Arbitration Clauses and Litigation :: Business Law Essays

Arbitration Clauses and Litigation More and more companies are including arbitrational clauses in their contracts. Whether it is in an employee application or an online selling website, arbitrational clauses are becoming abundant. But do these clauses hold up in the legal system? In the recent Paypal Corporation case, the arbitration clause was not enforced due to miniscule details such as clicking a mouse. The overruling of these clauses is becoming the norm as people are beginning to realize what they have gotten themselves into. The same type of arbitrational clause dispute as the Paypal Corporation occurred in the case of BellSouth Mobility LLC v. Christopher. BellSouth institutionalized an arbitrational clause in its service agreement that states that â€Å"instead of suing in court, company and customer agree to arbitrate any and all dispute†. In the event that the disagreement goes through arbitration, the arbitrator can not give punitive damages to the plaintiff as well as only receive a limited amount of recovery money. When Christopher brought the case before an appellate court, the court sided in his favor claiming that the contract was â€Å"substantively unconscionable† due to the fact that BellSouth still had the right to bring Christopher to court over different legal matters, giving them an â€Å"unfair advantage.† The case continued to go to trial court to see if the contract was â€Å"procedurally unconscionable† because of the small print of the arb itrational clause. Because Christopher was not fully forewarned about the arbitrational clause, and the fact that BellSouth took advantage of a client, BellSouth was found guilty (Hackbarth). A similar situation occurs in the case of Toppings v. Meritech Mortgage Services (MMS). An elderly couple, Margaret and Roger Toppings received a loan from Meritech Mortgage Services for thirty-seven thousand dollars with a monthly payment plan which would last for fifteen years, along with thirty-six thousand dollars in interest. Before signing the loan, the couple asked for the document to be explained. At the time, the MMS lawyer was not coherent with the document but told the couple to read it at home after signing the document. Upon reading the contract, the Toppings came across the amount to be repaid in interest and tried to bring MSS to court for trying to take advantage of the elderly.

The Themes of Euripides Medea :: Euripides Medea Essays

The Themes of Medea    Medea, a play by the Greek playwright Euripides, explores the Greek-barbarian dichotomy through the character of Medea, a princess from the "barbarian", or non-Greek, land of Colchis.   Throughout the play, it becomes evident to the reader that Medea is no ordinary woman by Greek standards.   Central to the whole plot is Medea's barbarian origins and how they are related to her actions.   In this paper, I am attempting to answer questions such as how Medea behaves like a female, how she acts heroically from a male point of view, why she killed her children, if she could have achieved her goal without killing them, if the murder was motivated by her barbarian origins, and how she deals with the pain of killing her children.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   As an introduction to the play, the status of women in Greek society should be briefly discussed.   In general, women had very few rights.   In the eyes of men, the main purposes of women in Greek society were to do housework such as cooking and cleaning, and bear children.   They could not vote, own property, or choose a husband, and had to be represented by men in all legal proceedings.   In some ways, these Greek women were almost like slaves.   There is a definite relationship between this subordination of women and what transpires in the play.   Jason decides that he wants to divorce Medea and marry the princess of Corinth, casting Medea aside as if they had never been married.   This sort of activity was acceptable by Greek standards, and shows the subordinate status of the woman, who had no say in any matter like this.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Even though some of Medea's actions were not typical of the average Greek woman, she still had attitudes and emotions common among women. For instance, Medea speaks out against women's status in society, proclaiming that they have no choice of whom to marry, and that a man can rid themselves of a woman to get another whenever he wants, but a woman always has to "keep [her] eyes on one alone." (231-247)   Though it is improbable that women went around openly saying things of this nature, it is likely that this attitude was shared by most or all Greek women.   Later in the play, Medea debates with herself over whether or not to kill her children: "Poor heart, let them go, have pity upon the

Analytical and Empirical Method

How to conduct heuristic evaluation? 5 Advantages5 Disadvantages6 Empirical Method (Usability Testing)7 What is Usability Testing? 7 How to conduct a usability testing? 7 Advantages of Usability Testing8 Disadvantages of Usability Testing9 Conclusion10 Task 211 Introduction11 Description of the problem11 Description of the difficulties11 Conclusion:14 References:15 Task 1 Introduction There are 4 ways mostly to evaluate user interface.Heuristic Evaluation (heuristically) by just looking at the interface and passing judgement according to one’s own view. Cognitive Walkthrough (analytical method) by some analysis procedure, Pluralistic Walkthroughs (empirically) by experiments with trial users, Feature, Consistency & Standards Inspection (automatically),by a programmed procedure. For this assignment I have choose the usability testing for empirical method where else for analytical method I have choose heuristic method. [1] Analytical Method (Heuristic usability) What is heur istic? Heuristic evaluation is a discount usability engineering method for quick, cheap, and easy evaluation of a user interface design. † [3] In heuristic evaluation the usability issues covered are effectiveness and efficiency but do not considered the satisfaction. Quantitative Data is not collected. this method also can be conducted remotely also can be used on any system. Several evaluators independently evaluate the interface & come up with potential usability problems. It is important that there be several of these evaluators and that the evaluations be done independently.Nielsen's experience indicates that around 5 evaluators usually results in about 75% of the overall usability problems being discovered. Heuristic Usability Principals Here I am going to explain more and have a closer look at what condition are needed to evaluate interfaces. Basically the evaluation is easier than design. The principles are the basic points that drive design and drive evaluation as well. The principals are: 1. Visibility of system status The application should always keep user on track and well known on which section of the application they are.This can avoid confusion and user can get the information easily without consuming more time. 2. Match between system and the real world. The relationship between two things, in most cases the mapping from digital world to real world. Metaphors and analogies 3. User control and freedom User should be informed what the system is doing and is the user on the right track, does it work the way it is suppose to work. This can prevent the user to do their activity correctly on path without having to redo all again. The application also should support undo and redo.The system should confirm with the user before take any action on the application, so user can exit immediately from new actions that risk them. 4. Consistency and standards An application should have a standard platform convention like the symmetry of the interfac e. Is it symmetrical? (Left-Right, Top-Bottom, Center). This includes also the wording to access the application example for navigation bar. The â€Å"Home† button should be same for all pages. It should not been changed to other type of looks (symbol or font) at other page when user access to it. 5.Error prevention The text message for error prevention should convey a message to user while they are currently on the application and risky to faulty. User can understand what the error message is about and prevent the faulty happen before they proceed to the next step. 6. Recognition rather than recall Using symbols that make user feel ease to recognize, remember and think what is the use of the button. The colour contrast also effect attention and focus where the user has been explored on the application. It also is to make user more focus and remember what they did.User also might convey the color for pink †¦ it’s a girl and blue †¦ it’s a boy. This wou ld not affect user memory load. 7. Flexibility and efficiency of use This refers to the perceived and actual properties of the â€Å"thing† that determine just how the â€Å"thing† could be used for new user that never been using that application. The appearance of the â€Å"thing† tells them how to use it. If the system is understandable, neither expert nor inexperienced can easily use the application. 8. Aesthetic and minimalist design The application should display the important and description for the point it is focusing on.The application should not display unnecessary information which will only cause the information traffic on the application. A special consideration for standards graphics and design are enough to make user comfortable when using the application. 9. Help users recognize, diagnose, and recover from errors The error text must not be too long or too short. It should be simple and precisely organized to make sure the user understand the faul ty they have done and they can correct the error based on the solution given in the error message. 10. Help and documentationThere might be possibilities that user don’t need any help and documentation while or after using an application, but there is the chances for user to document the information and get the help if there are lost. This should be considered to ensure user get fully access and satisfied after exit the application. The application must make the user easy to find the information they are looking for focusing on their task and help should provide the step they should pursue. How to conduct heuristic evaluation? Heuristic evaluation by Nielson is a method used by researchers to find usability problems in interfaces designed for use by public.The heuristic evaluation process starts from questions a small number of users, called evaluators, to complete a set of pre-programmed tasks usually related with using the interface. As summary basically the evaluation will be : * Obtain the service of 4, 5 or 6 usability experts, questions a set of pre-programmed tasks usually related with using the interface. * Each expert will perform an independent evaluation, answer question related to interface * Give experts a heuristics inspection guide, is followed from Nielsen’s ten usability principles (Nielsen, Jakob 1994). Collect the individual evaluations, which are answered in subsequent iterative sketch of the interface for better simplicity of use and significant knowledge.* Bring the experts together and do a group heuristic evaluation. (Optional) Advantages Quick and relatively inexpensive feedback to designers can be provided by this method. Response can be obtained early in the design process. Conveying the correct heuristic can help suggest the best corrective measures to designers. * Cheaper comparative to other assessment method. * Spontaneous and simple to encourage potential evaluators to use the method * No advance planning required Formal usability training is not required for evaluators. Professional computer programmers and computer science students are used in Nielsen and Molich * Can be used early in the growth process * Quicker revolve time than laboratory testing Disadvantages Applying heuristics effectively need a good knowledge and skill. Usability experts sometime hard to find and can be pricey. Multiple evaluators are recommended and results must be aggregated. Minor issues and fewer major issues can be detected through these evaluations. * Evaluators maybe just have the understanding of usability design principles, but not for usability experts.Usability experts would recognize more issues than non-experts, experts are the people who have the skill with the type of interface being evaluated, they can identified the most issues was according to Nielson. * The results from multiple evaluators must be aggregated. This is because the evaluator only can detect few issues, but if the issues combined the m ore precise result outcome they can gain. * Heuristic evaluations only identify lesser usability issues compare to and other discount methods may not identify as many usability issues as other usability engineering methods, for example, usability testing. Complex interfaces may not scale well for heuristic reviews. Evaluators may not find the major or serious problem the interface had if the interfaces are complex. * All the issues identified may not willingly suggest solutions for the usability issues * The evaluators are just pretend to be the user but they are not the real user. * The entire problem identified may not be the actual problem in real interface. Empirical Method (Usability Testing) What is Usability Testing? We need to use usability testing method to produce tasks professionally, efficiently and pleasingly for the user benefit.It is better to produce a pre-programme application to ensure so that any important issues can identify. Usability testing can be done at any stages of the design process. Usability testing is not an alternative for a human centred design process and appropriate techniques such as walkthroughs also can be use. Test host or the user who is going to represent for usability testing session should attend. The attendance should be at least one observer one developer and one business representative. Observers should strictly be in a separate observation room.Density of the system, the number of intended users, and budgetary constraints should be considered when doing the session. Even one test session is better than none, but if its more it will tend to discover the majority of important issues. There are two major factor should be considered when conducting a usability testing. The first is to ensure that the best possible method for testing is used. The second major consideration is to ensure that an iterative approach is used. How to conduct a usability testing? To prepare for a usability test we must identify delegate tasks .Ideally, these should derive from scenarios or use cases developed earlier in the design process. Have a clear definition of when a task is complete. We also must prepare a test schedule, book the required room, and identify representative users and invite them to attend. We will need the following materials: †¢ A formal script so that all participants are treated in the same way. †¢ A pre-evaluation questionnaire to check that your participants match the required profile. †¢ A list of tasks, together with criteria for measuring whether they have been successfully completed. Logging sheets to record timing, events, participant actions, concerns and comments. The guidelines should be followed: †¢ Run a pilot test. †¢ Ensure that participants have the option to abandon any tasks which they are unable to complete. †¢ Do not prompt participants unless it is clearly necessary to do so. †¢ Record events in as much detail as possible to the level of keys trokes and mouse clicks if necessary. †¢ If there are observers, ensure that they do not interrupt in any way. Observers formally prior to test. †¢ Be sensitive to the fact that developers may be upset by what they observe or what you report.Advantages of Usability Testing The advantages of usability testing are [5]: * Reply direct from the board audience to spot the project group * Private conversation can be done by testing the issue to see how users response to the different options being discussed * Problem and possible difficulties are stated clearly before the product is launched. The business advantages of usability testing can be seen at the end of the project: * Probability of usage and frequent usage increase * Risk of the product fault is reduce * Users are able to get what they expect, which makes business meeting its targets achievable.Benefits of using usability testing are [6]: * Reduce maintenance costs, increase user fulfilment, and save on development eff ort cost. * Relevance feedback helps to increase the quality experience. * Measures activities, not preference. Users like to insist what they want, but by observing and measuring behaviour, we can understand what the best for their needs and support is. * Get a competitive benefit. User with no experience can give negative impact to a website. A systematic website is expected by users, especially when the competitors of websites developer are a lot now. Focus to the major needs and the aim of the application. Disadvantages of Usability Testing Usability testing provides many benefits, but there are the disadvantages in using this methodology, which should be noted. Firstly, testing is not 100% representative of the real life scenario. Also, usability testing is mainly qualitative, so does not provide the large samples of response that a questionnaire may, but the response can be far more precise and understandable. It requires a certain level of knowledge and experience to apply th e heuristics effectively. Trained usability experts are sometimes hard to find and can be expensive.Multiple evaluators are recommended and results must be aggregated. The evaluation may identify more minor issues and fewer major issues. [9] Conclusion Two common usability evaluation methods, heuristic evaluation and usability testing were analayse. Results found that heuristic evaluation performed by HCI experts revealed more general problems, while usability testing is a way to see how easy to use something is by testing it with real users. To provide the most effective and thorough usability evaluation results, a combination of usability evaluation techniques from both expert and system user perspectives is recommended. Task 2Introduction For task 2, I am going to evaluate a website from a bank which is OCBC bank. Description of the problem 1. The first problem i notice when I start to evaluate the website was the layout. the layout is merely merge to left side of the webpage. 2. The navigation bar size. It is too small. 3. The application do not provide â€Å"search† function for user to search information. 4. I didnt notice the login function,no idea where to login. The homepage there is no official login space. 5. No language selection option. 6. No register new user and login specific space at main page. 7. No sitemap to track location in application page. . The process option button and link appearance are not interesting and recognizable. 9. Once the user click any options from main page to proceed the next step,the application open new window. Description of the difficulties 1. User need to focus on the left side of the webpage whereelse it should be in the center for user to feel more comfortable when viewing webpage. The view is not standard as normal webpage. 2. The navigation bar font is too small and situated at the upper right corner of the webpage. It is not standard. These two diffrent navigation bar makes user confuse to choose which function to move in the application.Furthermore,when user click to login page,another window open and it does not have a standard navigation bar as the previous page. User will feel confuse,with diffrent thing appears. 3. The login function is not standard. There are several option to login to application. It suppose to have a official login space then allows user to choose the service they want to involve. the login space looks like advertistment and not noticable. 4. Language. User supposed can select the language they are prefereable for. But this application dont allow user to choose the language they understand.If the user dont understand,how they want to use the application. There is just the selection list of country only but not for language. 5. The application does not provide sitemap to track location of user that use the application at current time. Sitemap used to make user easy to navigate along the pages gone through. user can eesayly get back to the pages prevoiously without wasting time to find pages that they want. 6. The hyperlink should be as buttons or symbol button to make user easy to recognize and remember the direction to use the application for the next visit. . Once the user click any options from main page to proceed the next step,the application open new window.User cannot go back to the previous page because noâ€Å"back†function at the web page neither at the browser. If user want go back,user must exit the new window and restore the main page again. If user did some errors user need to repeat all the steps again. Heuristic and explanation 1. The small and not standard navigation bar is link to consistency and standards. * It suppose to gathered and user should select from a line of navigation bar. when user click to login page,another window open and it does not have a standard navigation bar as the previous page. * User feels a diffrent environment,diffrent situation word. 2. The no option for language is match between sy stem and real world. * The system should speaks user language,word or phrase which familliar with the user. This will make user more comfortable and understand what they are doing. 3. Layout of the web which go to left side is a aesthetic and minimalist design. * User should not wonder the blank space of the webpage. It should be used to put any related information. 4.No sitemap for user location is equal with flexibility and efficiency of use. * Sitemap can help the inexperience user to go back to previous page they were to make sure they are on the correct path to do their job. 5. Error prevention which dont have â€Å"back† button,is link with user control and freedom. * This sytem dont allow users to go back. It do no support undo and redo. * User need to navigate away from the page directly and go back to main page and access again the application. 6. Uninteresting link and button to continue using the application is a recognition rather than recall heuristic. The links navigation dont give the user idea when they first see the webpage. User need to read the links and then click to continue. * The link should suppose to be icons so that whenever the user come for next visit,they will recognize the buttons and save their memory load and time. Summary A heuristic evaluation is a usability inspection helps to identify usability problems in the user interface (UI) design, As I find in the OCBC Bank website, it specifically involves me examine the interface and judge its compliance with recognized usability principles (the â€Å"heuristics†).I independently examine a user interface and judge its compliance with a set of usability principles. The result of this analysis is a list of potential usability issues or problems. The usability principles, also referred to as usability heuristics, are taken from published lists. Ideally, each potential usability problem is assigned to one or more heuristics to help facilitate fixing the problem. As more ev aluators are involved, more true problems are found.But as from my view, I can list out only these usability problems and I also have stated the reasons and why it is a problem to me and also what is lacking at the webpage.

Discuss A Streetcar Named Desire Essay

Discuss A Streetcar Named Desire as a portrayal of a broken world. Hart Crane’s stanza, printed on the title-page of A Streetcar Named Desire, speaks of love’s voice as â€Å"an instant in the wind†. The last line goes on to allude to its transiency. Given that this is placed under the banner of a â€Å"broken world†, a play that otherwise boasts the subtlety of its imagery seems to get a rather blunt prefix. William’s intention was to create for Blanche a form of heroism. Here, on the first page, he pronounces love to be a dying entity. He tells us that it cannot be a permanent force within the vessel of human decision making. In the scenes that follow, he introduces a character positively wrestling with her internal nihilism, a character in contemplative turmoil about whether there is something more to her being-and her decisions. She quickly enters wholly hedonistic and materialistic surroundings, where her fading romanticism is contrasted with the apathetic humanism of Stanley. There is not, as the question suggests, a deep and deliberate flaw spanning the world of A Streetcar Named Desire. Indeed, such an assertion entirely misses the point. Perhaps it is a semantic difference, but the world depicted in the play is an entirely functional one; it is a world in which all the coherent parts play off each other, with both friction and cooperation. It is entirely incorrect to state that the lives of Stanley, Stella, Eunice, and Steve don’t continue from day-to-day with regularity and a certain degree of contentedness. That is not to say that Williams presents a situation that is either positive or, more crucially, hopeful. Instead, he examines twentieth century society as a great evolutionary mechanism: a fact, an absolute, in which Blanche constitutes an anomaly. Within this mechanism, base-line motivations act as stimuli for every moment of character action-and, as a reader, there is a temptation to focus on this. However, a more consequential realisation is that these hedonistic human pursuits-sex, monetary accumulation, power, and so on-also power the stasis of the play. Every moment of internal equilibrium is clamped in place by microcosm of personal economics and raw desire. The best illustrations of this come through Stella. Of course, her behaviour is often contrasted in blunt and unequal terms to Blanche’s melodrama. As a result of this, the passages between them exhibit a rather distorted sense of tension, whereby Stella’s consistency of view-point deflates the very conflict that Blanche is starting. Therefore, there is a tendency, particularly early in the play, to see Stella as a defeated character: there is aura of disconnection about her, as if the world has no effect on her: Blanche: And you are standing there smiling. Stella: What do you want me to do? B: Pull yourself together and face the facts. S: What are they, in your opinion? B: In my opinion? You’re married to a madman!

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]

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