value creation in innovation ecosystems: How the structure of technological interdependence affects firm performance in new technology generations.Strategic management journal,31(3), pp.306-333. Cavusgil, S.T., Ghauri, P.N. and Akcal, A.A., 2012.Doing business in emerging markets. Sage. Cui, A.S. and Wu, F., 2016. Utilizing customer knowledge in innovation: antecedents and impact of customer involvement on new product performance.Journal of the academy of marketing science,44(4), pp.516-538. Enqvist, J., Graham, M. and Nikkinen, J., 2014. The impact of working capital management on firm profitability in different business cycles: Evidence from Finland.Research in International Business and Finance,32, pp.36-49. Habtay, S.R. and Holmn, M., 2014. Incumbents responses to disruptive business model innovation: The moderating role of technology vs. market-driven innovation.International Journal of Entrepreneurship and Innovation Management 11,18(4), pp.289-309. Hohenthal, J., Johanson, J. and Johanson, M., 2014. Network knowledge and business-relationship value in the foreign market.International Business Review,23(1), pp.4-19. Jaakkola, E. and Alexander, M., 2014. The role of customer engagement behavior in value co-creation: a service system perspective.Journal of Service Research,17(3), pp.247-261. Jahanshani, A.A., Hajizadeh, G.M.A., Mirdhamadi, S.A., Nawaser, K. and Khaksar, S.M.S., 2014. Study the effects of customer service and product quality on customer satisfaction and loyalty.

Eviews Illustrator free essay sample

Windows, Word and Excel are trademarks of Microsoft Corporation. PostScript is a trademark of Adobe Corporation. Professional Organization of English Majors is a trademark of Garrison Keillor. All other product names mentioned in this manual may be trademarks or registered trademarks of their respective companies. Quantitative Micro Software, LLC 4521 Campus Drive, #336, Irvine CA, 92612-2699 Telephone: (949) 856-3368 Fax: (949) 856-2044 web: www. eviews. com First edition: 2007 Second edition: 2009 Editor: Meredith Startz Index: Palmer Publishing Services Chapter 3. Getting the Most from Least Squares Regression is the king of econometric tools. Regression’s job is to find numerical values for theoretical parameters. In the simplest case this means telling us the slope and intercept of a line drawn through two dimensional data. But EViews tells us lots more than just slope and intercept. In this chapter you’ll see how easy it is to get parameter estimates plus a large variety of auxiliary statistics. We begin our exploration of EViews’ regression tool with a quick look back at the NYSE volume data that we first saw in the opening chapter. Then we’ll talk about how to instruct EViews to estimate a regression and how to read the information about each estimated coefficient from the EViews output. In addition to regression coefficients, EViews provides a great deal of summary information about each estimated equation. We’ll walk through these items as well. We take a look at EViews’ features for testing hypotheses about regression coefficients and conclude with a quick look at some of EViews’ most important views of regression results. Regression is a big subject. This chapter focuses on EViews’ most important regression features. We postpone until later chapters various issues, including forecasting (Chapter 8, â€Å"Forecasting†), serial correlation (Chapter 13, â€Å"Serial Correlation—Friend or Foe? †), and heteroskedasticity and nonlinear regression (Chapter 14, â€Å"A Taste of Advanced Estimation†). A First Regression Returning to our earlier examination of trend growth in the volume of stock trades, we start with a scatter diagram of the logarithm of volume plotted against time. EViews has drawn a straight line—a regression line—through the cloud of points plotted with log ( volume ) on the vertical axis and time on the horizontal. The regression line can be written as an algebraic expression: log ( volume t ) = a + bt Using EViews to estimate a regression lets us replace a and b with numbers 62—Chapter 3. Getting the Most from Least Squares based on the data in the workfile. In a bit we’ll see that EViews estimates the regression line to be: log ( volume t ) = – 2. 629649 + 0. 017278t In other words, the intercept a is estimated to be -2. 6 and the slope b is estimated to be 0. 017. Most data points in the scatter plot fall either above or below the regression line. For example, for observation 231 (which happens to be the first quarter of 1938) the actual trading volume was far below the predicted regression line. In other words, the regression line contains errors which aren’t accounted for in the estimated equation. It’s standard to write a regression model to include a term u t to account for these errors. (Econometrics texts sometimes use the Greek letter epsilon, e , rather than u for the error term. ) A complete equation can be written as: log ( volume t ) = a + bt + u t Regression is a statistical procedure. As such, regression analysis takes uncertainty into ? account. Along with an estimated value for each parameter (e. g. , b = 0. 017 ) we get: †¢ Measures of the accuracy of each of the estimated parameters and related information for computing hypothesis tests. †¢ Measures of how well the equation fits the data: How much is explained by the estimated values of a and b and how much remains unexplained. †¢ Diagnostics to check up on whether assumptions underlying the regression model seem satisfied by the data. We’re re-using the data from Chapter 1, â€Å"A Quick Walk Through† to illustrate the features of EViews’ regression procedure. If you want to follow along on the computer, use the workfile â€Å"NYSEVOLUME† as shown. A First Regression—63 EViews allows you to run a regression either by creating an equation object or by typing commands in the command pane. We’ll start with the former approach. Choose the menu command Object/New Object†¦. Pick Equation in the New Object dialog. The empty equation window pops open with space to fill in the variables you want in the regression. Regression equations are easily specified in EViews by a list in which the first variable is the dependent variable—the variable the regression is to explain, followed by a list of explanatory—or independent—variables. Because EViews allows an expression pretty much anywhere a variable is allowed, we can use either variable names or expressions in our regression specification. We want log ( volume ) for our dependent variable and a time trend for our independent variable. Fill out the equation dialog by entering â€Å"log(volume) c @trend†. Hint: EViews tells one item in a list from another by looking for spaces between items. For this reason, spaces generally aren’t allowed inside a single item. If you type: log (volume) c @trend you’ll get an error message. 64—Chapter 3. Getting the Most from Least Squares Exception to the previous hint: When a text string is called for in a command, spaces are allowed inside paired quotes. Reminder: The letter â€Å"C† in a regression specification notifies EViews to estimate an intercept—the parameter we called a above. Hint: Another reminder: @trend is an EViews function to generate a time trend, 0, 1, 2, †¦. Our regression results appear below: The Really Important Regression Results There are 25 pieces of information displayed for this very simple regression. To sort out all the different goodies, we’ll start by showing a couple of ways that the main results might be presented in a scientific paper. Then we’ll discuss the remaining items one number at a time. A favorite scientific convention for reporting the results of a single regression is display the estimated equation inline with standard errors placed below estimated coefficients, looking something like: The Really Important Regression Results—65 log ( volume t ) = – 2. 629649 + 0. 017278 ? t , ser = 0. 967362, R = 0. 852357 ( 0. 89576 ) ( 0. 000334 ) 2 Hint: The dependent variable is also called the left-hand side variable and the independent variables are called the right-hand side variables. That’s because when you write out the regression equation algebraically, as above, convention puts the dependent variable to the left of the equals sign and the independent variabl es to the right. The convention for inline reporting works well for a single equation, but becomes unwieldy when you have more than one equation to report. Results from several related regressions might be displayed in a table, looking something like Table 2. Table 2 (1) Intercept -2. 629649 (0. 089576) 0. 017278 (0. 000334) — (2) -0. 106396 (0. 045666) -0. 000736 (0. 000417) 6. 63E-06 (1. 37E-06) 0. 868273 (0. 022910) 0. 289391 0. 986826 t t 2 log(volume(-1)) ser — 0. 967362 0. 852357 R 2 Column (2)? Don’t worry, we’ll come back to it later. Hint: Good scientific practice is to report only digits that are meaningful when displaying a number. We’ve printed far too many digits in both the inline display and in Table 2 so as to make it easy for you to match up the displayed numbers with the EViews output. From now on we’ll be better behaved. EViews regression output is divided into three panels. The top panel summarizes the input to the regression, the middle panel gives information about each regression coefficient, and the bottom panel provides summary statistics about the whole regression equation. 66—Chapter 3. Getting the Most from Least Squares The most important elements of EViews regression output are the estimated regression coefficients and the statistics associated with each coefficient. We begin by linking up the numbers in the inline display—or equivalently column (1) of Table 2—with the EViews output shown earlier. The names of the independent variables in the regression appear in the first column (labeled â€Å"Variable†) in the EViews output, with the estimated regression coefficients appearing one column over to the right (labeled â€Å"Coefficient†). In econometrics texts, regression coefficients are commonly denoted with a Greek letter such as a or b or, occasionally, with a Roman b . In contrast, EViews presents you with the variable names; for example, â€Å"@TREND† rather than â€Å" b †. The third EViews column, labeled â€Å"Std. Error,† gives the standard error associated with each regression coefficient. In the scientific reporting displays above, we’ve reported the standard error in parentheses directly below the associated coefficient. The standard error is a measure of uncertainty about the true value of the regression coefficient. The standard error of the regression, abbreviated â€Å"ser,† is the estimated standard deviation of the error terms, u t . In the inline display, â€Å"ser=0. 967362† appears to the right of the regression equation proper. EViews labels the ser as â€Å"S. E. of regression,† reporting its value in the left column in the lower summary block. Note that the third column of EViews regression output reports the standard error of the estimated coefficients while the summary block below reports the standard error of the regression. Don’t confuse the two. The final statistic in our scientific display is R . R measures the overall fit of the regression line, in the sense of measuring how close the points are to the estimated regression line 2 in the scatter plot. EViews computes R as the fraction of the variance of the dependent variable explained by the regression. (See the User’s Guide for the precise definition. 2 2 Loosely, R = 1 means the regression fit the data perfectly and R = 0 means the regression is no better than guessing the sample mean. Hint: EViews will report a negative R for a model which fits worse than a model consisting only of the sample mean. 2 2 2 The Pretty Important (But Not So Important As the Last Section’s) Regression Results We’re usually most interested in the regression coefficients and the statistical information provided for each one, so let’s continue along with the middle panel. The Pretty Important (But Not So Important As the Last Section’s) Regression Results—67 -Tests and Stuff All the stuff about individual coefficients is reported in the middle panel, a copy of which we’ve yanked out to examine on its own. The column headed â€Å"t-Statistic† reports, not surprisingly, the t-statistic. Specifically, this is the t-statistic for the hypothesis that the coefficient in the same row equals zero. (It’s computed as the ratio of the estimated coefficient to its standard error: e. g. , 51. 7 = 0. 017  § 0. 00033 . ) Given that there are many potentially interesting hypotheses, why does EViews devote an entire column to testing that specific coefficients equal zero? The hypothesis that a coefficient equals zero is special, because if the coefficient does equal zero then the attached coefficient drops out of the equation. In other words, log ( volume t ) = a + 0 ? t + u t is really the same as log ( volume t ) = a + u t , with the time trend not mattering at all. Foreshadowing hint: EViews automatically computes the test statistic against the hypothesis that a coefficient equals zero. We’ll get to testing other coefficients in a minute, but if you want to leap ahead, look at the equation window menu View/Coefficient Tests†¦. If the t-statistic reported in column four is larger than the critical value you choose for the test, the estimated coefficient is said to be â€Å"statistically significant. † The critical value you pick depends primarily on the risk you’re willing to take of mistakenly rejecting the null hypothesis (the technical term is the â€Å"size† of the test), and secondarily on the degrees of freedom for the test. The larger the risk you’re willing to take, the smaller the critical value, and the more likely you are to find the coefficient â€Å"significant. † Hint: EViews doesn’t compute the degrees of freedom for you. That’s probably because the computation is so easy it’s not worth using scarce screen real estate. Degrees of freedom equals the number of observations (reported in the top panel on the output screen) less the number of parameters estimated (the number of rows in the middle panel). In our example, df = 465 – 2 = 463 . The textbook approach to hypothesis testing proceeds thusly: 1. Pick a size (the probability of mistakenly rejecting), say five percent. 2. Look up the critical value in a t-table for the specified size and degrees of freedom. 68—Chapter 3. Getting the Most from Least Squares . Compare the critical value to the t-statistic reported in column four. Find the variable to be â€Å"significant† if the t-statistic is greater than the critical value. EViews lets you turn the process inside out by using the â€Å"p-value† reported in the right-most column, under the heading â€Å"Prob. † EViews has worked the problem backwards an d figured out what size would give you a critical value that would just match the t-statistic reported in column three. So if you are interested in a five percent test, you can reject if and only if the reported p-value is less than 0. 05. Since the p-value is zero in our example, we’d reject the hypothesis of no trend at any size you’d like. Obviously, that last sentence can’t be literally true. EViews only reports p-values to four decimal places because no one ever cares about smaller probabilities. The p-value isn’t literally 0. 0000, but it’s close enough for all practical purposes. Hint: t-statistics and p-values are different ways of looking at the same issue. A t-statistic of 2 corresponds (approximately) to a p-value of 0. 05. In the old days you’d make the translation by looking at a â€Å"t-table† in the back of a statistics book. EViews just saves you some trouble by giving both t- and p-. Not-really-about-EViews-digression: Saying a coefficient is â€Å"significant† means there is statistical evidence that the coefficient differs from zero. That’s not the same as saying the coefficient is â€Å"large† or that the variable is â€Å"important. † â€Å"Large† and â€Å"important† depend on the substantive issue you’re working on, not on statistics. For example, our estimate is that NYSE volume rises about one and one-half percent each quarter. We’re very sure that the increase differs from zero—a statement about statistical significance, not importance. Consider two different views about what’s â€Å"large. † If you were planning a quarter ahead, it’s hard to imagine that you need to worry about a change as small as one and one-half percent. On the other hand, one and one-half percent per quarter starts to add up over time. The estimated coefficient predicts volume will double each decade, so the estimated increase is certainly large enough to be important for long-run planning. More Practical Advice On Reporting Results Now you know the principles of how to read EViews’ output in order to test whether a coefficient equals zero. Let’s be less coy about common practice. When the p-value is under 0. 05, econometricians say the variable is â€Å"significant† and when it’s above 0. 05 they say it’s â€Å"insignificant. † (Sometimes a variable with a p-value between 0. 10 and 0. 05 is said to be â€Å"weakly significant† and one with a p-value less than 0. 01 is â€Å"strongly significant. †) This practice may or may not be wise, but wise or not it’s what most people do. The Pretty Important (But Not So Important As the Last Section’s) Regression Results—69 We talked above about scientific conventions for reporting results and showed how to report results both inline and in a display table. In both cases standard errors appear in parentheses below the associated coefficient estimates. â€Å"Standard errors in parentheses† is really the first of two-and-a-half reporting conventions used in the statistical literature. The second convention places the t-statistics in the parentheses instead of standard errors. For example, we could have reported the results from EViews inline as log ( volume t ) = – 2. 629649 + 0. 017278 ? t , ser = 0. 967362, R = 0. 852357 ( – 29. 35656 ) ( 51. 70045 ) 2 Both conventions are in wide use. There’s no way for the reader to know which one you’re using—so you have to tell them. Include a comment or footnote: â€Å"Standard errors in parentheses† or â€Å"t-statistics in parentheses. † Fifty percent of economists report standard errors and fifty percent report t-statistics. The remainder report p-values, which is the final convention you’ll want to know about. Where Did This Output Come From Again? The top panel of regression output, shown on the right, summarizes the setting for the regression. The last line, â€Å"Included observations,† is obviously useful. It tells you how much data you have! And the next to last line identifies the sample to remind you which observations you’re using. Hint: EViews automatically excludes all observations in which any variable in the specification is NA (not available). The technical term for this exclusion rule is â€Å"listwise deletion. † 70—Chapter 3. Getting the Most from Least Squares Big (Digression) Hint: Automatic exclusion of NA observations can sometimes have surprising side effects. We’ll use the data abstract at the right as an example. Data are missing from observation 2 for X1 and from observation 3 for X2. A regression of Y on X1 would use observations 1, 3, 4, and 5. A regression of Y on X2 would use observations 1, 2, 4, and 5. A regression of Y on both X1 and X2 would use observations 1, 4, and 5. Notice that the fifth observation on Y is zero, which is perfectly valid, but that the fifth observation on log(Y) is NA. Since the logarithm of zero is undefined EViews inserts NA whenever it’s asked to take the log of zero. A regression of log(Y) on both X1 and X2 would use only observations 1 and 4. The variable, X1(-1), giving the previous period’s values of X1, is missing both the first and third observation. The first value of X1(-1) is NA because the data from the observation before observation 1 doesn’t exist. (There is no observation before the first one, eh? The third observation is NA because it’s the second observation for X1, and that one is NA. So while a regression of Y on X1 would use observations 1, 3, 4, and 5, a regression of Y on X1(-1) would use observations 2, 4, and 5. Moral: When there’s missing data, changing the variables specified in a regression can a lso inadvertently change the sample. What’s the use of the top three lines? It’s nice to know the date and time, but EViews is rather ungainly to use as a wristwatch. More seriously, the top three lines are there so that when you look at the output you can remember what you were doing. Dependent Variable† just reminds you what the regression was explaining— LOG(VOLUME) in this case. â€Å"Method† reminds us which statistical procedure produced the output. EViews has dozens of statistical procedures built-in. The default procedure for estimating the parameters of an equation is â€Å"least squares. † The Pretty Important (But Not So Important As the Last Section’s) Regression Results—71 The third line just reports the date and time EViews estimated the regression. It’s surprising how handy that information can be a couple of months into a project, when you’ve forgotten in what order you were doing things. Since we’re talking about looking at output at a later date, this is a good time to digress on ways to save output for later. You can: †¢ Hit the button to save the equation in the workfile. The equation will appear in the workfile window marked with the icon. Then save the workfile. Hint: Before saving the file, switch to the equation’s label view and write a note to remind yourself why you’re using this equation. †¢ Hit the button. †¢ Spend output to a Rich Text Format (RTF) file, which can then be read directly by most word processors. Select Redirect: in the Print dialog and enter a file name in the Filename: field. As shown, you’ll end up with results stored in the file â€Å"some results. rtf†. †¢ Right-click and choose Select non-empty cells, or hit Ctrl-A— it’s the same thing. Copy and then paste into a word processor. Freeze it If you have output that you want to make sure won’t ever change, even if you change the equation specification, hit . Freezing the equation makes a copy of the current view in the form of a table which is detached from the equation object. (The original equation is unaffected. ) You can then this frozen table so that it will be saved in the workfile. See Chapter 17, â€Å"Odds and Ends. † 72—Chapter 3. Getting the Most from Least Squares Summary Regression Statistics The bottom panel of the regression provides 12 summary statistics about the regression. We’ll go over these statistics briefly, but leave technical details to your favorite econometrics text or the User’s Guide. We’ve already talked about the two most important numbers, â€Å"R-squared† and â€Å"S. E. of regression. † Our regression accounts for 85 percent of the variance in the dependent variable and the estimated standard deviation of the error term is 0. 97. Five other elements, â€Å"Sum squared residuals,† â€Å"Log likelihood,† â€Å"Akaike info criterion,† â€Å"Schwarz criterion,† and â€Å"Hannan-Quinn criter. † are used for making statistical comparisons between two different regressions. This means that they don’t really help us learn anything about the regression we’re working on; rather, these statistics are useful for deciding if one model is better than another. For the record, the sum of squared residuals is used in computing F-tests, the log likelihood is used for computing likelihood ratio tests, and the Akaike and Schwarz criteria are used in Bayesian model comparison. The next two numbers, â€Å"Mean dependent var† and â€Å"S. D. dependent var,† report the sample mean and standard deviation of the left hand side variable. These are the same numbers you’d get by asking for descriptive statistics on the left hand side variables, so long as you were using the sample used in the regression. (Remember: EViews will drop observations from the estimation sample if any of the left-hand side or right-hand side variables are NA— i. e. , missing. ) The standard deviation of the dependent variable is much larger than the standard error of the regression, so our regression has explained most of the variance in og(volume)—which is exactly the story we got from looking at the R-squared. Why use valuable screen space on numbers you could get elsewhere? Primarily as a safety check. A quick glance at the mean of the dependent variable guards against forgetting that you changed the units of measurement or that the sample used is so mehow different from what you were expecting. â€Å"Adjusted R-squared† makes an adjustment to the plain-old R to take account of the num2 ber of right hand side variables in the regression. R measures what fraction of the variation in the left hand side variable is explained by the regression. When you add another 2 right hand side variable to a regression, R always rises. (This is a numerical property of 2 2 least squares. ) The adjusted R , sometimes written R , subtracts a small penalty for each additional variable added. â€Å"F-statistic† and â€Å"Prob(F-statistic)† come as a pair and are used to test the hypothesis that none of the explanatory variables actually explain anything. Put more formally, the â€Å"F-sta2 A Multiple Regression Is Simple Too—73 tistic† computes the standard F-test of the joint hypothesis that all the coefficients, except the intercept, equal zero. Prob(F-statistic)† displays the p-value corresponding to the reported F-statistic. In this example, there is essentially no chance at all that the coefficients of the right-hand side variables all equal zero. Parallel construction notice: The fourth and fifth columns in EViews regression output report the t-statistic and corresponding p-value for the hypothesis th at the individual coefficient in the row equals zero. The F-statistic in the summary area is doing exactly the same test for all the coefficients (except the intercept) together. This example has only one such coefficient, so the t-statistic and the F-statistic test exactly the same hypothesis. Not coincidentally, the reported p-values are identical 2 and the F- is exactly the square of the t-, 2672 = 51. 7 . Our final summary statistic is the â€Å"Durbin-Watson,† the classic test statistic for serial correlation. A Durbin-Watson close to 2. 0 is consistent with no serial correlation, while a number closer to 0 means there probably is serial correlation. The â€Å"DW,† as the statistic is known, of 0. 095 in this example is a very strong indicator of serial correlation. EViews has extensive facilities both for testing for the presence of serial correlation and for correcting regressions when serial correlation exists. We’ll look at the Durbin-Watson, as well as other tests for serial correlation and correction methods, later in the book. (See Chapter 13, â€Å"Serial Correlation—Friend or Foe? †). A Multiple Regression Is Simple Too Traditionally, when teaching about regression, the simple regression is introduced first and then â€Å"multiple regression† is presented as a more advanced and more complicated technique. A simple regression uses an intercept and one explanatory variable on the right to explain the dependent variable. A multiple regression uses one or more explanatory variables. So a simple regression is just a special case of a multiple regression. In learning about a simple regression in this chapter you’ve learned all there is to know about multiple regression too. Well, almost. The main addition with a multiple regression is that there are added right hand-side variables and therefore added rows of coefficients, standard errors, etc. The model we’ve used so far explains the log of NYSE volume as a linear function of time. Let’s add two more variables, time-squared and lagged log(volume), hoping that time and timesquared will improve our ability to match the long-run trend and that lagged values of the dependent variable will help out with the short run. In the last example, we entered the specification in the Equation Estimation dialog. I find it much easier to type the regression command directly into the command pane, although the 74—Chapter 3. Getting the Most from Least Squares method you use is strictly a matter of taste. The regression command is ls followed by the dependent variable, followed by a list of independent variables (using the special symbol â€Å"C† to signal EViews to include an intercept. ) In this case, type: ls log(volume) c @trend @trend^2 log(volume(-1)) and EViews brings up the multiple regression output shown to the right. You already knew some of the numbers in this regression because they appeared in the second column in Table 1 on page 65. When you specify a multiple regression, EViews gives one row in the output for each independent variable. Hint: Most regression specifications include an intercept. Be sure to include â€Å"C† in the list of independent variables unless you’re sure you don’t want an intercept. Hint: Did you notice that EViews reports one fewer observation in this regression than in the last, and that EViews changed the first date in the sample from the first to the second quarter of 1888? This is because the first data we can use for lagged volume, from second quarter 1888, is the (non-lagged) volume value from the first quarter. We can’t compute lagged volume in the first quarter because that would require data from the last quarter of 1887, which is before the beginning of our workfile range. Hypothesis Testing We’ve already seen how to test that a single coefficient equals zero. Just use the reported tstatistic. For example, the t-statistic for lagged log(volume) is 37. 89 with 460 degrees of freedom (464 observations minus 4 estimated coefficients). With EViews it’s nearly as easy to test much more complex hypotheses. Hypothesis Testing—75 Click the button and choose Coefficient Diagnostics/Wald – Coefficient Restrictions†¦ to bring up the dialog shown to the right. In order to whip the Wald Test dialog into shape you need to know three things: †¢ EViews names coefficients C(1), C(2), C(3), etc. numbering them in the order they appear in the regression. As an example, the coefficient on LOG(VOLUME(-1)) is C(4). †¢ You specify a hypothesis as an equation restricting the values of the coefficients in the regression. To test that the coefficient on LOG(VOLUME(-1)) equals zero, specify â€Å"C(4)=0†. †¢ If a hypothesis involves multiple restrictions, you enter multiple coefficient equations separated by commas. Let’s work through some examples, starting with the one we already know the answer to: Is the coefficient on LOG(VOLUME(-1)) significantly different from zero? Hint: We know the results of this test already, because EViews computed the appropriate test statistic for us in its standard regression output. 76—Chapter 3. Getting the Most from Least Squares Complete the Wald Test dialog with C(4)=0. EViews gives the test results as shown to the right. EViews always reports an F-statistic since the F- applies for both single and multiple restrictions. In cases with a single restriction, EViews will also show the t-statistic. Hint: The p-value reported by EViews is computed for a two-tailed test. If you’re interested in a one-tailed test, you’ll have to look up the critical value for yourself. Suppose we wanted to test whether the coefficient on LOG(VOLUME(-1)) equaled one rather than zero. Enter â€Å"c(4)=1† to find the new test statistic. So this hypothesis is also easily rejected. Hypothesis Testing—77 Econometric theory warning: If you’ve studied the advanced topic in econometric theory called the â€Å"unit root problem† you know that standard theory doesn’t apply in this test (although the issue is harmless for this particular set of data). Take this as a reminder that you and EViews are a team, but you’re the brains of the outfit. EViews will obediently do as it’s told. It’s up to you to choose the proper procedure. EViews is happy to test a hypothesis involving multiple coefficients and nonlinear restrictions. To test that the sum of the first two coefficients equals the product of the sines of the second two coefficients (and to emphasize that EViews is perfectly happy to test a hypothesis that is complete nonsense) enter â€Å"c(1)+c(2)=sin(c(3))+sin(c(4))†. Not only is the hypothesis nonsense, apparently it’s not true. 78—Chapter 3. Getting the Most from Least Squares A good example of a hypothesis involving multiple restrictions is the hypothesis that there is no time trend, so the coefficients on 2 both t and t equal zero. Here’s the Wald Test view after entering â€Å"c(2)=0, c(3)=0†. The hypothesis is rejected. Note that EViews correctly reports 2 degrees of freedom for the test statistic. Representing The Representations view, shown at the right, doesn’t tell you anything you don’t already know, but it provides useful reminders of the command used to generate the regression, the interpretation of the coefficient labels C(1), C(2), etc. and the form of the equation written out with the estimated coefficients. Hint: Okay, okay. Maybe you didn’t really need the representations view as a reminder. The real value of this view is that you can copy the equation from this view and then paste it into your word processor, or into an EViews batch program, or even into Excel, where wi th a little judicious editing you can turn the equation into an Excel formula. What’s Left After You’ve Gotten the Most Out of Least Squares Our regression equation does a pretty good job of explaining log(volume), but the explanation isn’t perfect. What remains—the difference between the left-hand side variable and the value predicted by the right-hand side—is called the residual. EViews provides several tools to examine and use the residuals. What’s Left After You’ve Gotten the Most Out of Least Squares—79 Peeking at the Residuals The View Actual, Fitted, Residual provides several different ways to look at the residuals. Usually the best view to look at first is Actual, Fitted, Residual/Actual, Fitted, Residual Graph as illustrated by the graph shown here. Three series are displayed. The residuals are plotted against the left vertical axis and both the actual (log(volume)) and fitted (predicted log(volume)) series are plotted against the vertical axis on the right. As it happens, because our fit is quite good and because we have so many observations, the fitted values nearly cover up the actual values on the graph. But from the residuals it’s easy to see two facts: our model fits better in the later part of the sample than in the earlier years—the residuals become smaller in absolute value—and there are a very small number of data points for which the fit is really terrible. 80—Chapter 3. Getting the Most from Least Squares Points with really big positive or negative residuals are called outliers. In the plot to the right we see a small number of spikes which are much, much larger than the typical residual. We can get a close up on the residuals by choosing Actual, Fitted, Residual/Residual Graph. It might be interesting to look more carefully at specific numbers. Choose Actual, Fitted, Residual/Actual, Fitted, Residual Table for a look that includes numerical values. You can see enormous residuals in the second quarter for 1933. The actual value looks out of line with the surrounding values. Perhaps this was a really unusual quarter on the NYSE, or maybe someone even wrote down the wrong numbers when putting the data together! Grabbing the Residuals Since there is one residual for each observation, you might want to put the residuals in a series for later analysis. Fine. All done. Without you doing anything, EViews stuffs the residuals into the special series each estimation. You can use RESID just like any other series. after Quick Review—81 Resid Hint 1: That was a very slight fib. EViews won’t let you include RESID as a series in an estimation command because the act of estimation changes the values stored in RESID. Resid Hint 2: EViews replaces the values in RESID with new residuals after each estimation. If you want to keep a set, copy them into a new series as in: series rememberresids = resid before estimating anything else. Resid Hint 3: You can store the residuals from an equation in a series with any name you like by using Proc/Make Residual Series†¦ from the equation window. Quick Review To estimate a multiple regression, use the ls command followed first by the dependent variable and then by a list of independent variables. An equation window opens with estimated coefficients, information about the uncertainty attached to each estimate, and a set of summary statistics for the regression as a whole. Various other views make it easy to work with the residuals and to test hypotheses about the estimated coefficients. In later chapters we turn to more advanced uses of least squares. Nonlinear estimation is covered, as are methods of dealing with serial correlation. And, predictably, we’ll spend some time talking about forecasting. 82—Chapter 3. Getting the Most from Least Squares