Definition: Eco Tourism And Mass Tourism

Definition: Eco Tourism And Mass Tourism Eco-Tourism is a combination of two words Ecosystem and Tourism, together it is made Eco-tourism. Ecosystem is the system where everybody live, this system is compuse by the water, earth, sky and the living and not living objects such as micro-organism communities, plant, animal and their non living environment acting as a functional unit. Some examples to refer of ecosystem may be the river, the ocean, the jungle, a forest or a biome. And tourism means, the practice of travelling for pleasure. Therefore, a tourism which implicates a visit to an Ecosystem is well known as Eco-tourism. Thus, Eco-tourism can be defined as Tourism involving travel to areas of natural or ecological interest, mostly of the time under the guidance of a naturalist, having by objective learning about the environment making focus on wildlife awareness and conservation of the environment. However, Ecotourism still a new topic nowadays, the most acceptance definition is by Ceballos-Lascurain, who is generally a ccepted as the first person to define ecotourism Tourism that involves travelling to relatively undisturbed or uncontaminated areas with the specific objective of studying, admiring, and enjoying the scenery and its wild plants and animals, as well as any existing cultural manifestations found in these areas (Ceballos-Lascurain, 1991, P. 25). In addition, Ecotourism is a sustainable form of natural resources-based tourism that focuses primarily on experiencing and learning about nature, and which is ethically managed to be low-impact, non-consumptive, and locally oriented (control, benefits and scale). It typically occurs in natural areas, and should contribute to the conservation of such areas (Fennell, 1999, p 43). Fennell identified 13 main principles of ecotourism: interest in nature contributes to conservation reliance on parks and protected areas benefits local people long tern benefits Education and studies Low impact / non -consumptive ethics / responsibility management sustainable enjoyment and appreciation culture adventure and small scale In today world it is very easy to confuse Eco-tourism with mass tourism, but mass tourism is related to the number of people that visit a destination and most of the time those big groups are not eco-tourism because their only reason to visit those natural destinations is just for pleasure. They do not plan vacations to learn about the environment, know the culture of the destination and the most of the time they just expend the entire stayed in five start resort enjoying its facilities and they do not even has contact with any single natural attraction that the visit destination offer. 1.2 Importance of sustainable tourism Sustainable tourism has become essential for the tourism industy due to it is closely related to eco-tourism. Eco tourism is considered as the tourism development which try to capture a portion of tourism market that is attracted to natural beauties by providing low impact tourism products. Sustainable tourism would provide direct benefits to the local people. The private sector would assure least impact on the environment. Tourism projects will be initiated considering the size and scope of the land character with limited resorts located close to existing infrastructure offering economic benefits to local communities. It also includes development of preferential tourism zones committed to sustainable development and carrying out specific practices (Rauschelbach et al, 2002, pp: 229). 1.3 Benefits of Eco-Tourism practice The importance of the Eco-tourism and the benefits that this activity provides is indispensable for the sustainability of the natural destination. Eco-tourism is the only kind of tourism that provides awareness to the visitors as well the local population involving then in activities to preserve the destination, understanding the environmental issues and explain the consequences before they take place. Eco-tourism trainee the local populations in order to visualize the visitors as an alternative of income that will contribute to the destination survive. With significant economic benefits the local population needs to see their natural area as a source of income, conserving their local destination avoiding activities like cutting down all their rainforests that just contribute to the global warning. Tourism is the world ´s largest industry; in 1950 the number of tourist went from 25 million up to 702 million in 2000. Acoording to the World Tourism Organization for this 2010 the tourism growth rate will reach 1 billion and 1.6 billion in 2020. With the growth of science and technology, abundance in economy and revolutionary changes that have happened in the field of transportation have contributed largely to the development of the tourist trade all over the world. In todays world an individual can have breakfast in London, lunch in New York and dinner in Tokyo (Cooper and Hall, 2008, pp: 377). 2.0 Main body 2.1 Presentation of Cabo San Lucas as Eco-Tourism destination Cabo San Lucas has become an important vacation and spa destination located at the tip of the Baja peninsula in Baja California Sur, Mexico. This destination is well known for its natural attractions such as the sea of Cortez where the tourist can enjoy of several activities like whale watching, snorkel, kayak, scuba diving, parasailing, tours to the arch, and banana boat. The colorful desert with unique plant life is a natural curiosity in los Cabos, where hiking, ATV and horseback trip explore the area. The sea of Cortez is nationally and internationally recognized as ecosystem of significant biological, social and economic value. The Mexican government, who recognized the importance of the island, establishes then as natural protects area in 1978, under the category of flora and fauna protection area. Internationally the island is known as biosphere reserve by the UNESCO. Because of the beauty and natural of the destination and surrounding marine areas, visitors have the possibili ty to learn from this ecosystem. 2.2 Cabo San Lucas emerging issues For Cabo San Lucas still been a challenge the duty of educated local people and tourism to preserve the destination. One of the biggest obstacles to develop eco-tourism in Cabo San Lucas is the number of interest from the public and private serctor, especially in this destination that contain a vast American influence. For example, for some Tour Operators is very easy to pretend that they are bringing to the destination Eco-tourism but behind this, is just mass tourism. At this stage the destination is facing several emerging issues such as: The overfishing hurting of hundreds species including the Marlin and Tuna. The local population kill its flora and fauna to made local souvenir which are comercialize with the tourism. The local population is relative poor and is lack in knowledge and education, therefore they do not contribute to the conservation of the destination, as a method of income they operate tours in the Cortezs sea (using pangas, local wood boats), without respect the navigation rules such as the distance that they have to take in a while watching tour as well as the proximity to the sea lion colony. International investment in construction like Riu Hotels (Spanish company) which in 2007 built the Riu Sta. Fe with 1100 rooms and still operate as normal avoiding the FONATURs regulation rules ( National Fund for Tourism Development) . To clarify this example, in Cabo San Lucas is allowed hotels construction up to 900 rooms mention by FONATUR. Due to airline incresing price most of the tourist are opting to visit the destination by cruiseship which contribute to the massive desturbe of the fauna and pollution of the water. The mass tourism is overcrowding the destination through overbooking the accomodation and service facilities creating chaos in public areas such as bus transportation, beaches, parks and hospitals. The destination waste disposal is increasing due to the mass tourism. 2.3 Eco-Tourism strategies implement by Cabo San Lucas At this stage, the SEMARNAT (Secretary of Environment and Natural Resources) together with the CONANP (National Commission of Natural Protected) are implementing in the destination two eco-tourism strategies: A code of ethics for visitors and tourism operators, that have been implemented to complement the legal requirements in the destination having by objective the reduction of the emergencies issues mention before. This code of ethics is basically focus on tourism operators behaviors forcing them to: maintain the natural integrity of the places visit; respect the livehoods and culture of the local people. make a solid effort to be less wasteful with the natural resource. ensure waste disposal has not environmental impact. develop a recycling programs; support different companies in the hospitality and tourism industry that work under environmental policies. increase the network with other tours company in special those in the local area to keep aware of new conservation programs, environmental policies and eco-friendly techniques. get the appropriate education and training on Eco-tourism and low impact techniques such as respectful for the environment and local culture. support local economy but do not buy goods made from threatened or endangered species such as turtles; never disturb the wildlife and wildlife habitats. follow by the book the rules and regulations in protect areas; advertise truthfully and inform to the tourist about the natural and local history providing them useful materials. make sure that the clients that are coming are aware of the regulations, norms and code of ethic applicable in the visit destination and develop an environmental education program with the local communities. A guide for environmental tourism practice that refers to how the destination regularized activities related to bird watching, sea lions, diving and snorkeling, recreational booting etiquette, whales and dolphins, turtles and sport fishing. Some of those environmental tourism practices are summarizing here: obtaining permission from SEMART through the direction of flora and fauna protection area Islands of the Gulf of California before visit the island. forgibbean feed native animals. camp only on designated area. use only biodegradable products. is highly recommend visit the exotic fauna colonies with guide. watch the fauna from distance avoiding disturb or stress them. never attempt to touch the fauna, the boat have to be 90 feet away from the rocks and islets. avoid making loud noises and using strong lights near the colonies. it is forbidden by law to damage or remove coral or other marine organisms as well collect natural souvenirs like coral, shells, etc. do not interfere with the natural movements of the fauna. before fish make sure to have the permit through the local SAGARA office that provides information on official bag/size/ tackle limits, protected species and seasonal closures. As example of this successful Eco-tourism strategies, Olympus Tours one of the biggest tour operators from USA which is bringing to the destination more than 33.000 tourist year over year yearly, is totally agree with the procedures mentioned before and as prove of this in carryon the following iniciatives: Increasing employment opportunities for local residentes. Support and promote the local culture and customs. Contribiute to prevent the exploitation of children in tourism. Foment activities where the tourist interact with the local people. Operate safe trips for the natural envaironment and the tourism. Support local communities and organization. Work with suppliers that share the same social responsibility. Avoid activities which exploit flora and fauna such as diving with dolphins. Reduce energy consumption. Recycle as much as possible. Agrements with establishments and hotels that implement eco-friendly techniques and green practices. Another success example of the eco-tourism strategies in the destination are related to the hotels and resorts which are implementing several eco-friendly and green techniques such as: Intercontinental chain that asks to its customers to conserve water by reusing towels and limiting the number of times the linens are changed. Barcelà ³ chain is adopting many local plants that thrive in arid Baja in its landscaping an stead of grass that require daily irrigation. Nh-hotels has introduced for its customers the leastest technology in room functions, controlled by LCD where the air condition turns off when the patio door is open. Riu Hotels has finished its remodeling project replacing the baths with walking showers. Hilton Los Cabos is changing its wedding concept, offering to its customers a new phenomenon called green wedding. This package offers to brides and grooms a lot of alternatives adding green elements into their destination wedding. Based on the nature of the destination and the natural beauty of the surrounding environment, couples can choose an outdoor wedding area offered by the Hilton Los Cabos like lagoons, seaside gazebos, white sand beach, therefore reducing their energy used by having the wedding during the day. Dining service will include organic items such as organic tequila, domestic wines and organic cakes. End the celebration with eco-friendly fireworks that produce less smoke than the regulars and reduce the number of toxic metals that commonly left in the water. 3.0 Conclusion 3.1 Summary the importance of Eco-Tourism in destinations The Eco-tourism presence in natural destinations is becaming more and more importante for the sustainability of the destination. Eco-tourism main function is develop awarenesses between local people and tourist regarding the importance of the preservation and conservation of the natural environment as well local culture. As was discused above, Cabo San Lucas with the impact of the Eco-tourism is becaming as a green destination with several Eco-friendly iniciatives form the hospitality supplieres such as hotels and tour operators. Customer are willing to pay more for accommodation that promote green activities, and Hilton hotel can not be a better example of this transformation, which is offering to its clients green weddings introducing to the market a vast of green items such as out door facilities, organic dinner, organic cake and low smoke fireworks. All those elements have by objective the reduction of the negative impact in the destination. Another positive impac that eco-tourism bring to the natural destination is related to the economic aspect.Cabo San Luas has been growing rapidly in the past 10 years with mega constructions projects such as resorts, shoping malls, hospitals, speedway, international airport amplification that drive to the destination the international brand business such as Wallmart, Mc. Donalds, Hooters, burguer King and starbucks which increase and improve the laboral condition with several available positions. As was mentioned before, the destination has a significant international influx form Americans and Canadeans and it is semeed through the biggest tour operatior that bring tourism to the destination like is Apple Vacations, Olympus tours, The Mark Travel Corporation, Thomas Cook among others which are incentive and promote in Cabo San Lucas the Eco-tourism as an alternative way of travel. Today, there are Green Laws of preservation, which are making people aware of how humans and the environment can cohexist beneficially for more time to come and Eco-tourism is one way to maximize the environmental and social benefits of tourism, without exclude the economic developments having by objective the sustainability of the culture and environment. 3.2Recommendation to overcome emerging issues in Cabo San Lucas The Eco-tourism strategies that Cabo San Lucas is applying to contribuite with the sustainability of the destination are a code of ethics for visitors and tourism operators and a guide for environmental tourism practice which ones had been showed evidence that are working very well. However there is opportunity to improve, there are many things that have gone unobserved by the autorities. There is a need for altering the landowners act as it has caused many conflicts. The local landowners should be guaranteed with the insurance policies so that they can support the tourism activities that help in sustaining the resources. Department dealing with allotting areas and granting permission should frame uniform policies so that recreational providers dont get confused while asking for permission. It is believed that most of the tourists dont respect the local culture and also the local people. Tourists should be told to respect the local crowd and local culture. There is need for proper management in Cabo San Lucas and also the tourists should be allowed only during the certain seasons for activities like while watching and other watersports. Authorities need to work and implement aggressive marketing policies for the the destination as well foment activities where the new local generation get involve with the culture and preserve the sustainability of this one. 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Impact of Foreign Maids on Young Children

Project work Preliminary idea draft 3 -Qian Mengyuan 13s210 Choice of topic: access The problem: too much access to foreign maids Nowadays, due to the fast-paced work life, parents don’t have enough time to take care of their own children, hence they employ foreign maids to help them look after their children and manage household chores. As there is easy access [1]of maid service, there will be some impact (table 1) on the young children. Hence this project aims to reduce the negative impact and improve the quality of maids. Impact | |(+) | |Reduce the burden of parents | |Parents can be focused during work. | |Young children will not be lonely when parents are working. | |Enough infant education. | |(-) | |Children become lazy & cannot grow up independently. | |Maids are Unwell-trained-influence young children with bad habits e. g. azy, smoking. | |Very dangerous. Children may be hurt by the maids[2]. | |Poor relationships between parents and their children. | |Over-reliance of maids[3], | |â€Å" lazy society†[4] | Table1: the impact of easy excess maid service on young children and their parents. |Stakeholders |Why they ooperate | |? Parents who are too busy to look after their children |Safer and better environment for children to grow up. | |? the agencies[5] of foreign maids |Better quality will attract more customers. | |? students who study advertising &public relations in Temasek |Improve their advertising and publicity skills. | |polytechnic | | |? eighborhood committee of |Its their duty to ensure better life in the community. | In doing so, the collaboration will minimize the harmful impact as mentioned above. Proposals |Highlight plan: parents awareness day |Action plan: training plan | |Organization involved: |Organization involved: | |? +? |? | |Target audience: |Target audience: | |? Foreign maid who will be employed to take care of children. | |Objectives: | | |To raise the awareness of parents about their care of children. |Details of plan: | |Details of plan: |-connect with the maids agencies to train them by giving them | |- collect information of family with maids from the |certain lessons and conduct examinations after the lesson. |neighborhood committee |-after passing the exam they can then sign a guarantee. | |- let the children to write cards, make small gift and make |-certain amount of fine will be asked by the agency if maids | |videos -what they want to say to parents (done by ? ) to show |are complained. | |their need of parents. |-therefore maids will be more careful. | |- organize a â€Å"i want to say† session between parents and |-teach the maids how to help the children to grow up | |children to allow parents to know more about their children. independently by teaching them do simple housework with prize, | |- The committee educates parents about the importance of taking|but not do everything for them. | |more care of children. | | |-date:1/6/2013 | | |-place: meridian pri mary school(rent a room) | | Rationale: (+): -children will grow up independently, happily and learn to care about others | |the quality of maids will be ensured> more requirement | |the safety and good environment of young children will be ensured | |- Closer relationship between children and parents. | |(-):-the price for employing a maid will be increased because of the training. | [pic] Figure 1. [pic]Figure2 the â€Å"I want to say†¦Ã¢â‚¬ card for children to write down what they want to say to their parents. [pic] The paper heart shape made by children to show their love and need to their parents. [pic] Figure 3 Word Count: 496 [Excluding titles, headings/sub-headings (underlined) and footnotes] ———————– [1] Figure 1 [2] Malaysia – Indonesian maid hurling baby onto floor. http://videocombo. com/video. php? v=678&error=access_denied&error_code=200&error_description=Permissions+error&error_reason=user_denied &state#_=_ [3] Figure 2. A Singapore soldier’s shame? His maid carries his rucksack. ttp://translate. google. com. sg/translate? hl=zh-CN&sl=en&tl=zh-CN&u=http%3A%2F%2Fwww. globalpost. com%2Fdispatches%2Fglobalpost-blogs%2Fthe-rice-bowl%2Fsingapore-soldiers-shame-his-maid-carries-his-rucksack&anno=2 [4] But go-getter Singapore appears particularly reliant on its 200,000-plus maids. http://translate. google. com. sg/translate? hl=zh-CN&sl=en&tl=zh-CN&u=http%3A%2F%2Fwww. globalpost. com%2Fdispatch%2Fnews%2Fregions%2Fasia-pacific%2F120529%2Fsingapore-maid-domestic-servant-labor-rights&anno=2 [5] 1. Filipino maids agencies 2. Maid Agency in Singapore 3. Filipino maid

The Determinants of Consumer Price Index in Indonesia

[pic] THE DETERMINANTS OF CONSUMER PRICE INDEX IN INDONESIA Instructor DR. Moussa Larbani Prepared By Ali Faris(G0912449) Imala Hussain(G0822498) Ma Yue(G0918271) Mia Fathia(G0827756) Nurma Saleah(G0912298) Suthinee Suayngam(G0916798) Ulfah Hidayatun(G0815892) ECON 6030 ADVANCE QUANTITATIVE METHOD Term Paper Kulliyah of Economics and Management Sciences Department of Business Administration 2009/2010 Abstract The most well known and widely quoted economic indicator is the CPI (Consumer Price Index).It represents an estimation of the change in prices of consumer goods and services. Generally, it represents a measurement of our expenses on goods and services we use to meet our day-to-day needs. Severe problems to the overall economy can be caused if the prices of consumer goods and services are abruptly changed. This paper attempts to examine the factors that influence the Consumer Price Index. We observe four variables, namely, money supply, gross domestic product, interest rate, and share price.By utilizing quarterly data from 1996 to 2008, this study applies multiple regressions method to find the best model and factors which can explain Consumer Price Index. The result indicates gross domestic product, interest rate, and stock price significant effect to consumer price index, whereas money supply does not have significant effect. This study also finds that the highest Adjusted R2 as goodness criteria of the model is derived when we include all the factors in the model.Hence, we can conclude that those factors have either strong or weak contribution to consumer price index. Keyword: Consumer Price Index, 1. INTRODUCTION From the beginning of civilization, tribes, countries and nations have always been looking for ways to attain prosperity and growth so as to improve the standard of living for their own people. From the times of Caesar to leaders of today such as John F. Keneddy, things haven’t changed much. To attain prosperity one of the most important things is to maintain a healthy economy.However there are many factors that threaten a healthy economy such as inflation, economic recessions and many other factors. Despite all these threats and inevitable slumps and declines in economy, an economy can be monitored and as such Consumer Price Index is one of the most important economic indicators. Using consumer pricing index, the health of the economy can be in check and the state can take necessary preventive measures otherwise not taken could lead to devastating effects in the form of high unemployment, bankruptcies, major financial losses etc.The CPI is a fixed-basket price index as it represents the price of a constant quantities basket of goods and services purchased by the average consumer. CPI is one of the most frequently used  statistics for identifying periods of inflation or deflation. This is  because large rises in CPI during a short period of time typically denote periods of inflation and  large drops in CPI du ring a short period of time usually  mark  periods of  deflation. It is compiled by the Department of Labor's Bureau of Labor Statistics.In order to get the final result for the CPI, wide researches of the prices of the included in the consumer basket goods and services are made. Then they are entered into a special computer program that makes the calculations. The importance of CPI is viewed in the fact that the estimations of other products, services and benefits are directly linked to the levels of the CPI. For example, if the CPI experiences an increase in its value, then the Social Securities benefits will rise as well. Other things that are directly linked to CPI include: †¢ Wages †¢ Lease agreements Union contracts †¢ Benefit statements and etc. Severe problems to the overall economy can be caused if the prices of consumer goods and services are abruptly changed. Most people associate the concept of CPI with inflation. An increase in the value of the CPI means that an increase in inflation has been observed. When inflation increases the purchasing power of money is lost and people will change their spending habits as they meet their purchasing thresholds and producers will suffer and be forced to cut output. This can be readily tied to higher unemployment rates.The whole economy falls into a recession. The objective of this paper is to find a linear regression model that will accurately estimate the consumer pricing index of Indonesia by using the following independent variables, 1) money supply, 2) Gross domestic product, 3) interest rates and 4) stock prices. In economics, money supply is the total amount of money available in an economy at a particular point in time. There are several ways to define â€Å"money†, but standard measures usually include currency in circulation and demand deposits.The gross domestic product (GDP) or gross domestic income (GDI) is a basic measure of a country's economic performance and is the m arket value of all final goods and services made within the borders of a country in a year. It is a fundamental measurement of production and is very often positively correlated with the standard of living. An interest rate is the price a borrower pays for the use of money they do not own, for instance a small company might borrow from a bank to kick start their business, and the return a lender receives for deferring the use of funds, by lending it to the borrower.Interest rates are normally expressed as a percentage rate over the period of one year. Stock Price in this paper is referred to as Stock Market index which is based on a statistical compilation of the share prices of a number of representative stocks. We observe four variables namely, money supply, gross domestic product, interest rate, and stock price. By utilizing quarterly data from 1996 to 2008, this study applies multiple regressions method to find the best model and factors which can explain Consumer Price Index (S ee appendix 1 and 2). 2. METHODOLOGY 2. 1Bivariate Pearson CorrelationPearson [pic]is typically used to describe the strength of the linear relationship between two quantitative variables. Often, these two variables are designated [pic](predictor) and [pic](outcome). Pearson [pic] has values that range from -1. 00 to +1. 00. The sign of [pic]provides information about the direction of the relationship between [pic] and [pic]. A positive correlation indicates that as scores on [pic] increase, scores on [pic]also tend to increase; a negative correlation indicates that as scores on [pic] increase, scores on [pic]neither increase nor decrease in a linear manner.The absolute magnitude of Pearson [pic] provides information about the strength of the linear association between scores on [pic] and [pic]. For values of [pic]close to 0, there is no linear association between [pic] and [pic]. When [pic]= +1. 00, there is a perfect positive linear association; when [pic]= -1. 00, there is a perf ect negative linear association. Intermediate values of [pic]correspond to intermediate strength of the relationship (Warner, 2008). 2. 1. 1Assumption for Pearson [pic] (Warner, 2008)The assumptions that need to be met for Pearson [pic] to be an appropriate statistic to describe the relationship between a pair of variables are as follows: 1. Each scores on [pic] should be independent of other [pic] scores (and each score on [pic]should be independent of other [pic]scores). 2. Scores on both [pic]and [pic]should be quantitative and normally distributed. 3. Scores on [pic]should be linearly related to scores on[pic]. 4. [pic], [pic]scores should have a bivariate normal distribution. 2. 1. Computation of Pearson [pic] (Warner, 2008) Formula to calculate Pearson [pic]from the raw scores on [pic]and [pic]is as follows: [pic](2. 1) 2. 1. 3Correlation matrix (Warner, 2008) A correlation matrix usually denoted by R; it contains the correlations among all possible pairs of [pic]variables. Th e entire set of correlation in an R matrix is as follow [pic] R = [pic][pic] Note several characteristics of this matrix. All the diagonal elements equal 1 (because the correlation of a variable with itself is, by definition, 1. 0).The matrix is â€Å"symmetric† because each element below the diagonal equals one corresponding element above the diagonal. 2. 2Multiple Regressions Multiple Regression analysis provides an equation that predicts raw score on a quantitative [pic] variable from raw scores on [pic] variables, with [pic]. The predictor or [pic] variables are usually also quantitative, but it can also be a dichotomous variable (dummy variable). Usually, regression analysis is used in non experimental research situations, in which the researcher has manipulated none of the variables.In the absence of an experimental design, causal inferences cannot be made. However, researchers often select at least some of the predictor variables for regression analysis because they be lieve that these might be â€Å"causes† of the outcome variable. If an [pic] variable that is theorized to be a â€Å"cause† of [pic]fails to account for a significant amount of variance in the [pic] variable in the regression analysis, this outcome may weaken the researcher’s belief that the [pic] variable has a causal connection with [pic].On the other hand, if a [pic] variable that is thought to be â€Å"causal† does uniquely predict a significant proportion of variance in [pic] even when confounded variables or competing causal variables are statistically controlled, this outcome may be interpreted as consistent with the possibility of causality. (Warner, 2008) 1. The Multiple Regressions Model Equation The raw score version of regression equation with [pic] predictor variables is written as follows [pic](2. 2) here [pic]is the predicted score on the outcome ([pic]) variable, [pic]is the intercept or constant term, [pic]are regression coefficients, an d [pic]are predictor variables. The [pic]regression coefficient represent partial slope. The [pic] slope represents the predicted change in [pic] for a one-unit increase in[pic], controlling for [pic](i. e. , controlling for all other predictor variables included in the regression analysis). The standard score version of a regression equation with [pic] predictors is represented as follows: [pic](2. ) where [pic]is [pic]scores on [pic], [pic]are beta coefficient that is used to predict The beta coefficients in the standard score version of the regression can be compared across variables to assess which of the predictor variables are more strongly related to the [pic]outcome variable when all the variables are represented in [pic]score form. Beta coefficient may be influenced by many types of artifacts such as unreliability of measurement and restricted range of scores in the sample. (Warner, 2008) 2. Model buildingThis paper use Stepwise regression model building to develop the leas t squares regression in steps, either to forward selection backward elimination, or through standards stepwise regression. The coefficient of partial determination is the measure of the marginal contribution of each independent variable, given that other independent variables are in the model. 2. 2. 3Statistics Sum-of-squares terms. Several regression statistics are computed as functions of the sums of-squares terms: [pic] (2. 4) Partitioning of variation.The regression equation is estimated such that the total sum-of squares can be partitioned into components due to regression and residuals: SST = SSR+ SSE(2. 5) Coefficient of determination. The explanatory power of the regression is summarized by its â€Å"R-squared† value, computed from the sums-of-squares terms as [pic](2. 6) R2, also called the coefficient of determination, is often described as the proportion of variance â€Å"accounted for†, â€Å"explained†, or â€Å"described† by regression. It i s important to keep in mind that a high R2 does not imply causation.The relative sizes of the sums-of-squares terms indicate how â€Å"good† the regression is in terms of fitting the calibration data. If the regression is â€Å"perfect†, all residuals are zero, SSE is zero, and R2 is 1. If the regression is a total failure, the sum-of-squares of residuals equals the total sum-of-squares, no variance is accounted for by regression, and R2 is zero. Adjusted R2. The R2 value for a regression can be made arbitrarily high simply by including more and more predictors in the model. The adjusted R2 is one of several statistics that attempts to compensate for this artificial increase in accuracy.The adjusted R2 is given by: [pic](2. 7) n = sample size (e. g. , number of years of data in calibration period) p = number of predictors in the model, not counting the constant term As shown by the equation, R2 with hat is lower than R2 if the model has more than one predictor. Adding predictors has the effect of increasing the difference between R2 with hat and R2. Adjusted R2 is also useful in comparing among models. ANOVA table and definition of â€Å"mean squared† terms. The sums-of-squares terms and related statistics are often summarized in an Analysis of Variance (ANOVA) table: [pic] Source= source of variationSS= sum-of-squares term df= degrees of freedom for SS term MS= â€Å"mean squared† terms The mean squared terms are the sums-of-squares terms Standard error of the estimate. The residual mean square (MSE) is the sample estimate of the variance of the regression residuals. The population value of the error term is sometimes written as ? e2 while the sample estimate is given by se2 = MSE(2. 8) where MSE has been defined previously. The square root of the residual mean square is called the root-mean-square error (RMSE), or the standard error of the estimate. [pic](2. 9) The subscript â€Å"c† is attached (RMSEc) in (4. ) to distingu ish the RMSE derived from calibration from the root-mean-square error derived by cross-validation (see later). F ratio or â€Å"overall F†. Recall that the explanatory power of a regression is given by the regression R2, which is computed from sums-of-squares terms. The F-ratio, or overall F, which is computed from the mean squared terms in the ANOVA table, estimates the statistical significance of the regression equation. The F-ratio is given by [pic](2. 10) The advantage of the F- ratio over R2 is that the F- ratio takes into account the degrees of freedom, which depend on the sample size and the number of predictors in the model.A model can have a high R2 and still not be statistically significant if the sample size is not large compared with the number of predictors in the model. The F- ratio incorporates sample size and number of predictors in an assessment of significance of the relationship. The significance of the F- ratio is obtained by referring to a table of the F distribution, using degrees of freedom {df1,df2}, where df1 and df2 are the degrees of freedom for the regression mean square and residual mean square from the ANOVA table.How to reject or accept F-test (for overall significance) HO: ? 1 = ? 2 HA : ? 1 and ? 2 not both zero ? = . 05 Decision: Reject Ho if the f-stat falls in the rejection area (p values > ? = . 05) [pic] T-test. The T-test shows if there is a linear relationship between the variable xi and y. The test statistic: [pic](2. 11) How to reject or accept T-test (for individual significance) HO: ? 1 = 0 HA : ? 1 ? 0 ? = . 05 Decision: Reject Ho if the test statistic for each variable falls in the rejection region (p values < . 05) [pic]Confidence interval for estimated coefficients. If the regression assumptions on the residuals are satisfied, including the normality assumption, then the sampling distribution of an estimated regression coefficient is normal with a variance proportional to the residual mean square (MSE). Th e variance of the estimator also depends on the variances and covariances of the predictors. The idea is best illustrated for the case of simple linear regression, for which the variance of the regression coefficient is given by [pic](2. 12)Where Se2 is the residual mean square, xi is the value of the predictor in year xi with hat is the mean of the predictor, and the summation is over the n years in the calibration period. The 100 (1 ? ?) % confidence interval is [pic], where t? /2 is obtained from s t distribution with n-2 degrees of freedom. For more than one predictor, the confidence intervals for regression can be computed similarly, but the equation is more complicated. The equation for the variances and covariances of estimated coefficients is expressed in matrix terms by [pic](2. 13) where X is the time series matrix of predictors.This equation returns a matrix, with the variances of the parameters along the diagonal, and the covariances as the off-diagonal elements (Weisber g 1985, p. 44). The appropriate degrees of freedom of the t distribution is df = n ? K ? 1, where K is the number of predictors in the model, and n is the sample size. Multicolinearity The predictors in a regression model are often called the â€Å"independent variables†, but this term does not imply that the predictors are themselves independent statistically from one another. In fact, for natural systems, the predictors can be highly intercorrelated. Multicolinearity† is a term reserved to describe the case when the intercorrelation of predictor variables is high. It has been noted that the variance of the estimated regression coefficients depends on the intercorrelation of predictors. Haan (2002) concisely summarizes the effects of multicolinearity on the regression model. Multicolinearity does not invalidate the regression model in the sense that the predictive value of the equation may still be good as long as the prediction are based on combinations of predictors within the same multivariate space used to calibrate the equation.But there are several negative effects of multicolinearity. First, the variance of the regression coefficients can be inflated so much that the individual coefficients are not statistically significant – even though the overall regression equation is strong and the predictive ability good. Second, the relative magnitudes and even the signs of the coefficients may defy interpretation. For example, the regression weight on a tree-ring index in a multivariate regression equation to predict precipitation might be negative even though the tree-ring index by itself is positively correlated with precipitation.Third, the values of the individual regression coefficients may change radically with the removal or addition of a predictor variable in the equation. In fact, the sign of the coefficient might even switch. Signs of multicolinearity. Signs of multicolinearity include 1) high correlation between pairs of predictor variables, 2) regression coefficients whose signs or magnitudes do not make good physical sense, 3) statistically non-significant regression coefficients on important predictors, and 4) extreme sensitivity of sign or magnitude of regression coefficients to insertion or deletion of a predictor variable.Variance Inflation Factor (VIF). The Variance Inflation Factor (VIF) is a statistic that can be used to identify multicolinearity in a matrix of predictor variables. â€Å"Variance Inflation† refers here to the mentioned effect of multicolinearity on the variance of estimated regression coefficients. Multicolinearity depends not just on the bivariate correlations between pairs of predictors, but on the multivariate predictability of any one predictor from the other predictors. Accordingly, the VIF is based on the multiple coefficient of determination in regression of eachpredictor in multivariate linear regression on all the other predictors: pic](2. 14) where Ri2 is the multip le coefficient of determination in a regression of the ith predictor on all other predictors, and i VIF is the variance inflation factor associated with the ith predictor. Note that if the ith predictor is independent of the other predictors, the variance inflation factor is one, while if the ith predictor can be almost perfectly predicted from the other predictors, the variance inflation factor approaches infinity. In that case the variance of the estimated regression coefficients is unbounded.Multicolinearity is said to be a problem when the variance inflation factors of one or more predictors becomes large. How large it appears to be a subjective judgement. According to Haan (2002), some researchers use a VIF of 5 and others use a VIF of 10 as a critical threshold. These VIF values correspond, respectively, to Ri2 values of 0. 80 and 0. 90. Some compute the average VIF for all predictors and declare that an average â€Å"considerably† larger than one indicates multicolinea rity (Haan, 2002).At any rate, it is important to keep in mind that multicolinearity requires strong intercorrelation of predictors, not just non-zero intercorrelation. The VIF is closely related to a statistic call the tolerance, which is 1/VIF. Some statistics packages report the VIF and some report the tolerance (Haan 2002). 3. MODEL SPESIFICATION AND DATA SOURCE Based on the theory review in the previous section, we build the following specification to capture the determinants of money supply in Indonesia: CPI = ? 0 + ? 1M1 + ? 2GDP + ? 3IR + ? 4SP + ? The variables are defined as followed: 1.Money supply (M1) is M0 (physical currency) and demand deposits, which are checking accounts. This is used as a measurement for economists trying to quantify the amount of money in circulation. The M1 is a very liquid measure of the money supply, as it contains cash and assets that can quickly be converted to currency. 2. Gross Domestic Product (GDP) is the income of individuals or nations after adjusting for inflation. 3. Consumer price index (CPI) is an index number measuring the average price of consumer goods and services purchased by households. 4. Interest rate (IR) is a fee paid on borrowed capital. . Share Price (SP) is the price of one share of stock. This paper uses quarterly data, from quarter 1 of 1996 to quarter 2 of 2008 that is taken from International Financial Statistic. We also use SPSS software to regress the model above. 4. VALUATION 4. 1Model Estimation We will present the result of data analysis using Multiple Regression Analysis. Multiple Regression analysis provides an equation that predicts raw score on a quantitative [pic] variable from raw scores on [pic] variables, with[pic]. The best model is indicated by the highest Adjusted R2 and the lowest standard errors.In this study, consumer price indexes (CPI) were predicted from the following variables: money supply (M1), gross domestic product (GDP), interest rate (IR) and share price (SP). The sample size[pic]is 50. 4. 2Bivariate correlation In this part, we will observe the strength of the linear relationship between each independent variable and CPI. Table 1. Correlations | | |CPI |M1 |GDP |IR |SP | |1996Q1 |11. 716111 |19. 30 |4. 771904 |3. 788341 |3. 853983 |0. 065642 | |1996Q2 |11. 766373 |19. 4 |4. 819983 |3. 822246 |3. 858643 |0. 036398 | |1996Q3 |11. 827298 |19. 17 |4. 717570 |3. 877778 |3. 863081 |-0. 014697 | |1996Q4 |11. 879324 |19. 16 |4. 810590 |3. 909344 |3. 872063 |-0. 037281 | |1997Q1 |11. 889998 |18. 98 |4. 934683 |3. 905124 |3. 897606 |-0. 007518 | |1997Q2 |11. 914423 |18. 72 |4. 941414 |3. 921527 |3. 906252 |-0. 015275 | |1997Q3 |12. 002958 |23. 38 |4. 78997 |4. 036802 |3. 924765 |-0. 112037 | |1997Q4 |12. 039144 |26. 19 |4. 477901 |4. 111444 |3. 959830 |-0. 151614 | |1998Q1 |12. 262335 |26. 33 |4. 624532 |4. 270861 |4. 140733 |-0. 130127 | |1998Q2 |12. 314070 |32. 16 |4. 495154 |4. 363053 |4. 309088 |-0. 053965 | |1998Q3 |12. 484700 |34. 93 |4. 308177 |4. 530508 |4. 491942 |-0. 038566 | |1998Q4 |12. 457244 |35. 20 |4. 294247 |4. 12515 |4. 538626 |0. 026111 | |1999Q1 |12. 510708 |34. 11 |4. 396215 |4. 536868 |4. 585091 |0. 048223 | |1999Q2 |12. 512071 |30. 34 |4. 767910 |4. 478028 |4. 578437 |0. 100409 | |1999Q3 |12. 533785 |24. 52 |4. 754038 |4. 455257 |4. 555728 |0. 100470 | |1999Q4 |12. 525806 |21. 68 |4. 830264 |4. 422381 |4. 555029 |0. 132648 | |2000Q1 |12. 689215 |19. 58 |4. 798267 |4. 536245 |4. 79349 |0. 043104 | |2000Q2 |12. 725801 |18. 46 |4. 615507 |4. 572988 |4. 589384 |0. 016396 | |2000Q3 |12. 796032 |17. 98 |4. 534614 |4. 630870 |4. 611431 |-0. 019440 | |2000Q4 |12. 817033 |17. 80 |4. 436443 |4. 654596 |4. 639514 |-0. 015082 | |2001Q1 |12. 894097 |17. 85 |4. 423641 |4. 715383 |4. 668689 |-0. 046693 | |2001Q2 |12. 957670 |18. 26 |4. 396349 |4. 769620 |4. 695093 |-0. 74527 | |2001Q3 |12. 980581 |18. 88 |4. 453272 |4. 786409 |4. 731538 |-0. 054871 | |2001Q4 |12. 967675 |19. 20 |4. 357638 |4. 787355 |4. 758569 |-0. 02878 5 | |2002Q1 |13. 014972 |19. 32 |4. 495629 |4. 812124 |4. 804455 |-0. 007668 | |2002Q2 |13. 038967 |19. 18 |4. 670443 |4. 813862 |4. 813371 |-0. 000492 | |2002Q3 |13. 083051 |18. 87 |4. 499660 |4. 860963 |4. 830240 |-0. 030724 | |2002Q4 |13. 67842 |18. 42 |4. 383610 |4. 856585 |4. 856372 |-0. 000213 | |2003Q1 |13. 119451 |18. 20 |4. 382903 |4. 894774 |4. 879052 |-0. 015721 | |2003Q2 |13. 127729 |17. 68 |4. 568618 |4. 880785 |4. 881073 |0. 000288 | |2003Q3 |13. 168067 |16. 44 |4. 706932 |4. 890676 |4. 889544 |-0. 001132 | |2003Q4 |13. 145558 |15. 43 |4. 867750 |4. 851847 |4. 910358 |0. 058511 | |2004Q1 |13. 193018 |14. 0 |5. 023394 |4. 869902 |4. 926710 |0. 056808 | |2004Q2 |13. 243557 |14. 28 |5. 023446 |4. 905169 |4. 946239 |0. 041070 | |2004Q3 |13. 296856 |13. 88 |5. 055704 |4. 940406 |4. 956855 |0. 016449 | |2004Q4 |13. 303815 |13. 54 |5. 255827 |4. 925389 |4. 972241 |0. 046852 | |2005Q1 |13. 357168 |13. 36 |5. 375579 |4. 954380 |5. 001198 |0. 046817 | |2005Q2 |13. 415743 |13. 29 |5. 03708 |4. 996403 |5. 019906 |0. 023503 | |2005Q3 |13. 477237 |13. 78 |5. 410051 |5. 046527 |5. 037628 |-0. 008900 | |2005Q4 |13. 539065 |15. 78 |5. 387751 |5. 110080 |5. 135998 |0. 025918 | |2006Q1 |13. 570606 |16. 34 |5. 539352 |5. 124611 |5. 157502 |0. 032891 | |2006Q2 |13. 608447 |16. 23 |5. 624725 |5. 145281 |5. 164111 |0. 018831 | |2006Q3 |13. 676882 |16. 00 |5. 78345 |5. 191494 |5. 176234 |-0. 015260 | |2006Q4 |13. 679898 |15. 35 |5. 839146 |5. 174745 |5. 194761 |0. 020015 | |2007Q1 |13. 732362 |14. 70 |5. 885796 |5. 206364 |5. 219177 |0. 012812 | |2007Q2 |13. 777640 |14. 08 |6. 039466 |5. 223014 |5. 222613 |-0. 000401 | |2007Q3 |13. 848229 |13. 56 |6. 144445 |5. 264208 |5. 239273 |-0. 024935 | |2007Q4 |13. 855779 |13. 11 |6. 305412 |5. 52354 |5. 259836 |0. 007483 | |2008Q1 |13. 930695 |12. 94 |6. 292750 |5. 309960 |5. 292817 |-0. 017143 | |2008Q2 |14. 023264 |12. 95 |6. 172412 |5. 392000 |5. 199684 |-0. 192316 | |   |   |   |   |   | |0. 175227 | From the table above, we found the sum square value of error is 0. 175. Predict Consumer Price index (CPI) for a quarter in which the logarithmic of GDP is 12. 89 logarithmic of Interest Rate is 17. 5 and logarithmic of Share Price is 4. 42 LCPI = -4. 927 + 0. 769 (LGDP) + 0. 007 (LIR) – 0. 090 (LSP) = -4. 927 + 0. 769 (12. 89) + 0. 007 (17. 52) – 0. 090 (4. 42) = 4. 72 Confidence interval for the mean LCPI value : [pic]; [pic] [pic] [pic] Prediction interval for the mean LCPI value : [pic]; [pic] [pic] [pic] CONCLUSION We have employed multiple regression analysis method, which involve five variables which are expected to affecting money supply. They are consumer price index, interest rate, stock price, GDP, and money supply [M1]. The data are selected from Indonesia international financial statistics.In the recent years Indonesia has been successfully controlling its money supply to get stability in economic circumstances. From the study we found out that there is strong relations hip between consumer price index [CPI]and GDP. When the Gross Domestic Product [GDP] increases, it will also increase consumer price index as these two have linear relationship. Also there is strong correlation between money supply and consumer price index, which means that mean of CPI increase when money supply increases. Addition to this there is positive correlation between stock price and CPI, when stock price increase it tend to increase CPI.However there is negative correlation between interest rate and CPI, when interest rate increases, CPI decreases. From our finding it shows that R-square is 96 percent, which means it is a good model to describe the relation between CPI and other variables we use in the study. REFERENCES Lawrence S. Meyers, Glenn Gamst, and A. J. Guarino. (2006). Applied Multivariate Research Design and Interpretation. Thousand Oaks, London, and New Delhi: Sage Publications. Miles, Jeremy and Mark Shevlin. (2001). Applying Regression & Correlation: A Guide for Students and Researchers. London: Sage Publications. Warner, R. M. (2008).Applied Statistics From Bivariate Through Multivariate Techniques. Los Angeles, London, New Delhi, Singapore: SAGE Publications. Watson, Collin J. and et al. (1993). Statistics for Management and Economics 5th Edition. Massachusetts: Allyn and Bacon. http://www. investopedia. com http://www. stock-market-investors. com http://www. wikipedia. org Appendix 1. Variables Data |   |M1 |Stock Price |CPI |INTEREST RATE |GDP | | | | | | | | |1996Q1 |53162. 00 |118. 14 |47. 8 |19. 30 |122530. 00 | |1996Q2 |56448. 00 |123. 96 |47. 40 |19. 24 |128846. 00 | |1996Q3 |59684. 00 |111. 90 |47. 61 |19. 17 |136940. 00 | |1996Q4 |64089. 00 |122. 80 |48. 04 |19. 16 |144253. 00 | |1997Q1 |63565. 00 |139. 03 |49. 28 |18. 98 |145801. 00 | |1997Q2 |69950. 00 |139. 97 |49. 71 |18. 72 |149406. 00 | |1997Q3 |66258. 00 |118. 99 |50. 64 |23. 8 |163237. 00 | |1997Q4 |78343. 00 |88. 05 |52. 45 |26. 19 |169252. 00 | |1998Q1 |98270. 30 |101. 96 |62. 85 |26. 33 |211575. 00 | |1998Q2 |109480. 00 |89. 58 |74. 37 |32. 16 |222809. 00 | |1998Q3 |102563. 00 |74. 30 |89. 29 |34. 93 |264263. 00 | |1998Q4 |101197. 00 |73. 28 |93. 56 |35. 20 |257106. 00 | |1999Q1 |105705. 00 |81. 14 |98. 01 |34. 11 |271226. 0 | |1999Q2 |105964. 00 |117. 67 |97. 36 |30. 34 |271596. 00 | |1999Q3 |118124. 00 |116. 05 |95. 18 |24. 52 |277558. 00 | |1999Q4 |124633. 00 |125. 24 |95. 11 |21. 68 |275352. 00 | |2000Q1 |124663. 00 |121. 30 |97. 45 |19. 58 |324232. 00 | |2000Q2 |133832. 00 |101. 04 |98. 43 |18. 46 |336314. 00 | |2000Q3 |135430. 00 |93. 19 |100. 63 |17. 98 |360783. 00 | |2000Q4 |162186. 0 |84. 47 |103. 49 |17. 80 |368440. 00 | |2001Q1 |148375. 00 |83. 40 |106. 56 |17. 85 |397956. 00 | |2001Q2 |160142. 00 |81. 15 |109. 41 |18. 26 |424077. 00 | |2001Q3 |164237. 00 |85. 91 |113. 47 |18. 88 |433905. 00 | |2001Q4 |177731. 00 |78. 07 |116. 58 |19. 20 |428341. 00 | |2002Q1 |166173. 00 |89. 62 |122. 05 |19. 32 |449087. 00 | |2002Q2 |174017. 00 |106. 5 |123. 15 |19. 18 |459993. 00 | |2002Q3 |181791. 00 |89. 99 |125. 24 |18. 87 |480725. 00 | |2002Q4 |191939. 00 |80. 13 |128. 56 |18. 42 |473469. 00 | |2003Q1 |181239. 00 |80. 07 |131. 51 |18. 20 |498546. 00 | |2003Q2 |195219. 00 |96. 41 |131. 77 |17. 68 |502690. 00 | |2003Q3 |207587. 00 |110. 71 |132. 89 |16. 44 |523382. 00 | |2003Q4 |223799. 00 |130. 03 |135. 9 |15. 43 |511733. 00 | |2004Q1 |219087. 00 |151. 93 |137. 93 |14. 80 |536605. 00 | |2004Q2 |226147. 00 |151. 93 |140. 65 |14. 28 |564422. 00 | |2004Q3 |234676. 00 |156. 92 |142. 15 |13. 88 |595321. 00 | |2004Q4 |245946. 00 |191. 68 |144. 35 |13. 54 |599478. 00 | |2005Q1 |244003. 00 |216. 07 |148. 59 |13. 36 |632331. 00 | |2005Q2 |261814. 00 |222. 23 |151. 40 |13. 9 |670476. 00 | |2005Q3 |267762. 00 |223. 64 |154. 10 |13. 78 |713000. 00 | |2005Q4 |271166. 00 |218. 71 |170. 03 |15. 78 |758475. 00 | |2006Q1 |270425. 00 |254. 51 |173. 73 |16. 34 |782779. 00 | |2006Q2 |303803. 00 |277. 20 |174. 88 |16. 23 |812968. 00 | |200 6Q3 |323885. 00 |292. 47 |177. 02 |16. 00 |870551. 00 | |2006Q4 |347013. 00 |343. 49 |180. 33 |15. 35 |873181. 0 | |2007Q1 |331736. 00 |359. 89 |184. 78 |14. 70 |920214. 00 | |2007Q2 |371768. 00 |419. 67 |185. 42 |14. 08 |962838. 00 | |2007Q3 |400075. 00 |466. 12 |188. 53 |13. 56 |1033260. 00 | |2007Q4 |450055. 00 |547. 53 |192. 45 |13. 11 |1041090. 00 | |2008Q1 |409768. 00 |540. 64 |198. 90 |12. 94 |1122080. 00 | |2008Q2 |453093. 00 |479. 34 |181. 22 |12. 95 |1230910. 00 |Appendix 2. Lag of Variable Data |   |lm1 |lsp |lcpi |lgdp |ir | |1996Q1 |10. 881099 |4. 771904 |3. 853983 |11. 716111 |19. 30 | |1996Q2 |10. 941075 |4. 819983 |3. 858643 |11. 766373 |19. 24 | |1996Q3 |10. 996819 |4. 717570 |3. 863081 |11. 827298 |19. 17 | |1996Q4 |11. 068028 |4. 810590 |3. 872063 |11. 879324 |19. 16 | |1997Q1 |11. 059818 |4. 934683 |3. 897606 |11. 889998 |18. 98 | |1997Q2 |11. 55536 |4. 941414 |3. 906252 |11. 914423 |18. 72 | |1997Q3 |11. 101311 |4. 778997 |3. 924765 |12. 002958 |23. 38 | |1997 Q4 |11. 268852 |4. 477901 |3. 959830 |12. 039144 |26. 19 | |1998Q1 |11. 495477 |4. 624532 |4. 140733 |12. 262335 |26. 33 | |1998Q2 |11. 603497 |4. 495154 |4. 309088 |12. 314070 |32. 16 | |1998Q3 |11. 538233 |4. 308177 |4. 491942 |12. 484700 |34. 93 | |1998Q4 |11. 524824 |4. 294247 |4. 538626 |12. 57244 |35. 20 | |1999Q1 |11. 568407 |4. 396215 |4. 585091 |12. 510708 |34. 11 | |1999Q2 |11. 570855 |4. 767910 |4. 578437 |12. 512071 |30. 34 | |1999Q3 |11. 679490 |4. 754038 |4. 555728 |12. 533785 |24. 52 | |1999Q4 |11. 733129 |4. 830264 |4. 555029 |12. 525806 |21. 68 | |2000Q1 |11. 733369 |4. 798267 |4. 579349 |12. 689215 |19. 58 | |2000Q2 |11. 804341 |4. 615507 |4. 589384 |12. 725801 |18. 46 | |2000Q3 |11. 16210 |4. 534614 |4. 611431 |12. 796032 |17. 98 | |2000Q4 |11. 996499 |4. 436443 |4. 639514 |12. 817033 |17. 80 | |2001Q1 |11. 907498 |4. 423641 |4. 668689 |12. 894097 |17. 85 | |2001Q2 |11. 983816 |4. 396349 |4. 695093 |12. 957670 |18. 26 | |2001Q3 |12. 009066 |4. 453272 |4. 731538 |1 2. 980581 |18. 88 | |2001Q4 |12. 088026 |4. 357638 |4. 758569 |12. 967675 |19. 20 | |2002Q1 |12. 020785 |4. 495629 |4. 804455 |13. 14972 |19. 32 | |2002Q2 |12. 066908 |4. 670443 |4. 813371 |13. 038967 |19. 18 | |2002Q3 |12. 110613 |4. 499660 |4. 830240 |13. 083051 |18. 87 | |2002Q4 |12. 164933 |4. 383610 |4. 856372 |13. 067842 |18. 42 | |2003Q1 |12. 107572 |4. 382903 |4. 879052 |13. 119451 |18. 20 | |2003Q2 |12. 181877 |4. 568618 |4. 881073 |13. 127729 |17. 68 | |2003Q3 |12. 243306 |4. 706932 |4. 889544 |13. 168067 |16. 44 | |2003Q4 |12. 18504 |4. 867750 |4. 910358 |13. 145558 |15. 43 | |2004Q1 |12. 297224 |5. 023394 |4. 926710 |13. 193018 |14. 80 | |2004Q2 |12. 328941 |5. 023446 |4. 946239 |13. 243557 |14. 28 | |2004Q3 |12. 365961 |5. 055704 |4. 956855 |13. 296856 |13. 88 | |2004Q4 |12. 412867 |5. 255827 |4. 972241 |13. 303815 |13. 54 | |2005Q1 |12. 404936 |5. 375579 |5. 001198 |13. 357168 |13. 36 | |2005Q2 |12. 475390 |5. 403708 |5. 19906 |13. 415743 |13. 29 | |2005Q3 |12. 497854 |5. 410051 |5. 037628 |13. 477237 |13. 78 | |2005Q4 |12. 510486 |5. 387751 |5. 135998 |13. 539065 |15. 78 | |2006Q1 |12. 507750 |5. 539352 |5. 157502 |13. 570606 |16. 34 | |2006Q2 |12. 624135 |5. 624725 |5. 164111 |13. 608447 |16. 23 | |2006Q3 |12. 688144 |5. 678345 |5. 176234 |13. 676882 |16. 00 | |2006Q4 |12. 757118 |5. 839146 |5. 194761 |13. 679898 |15. 5 | |2007Q1 |12. 712095 |5. 885796 |5. 219177 |13. 732362 |14. 70 | |2007Q2 |12. 826025 |6. 039466 |5. 222613 |13. 777640 |14. 08 | |2007Q3 |12. 899407 |6. 144445 |5. 239273 |13. 848229 |13. 56 | |2007Q4 |13. 017125 |6. 305412 |5. 259836 |13. 855779 |13. 11 | |2008Q1 |12. 923346 |6. 292750 |5. 292817 |13. 930695 |12. 94 | |2008Q2 |13. 023853 |6. 172412 |5. 199684 |14. 023264 |12. 95 | ———————– [pic] [pic] [pic]

A Good Man is Hard to Find by Flannery O’Connor Essay

â€Å"A Good Man is Hard to Find,† written by Flannery O’Connor tells the story of a dysfunctional family headed to vacation and their inevitable death. The family, including their matriarch, the grandmother, represents the delusion perfection that many modern Christians have. The family displays an extreme sense of vanity, self-centeredness, and disobedience during the first half of the story. The first half of the story does not follow a specific pattern nor does it hold significance to the family’s lives. O’Connor uses the first half of the story to show her audience that the family is heading down a path of destruction due to their narcissism and current lifestyle. In the second half of the story, O’Connor quickly introduces the†¦show more content†¦O’Connor reveals the family is not living a true Christian life, but instead living a lie unknowingly. Specifically referring to O’Connor’s layout of the story, she reveals the grandmother’s flaws and foreshadows how her behavior and lifestyle will lead to the deaths of her family. The grandmother causes many of the random acts to occur within the first half of the story, from her blurting out about the plantation’s whereabouts to discussing the lack of good people in the world with Red Sammy. Hendricks concludes the grandmother’s self-centered actions and lack of regard towards others results in her being â€Å"the source of her most serious shortcoming – her firm, and eventually fatal, conviction of her own rightness† (204). O’Connor allows the grandmother to alter the family’s route, create tension between the son and his children through introducing the idea of visiting the old plantation house, and by sneaking the cat on the vacation, which ultimately leads to the car accident. The grandmother’s illogical thought process combined with her over confidence and lack of humility, lead s to she and her family’s untimely deaths. Prior to the accident, the family participates in sightseeing, visiting Red Sammy’s restaurant, and arguing amongst each other. This portion of the story does not follow a pattern and serves only to show the readers that the characters, specifically the grandmother, are living life inShow MoreRelatedA Good Man is Hard to Find by Flannery OConnor1196 Words   |  5 PagesA prolific writer, famously known as Flannery O’Connor in 1953, wrote the short narrative titled â€Å"A Good Man is Hard to Find† (Scott 2). However, it was published two years later in 1955, in her second collection of short stories. This particular collection presented the author as a key voice in the ancient American literature world until she met her sudden death in 1964 when she was only 39. The collection also won her tremendous fame, especially concerning her unmatchable creativity and masteryRead MoreA Good Man is Hard to Find by Flanne ry OConnor748 Words   |  3 PagesFlannery O’Connor’s Southern Gothic short story, â€Å"A Good Man is Hard to Find,† is one of sudden violence; although, it begins rather uneventful (Kaplan 1). Bailey, his wife, and their children, John Wesley, June Star, and a baby boy, are all looking forward to a trip to Florida. Grandmother, Bailey’s mom, wants to go to east Tennessee to see her relatives, not Florida. She uses an article in the newspaper that tells of an escaped criminal, the Misfit, which is headed to Florida to try to persuadeRead MoreA Good Man Is Hard to Find by Flannery OConnor645 Words   |  3 PagesA Good Man â€Å"She would have been a good woman†¦if it had been somebody there to shoot her every minute of her life† (Gardner). 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Flannery OConnor, the author, lets the reader find out who the grandmother is by her conversations and reactions to the other characters in the story. The grandmother is the most important character in the story because she has a main role in the stories principal action. This little old lady is the protagonist in this piece. We learn more about her fromRead MoreEssay on A Good Man is Hard to Find by Flannery OConnor1564 Words   |  7 PagesA Good Man is Hard to Find by Flannery OConnor A Good Man is Hard to Find is an extremely powerful commentary that elucidates Flannery OConnors opinions about religion and society. Like the majority of her other works, A Good Man is Hard to Find has attracted many interpretations based on Christian dogma (Bandy 1). These Christian explications are justified because Miss OConnor is notorious for expressing Catholic doctrines through her fiction. Once she even remarked I see fromRead MoreA Good Man is Hard to Find by Flannery OConnor Essay1959 Words   |  8 Pages Who is the Misfit? In the short story, â€Å"A Good Man Is Hard to Find† a family comprising of a grandmother, a father, three children, and a wife is headed on vacation has the misfortune of meeting a murderous band of serial killers. The Misfit and his band of serial killers are recently escapees of a federal prison. In the following paragraphs this paper looks into the issues of, what one would do in a situation such as that and the background of the the family and murderers as well. The MisfitRead More Symbolism in A Good Man Is Hard to Find by Flannery OConnor1038 Words   |  5 PagesUse of Symbolism in A Good Man is Hard to Find by Flannery OConnor A Good Man is Hard to Find by Flannery OConnor is a short story that depicts a familys vacation to Florida that turned into an abysmal tragedy when they met with the Misfit, a convict who escaped from prison. This story is meant to be interpreted as a parable, whereby OConnor made skilful use of symbolism to bring about messages such as the class-consciousness and the lack of spiritual faith that exist amongst human. Read MoreA Good Man Is Hard to Find by Flannery O’Connor Essay1612 Words   |  7 PagesIn the short story A Good Man Is Hard to Find, written by Flannery O’Connor, the theme that the definition of a ‘good man’ is mysterious and flawed is apparent. The reader must realize that it is difficult to universalize the definition of a good man because every person goes through different experiences. Thus, these experiences affect his or her viewpoint and in turn flaw ones view on a good man. O’Connor conveys this theme through her excellent us e of diction, imagery, foreshadowing, and symbolism