Why Everybody Is Talking About Essay Topics Exam Practice...The Simple Truth Revealed

Why Everybody Is Talking About Essay Topics Exam Practice...The Simple Truth Revealed Life After Essay Topics Exam Practice You may also get a variety of discounts on our site which will help you to save some more money for future orders or anything you want to spend them on. Our customer support will gladly tell you whether there are any special offers at the present time, and make sure you are getting the very best service our company may deliver. When a business recognizes the part of supplies used over the course of a calendar year, the result is to decrease net income. A business in the procedure for liquidation is thought to be under the going concern assumption. The Ultimate Essay Topics Exam Practice Trick A seasoned professional will make an error-free assignment very quickly and can help you boost your grades. You should have your reasons, and our primary concern is that you wind up getting a great grade. There is an important problem of. 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It is very important to present a very clear standpoint and offer enough arguments to back up your viewpoint. Examine both views and provide your opinion. Examine both views and provide your own opinion.

The capital asset pricing model - Free Essay Example

Sample details Pages: 9 Words: 2800 Downloads: 4 Date added: 2017/06/26 Category Economics Essay Type Research paper Did you like this example? Introduction The Capital Asset Pricing Model (CAPM) is being used since the 1960s   to measure portfolio performance and to calculate the cost of capital.   In the 1990s Eugene Fama and Kenneth French tried to improve the performance of the CAPM by adding two factors to the model. The first factor is the book-to-market ratio of stocks in the portfolio and the second factor is the stocks underlying company size. This model was to be superior to the CAPM. Don’t waste time! Our writers will create an original "The capital asset pricing model" essay for you Create order However, Graham and Harvey (2001) proved that in 2001 the CAPM was still used by 73,5% of the U.S. CFOs to calculate the cost of capital and portfolio performance. In Europe, Brounen, de Jong and Koedijk (2004) showed that this percentage was still 45%. Why do CFOs still rely on an inferior model, or isnt the Fama French Three Factor Model superior to the CAPM? The goal of this paper is to determine whether the CAPM or the Fama French Three Factor Model is superior to one another in size and book-to-market portfolios.   Theory The Capital Asset Pricing Model The Capital Asset Pricing Model is a model that describes the relationship between risk and expected return. It is mainly used by investors to value assets and to determine expected returns. The main idea behind the model is that investors need to be compensated for their exposure to systematic risk, because not all of the investments are truly risky. By diversifying a portfolio, it is possible to reduce risk. The risk reduced is called the Risk Free Rate. In other words, the expected return of a security or a portfolio is formed by the risk free rate plus a risk premium. The CAPM by Sharpe (1964) and Lindtner (1965) is in fact an extension of the one period mean variance portfolio models of Tobin (1958) and Markowitz (1959). It builds on the model of portfolio choice, where an investor selects a portfolio that minimizes the variance of portfolio return and maximizes the expected return, given this variance. This is often referred to as the â€Å"mean variance model†. The CAPM is in fact a prediction test about the coherence between risk and expected return and identifies a portfolio that is efficient if asset prices are to clear the market of all assets. The two added assumptions to this mean variance model identify a mean-variance-efficient portfolio are â€Å"complete agreement† and â€Å"borrowing and lending at risk free rate†. Complete agreement stands for the agreement amongst investors on the joint distribution of asset returns from which the returns we use to test the model are drawn. Borrowing and lending at a risk free rate exists and is equal for all investors and does not depend on the amount borrowed or lent. The relationship between risk and the expected return for portfolios is apparent. The higher the risk, the higher the payoff and vice versa according to Fama and French (2004). The Sharpe-Lindtner CAPM equation: E(Ri) = Rf + bim * (E(Rm) Rf) Where E(Ri) is the expected return on asset i, Rf is the risk-free rate, bim is the market beta of asset i, E(Rm) is the expected return on the market. Where bim consists of the following factors: bim = cov(Ri, Rm)/s2(Rm) Where cov(Ri, Rm) is the covariance risk of asset i in m and s2(Rm) is the variance of the market return. In words, the expected return on asset i is the risk-free rate (Rf) times the unit premium of beta risk, E(Rm) Rf. The expected return on an investment portfolio is equal to the weighted average of all of the assets expected returns in the portfolio, thus is linearly combined. The standard deviation of a portfolio is nonlinearly combined, because of the diversification of risk that takes place when a portfolio is formed as seen in Figure 1. For example, when a portfolio of two equally risky assets is formed, with equally expected returns, the expected return on the portfolio will be equal to the expected return of one of the assets, though the standard deviation of the portfolio will be lower than the standard devia tion of each of the underlying assets because of the diversification effect. In other words, diversification leads to a risk reduction without diminishing the expected return. Figure 1 describes the various portfolio possibilities and explains the CAPM further. On the horizontal axis portfolio risk is set and on the vertical axis expected return is set. The curve is called the minimum variance frontier, and is a line that describes the minimum variance at different risk levels of multiple risky portfolios, when there is no opportunity of risk free borrowing. For example, when an investor is looking for a high expected return, this automatically brings a high risk along with it. The optimal choice of a portfolio for an investor lies on the minimum variance frontier, since it maximizes the expected return for a given volatility. By adding the opportunity to borrow at a risk free rate, the mean variance efficient frontier comes into being. This is now the efficient set, in stead of the minimum variance frontier according to Fama and French (2004). The Pros And Cons Of The Capital Asset Pricing Model The main advantage of the CAPM over any other pricing mode is its simplicity to use. However, there are some anomalies. During the 80s and 90s, several deviations in the CAPM were discovered which show anomalies in the CAPM and question its correctness. According to Basu (1977) future returns on high Earnings to Price ratios are in reality higher than the ratios predicted by the CAPM. Banz (1981) finds that the low market value stocks actually had a higher return than predicted by the CAPM. Bhandari (1988) showed that leverage high debt equity stocks had returns that were too high relative to their betas according to the CAPM. He also shows that there is a positive relation between between leverage and average return in the CAPM. Leverage could be associated with risk and expected return, but according to Bhandari (1988), the leverage risk should be explained by the market b. The Fama French Three Factor Model. Fama and French (1992) criticize the empirical adequacy of the CAPM and claim to improve the model by adding two empirically based factors. The factors are a product of empirical data research and there is no underlying theory to explain these factors. Of all the researched factors, these turn out to be the most effective. Fama and French (1992) used the cross-sectional regression approach of Fama and MacBeth (1973) to show that b doesnt suffice to explain average return. Size captures differences in average stock returns where b misses them. To improve the predictive value of the CAPM they added two factors to the model. According to the model the risk premium or excess return above the risk free rate is a composition of three factors, namely v The risk premium on the market portfolio (Rm RF). v The difference in returns between the small stock portfolios and the big stock portfolios (SMB). v The difference in returns between the high book to market stock portfolios and the low book to market stock portfolios (HML). The Fama and French Three Factor Model: E(Ri) Rf = bi * (E(Rm) Rf) + si E(SMB) + hi E(HML) Where: E(Rm) is the expected return on the market, Rf is the risk free rate,   E(SMB) and E(HML) are the expected premiums and bi, si and hi are the regression slopes. The first factor is Small Minus Big (SMB) which is designed to measure the excess return investors have historically received by investing in company stocks with small market capitalization. This excess return is most commonly known as the ‘size premium . Fama and French (2006) compose six value weight portfolios, SG, SN, SV, BG, BN, and BV. They state that: â€Å"The portfolios are intersections stocks of NYSE, AMEX (after 1962) and Nasdaq (after 1972) into two size groups, Small and Big and three book to market (B/M) equity groups Growth (firms in the bottom 30% of NYSE B/M), Neutral (middle 40% of NYSE B/M) and Value (high 30% of NYSE B/M). † The SMB factor is the average returns on the small stock portfolios minus the average returns on the big portfolios according to Fama and French (2006) and is computed as follows: SMB =1/3 (Small Value + Small Neutral + Small Growth) 1/3 (Big Value + Big Neutral + Big Growth) Where: Small Value are the firms with the June market cap below the NYSE median that are at the high 30% of NYSE B/M. Small Neutral are the firms with the June market cap below the NYSE median that are at the middle 40% of NYSE B/M. Small Growth are the firms with the June market cap below the NYSE median that are at the bottom 30% of the NYSE B/M. Big Value are the firms with the June market cap value above NYSE median that are at the high 30% of NYSE B/M. Big Neutral are the firms with the June market cap value above NYSE median that are at the middle 40% of NYSE B/M. Big Growth are the firms with the June market cap value above NYSE median that are at the bottom 30% of NYSE B/M. When thi s value is positive, the small caps have outperformed the large caps in the particular month and vice versa. The second is High Minus Low (HML) which is designed to measure the ‘value premium   investors get for investing in high book-to-market companies. The HML factor is the average returns on value portfolios minus the average returns on growth portfolios according to Fama and French (2006) and is computed as follows: HML =1/2 (Small Value + Big Value)- 1/2 (Small Growth + Big Growth) When this value is positive, the growth stocks outperformed the value stocks in that particular month and vice versa. The market risk premium (Rm-Rf) that is used is the value to weight return on all NYSE, AMEX and Nasdaq stocks diminished by the one-month Treasury bill rate. The Pros Aand Cons Of The Three Factor Model The additional two factors in the Fama and French Three factor model are purely empirical. There is no underlying theory as there exists in the CAPM. Though the three factor model needs additional date compared to the CAPM (the SMB factor and the HML factor) the higher costs in using the three factor model compared to the CAPM is not justified according to Bartholdy (2002), because the three factor model doesnt seem to outperform the CAPM significantly on individual stock returns estimation. The Momentum Factor Carhart (1997) adds another factor to the equation, creating his Four Factor Model. This fourth factor describes the effect of Jegadeesh and Titmans (1993) one year momentum anomaly. This model is a market equilibrium-based four-risk factor model. Carharts factor (PR1YR) brings the one-year momentum return to the equation, which enlarges the explanation of the model compared to the three factor model, so the fourth factor substantially improves the performance of the model according to Carhart (1997). In the three factor model, errors concerning last years stock portfolios are observably reduced. Carhart managed to reduce most of the patterns in pricing errors. This indicates a well performing model on describing cross sectional variation in average stock returns. The Carhart four factor model: E(Ri) Rf = bi * (E(Rm) Rf) + si E(SMB) + hi E(HML) + pi E(PR1YR) Research Questions And Hypotheses Which of the two models, the CAPM or the Three Factor Model, is superior to one another for the different portfolios of size and book-to-market in the time period between 1993 and 2009? Hypotheses: 1. The Three Factor model is superior in predicting the value of securities in the portfolio size in the time period 1993-2009. 2. The Three Factor model is superior in predicting the value of securities in the portfolio book-to-market in the time period 1993-2009. 3. Not all the factors of the three factor model are contributing to the adequacy of the model in valuing the portfolios size and book-to-market in the time period 1993-2009. 4. The momomentum factor helps explain returns. Methodology And Data Collection Data This paper uses the data gathered by Fama and French and published on the website of Kenneth R. French. According to Fama and French (1992) this data consists of all non-financial firms in the intersection of the NYSE, AMEX and NASDAQ files and the merged COMPUSTAT annual industrial files of income statement and balance sheet data, maintained by the Center for Research in Security Prices (CSRP). They have excluded the financial firms from their dataset because high leverage in these firms is quite normal in opposition to high leverage in non-financial firms. In non-financial firms this high leverage could possibly indicate distress. In the Small-Minus-Big factor in the Three Factor Model, accounting variables play a role, and to ensure that these accounting variables are known before the returns they are used to explain, the accounting data for all fiscal year-ends t, are calculated after a minimum of 6 months has passed the fiscal year-end. Estimating Market This paper uses the market b provided by Fama and French and published on the website of Kenneth R. French. The asset pricing tests performed in this paper use the cross sectional regression approach of Fama and MacBeth (1973). The market bs for portfolios are more precise than the individual bs, so the approach of Fama and MacBeth (1973) is to estimate the portfolios b and then assign this b to each stock in the underlying portfolio. By using this method, the useage of individual stocks in the asset-pricing tests of Fama and MacBeth is enabled. Methodology SPSS 15 will be used for the data analysis in this paper, which will consist of multivariate regression analysis to answer the research question and hypotheses. To determine whether the two additional factors of the Three Factor Model add extra predictive value to the CAPM, this paper uses multiple linear regression in SPSS to see if the R-squared value increases with these additional factors for each of the portfolios. . In the data library section on their site, Fama and French provide data about portfolios formed on size and portfolios formed on book-to-market. To investigate the size and book-to-market factors in the Three factor model, the data of these portfolios are used in the regression analysis. Literature v Brounen, D. Abe de Jong and K. Koedijk, 2004, Corporate Finance in Europe Confronting Theory with Practice, Financial Management 33, 71- 101. v Graham, J.R. and C.R. Harvey, 2001, The Theory and Practice of Corporate Finance: Evidence from the Field, Journal of Financial Economics 60, 187-243. v Litner, J., 1965, The Valuation of Risk Assets and the Selection of Risky Investments in Stock Portfolios and Capital Budgets, Review of Economics and Statistics 47, 13-37. v Sharpe, W.F., 1964, Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk, Journal of Finance 19,425-442. v Tobin, J., 1958, Liquidity Preference as Behaviour Toward Risk, Review of Economic Studies 25, 65-86. v Fama, E.F. and K.R. French, 2006, The Value Premium and the CAPM, Journal of Finance 61, 2163-2185. v Fama, E.F. and K.R. French, 2004, The Capital Asset Pricing Model: Theory and Evidence, Journal of Economic Perspectives 18, 25-46. v Fama, E.F. and K.R. French, 1 996, â€Å"The CAPM is Wanted, Dead or Alive†, Journal of Finance 51, 1947-1958. v Fama, E.F. and K.R. French, 1992, â€Å"Common risk factors in the returns on stocks and bonds†, Journal of Financial Economics 33, 3-56. v Fama, E.F. and K.R. French, 1992 â€Å"Cross-section of expected stock returns†, Journal of Finance 47, no 2, 427-465. v Fama, E.F. and K.R. French, 1997 â€Å"Industry cost of equity†, Journal of Financial Economics 43, 153-193. v Markowitz,H. 1952. Portfolio Selection. Journal of Finance.7:1, pp. 77-99. v Markowitz, Harry. 1959. Portfolio Selection: â€Å"Efficient Diversification of Investments† Cowles Foundation Monograph No. 16. New York, John Wiley Sons Inc. v Fama, E.F. and K.R. French, 2010 â€Å"Data Library† https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html v Basu, S., 1977, Investment Performance of Common Stocks in Relation to Their Price-Earnings Rations: A Test of the Efficient Market Hypothesis†, Journal of Finance 12, 129-156. v Banz, R.W., 1981, The Relationship between Return and Market Value of Common Stocks, Journal of Financial Economics 9, 3-18 v Bhandari, L., 1988, Debt/Equity Ratio and Expected Common Stock Returns: Empirical Evidence, Journal of Finance 43, 507-528 v Bartholdy, J. 2002, â€Å"Estimation of Expected Return: CAPM vs Fama and French†, Working paper series https://ssrn.com/abstract=350100 Nog te verwerken: Inleiding Smb gaat long in kleine aandelen en short in grote Fama, E.F. and K.R. French, 2004, The Capital Asset Pricing Model: Theory and Evidence, Journal of Economic Perspectives 18, page 27 Fama, E.F. and K.R. French, 2006, The Value Premium and the CAPM, Journal of Finance 61, p. 2166. https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html

Critique of a Research Article about Incarceration in the...

Background In a study of children that had a family member or family associate incarcerated prior to their 18th birthday, Loper Nichols (2012) attempted to address the impact that such incarceration had on such children. It was expected, consistent with previous literature, that household incarceration would have an impact on academic outcomes. The purpose of the study was to evaluate the relationship between household incarceration and two outcomes: failure to graduate high school and extended school absence. Loper and Nichols (2012) examined three factors: 1. If youth with incarcerated household members experienced more social and economic adversity and worse school outcomes than the rest of the sample. 2. Whether household member†¦show more content†¦Furthermore, this influence is not only related to the parents’ influence, but is extended to other close relationship’s in the child’s household. Other theories that may explain how incarceration has an impact on children are strain, attachment, social control, and stigma. Loper and Nichols hypothesized that household incarceration would have a great impact on academic outcomes of the children in the household. Methods There were several relevant variables used in this study. The control variables used were demographic, socioeconomic, and other adversity variables such as sex, ethnicity, poverty status, mother’s educational attainment, cognitive ability, and home environment quality. The dependent variables in this study were related to academic outcomes such as extended absences and failure to graduate from high school. The independent variables used were parental incarceration, sibling incarceration, and other household member’s incarceration (Loper and Nichols, 2012). The study by Lopers and Nichols was a longitudinal, study design using data from the National Longitudinal Survey of Youth, Child and Youth survey (NLSY 2010), which included women and their children. According to Lopers and Nichols, â€Å"Out of the sample, 585 met criteria for the household incarceration status, to be compared to 2,753 individuals who did not experience household incarceration† (p. 5). Furth ermore, itShow MoreRelatedThe Effects Of Parental Incarceration On Children3942 Words   |  16 Pages The Effects of Parental Incarceration on their Children Darlene Oliver May 4, 2016 The University of the District of Columbia Introduction The number of children with incarcerated parents continues to increase, thus the long-term ramifications of parental incarceration has become a topic of interest to many concerned people. In addition to understanding the effects of parental incarceration on children, school officials and penal institutions must be involved

Data Warehouse Case Study free essay sample

History of the CDR When the project began in 1995–96, the CDR, initially referred to as the â€Å"clinical research database,† was intended to support and enhance clinical research at the University of Virginia by providing clinicians, students, and researchers with direct, rapid access to retrospective clinical and administrative patient data. Re? ecting this intent, the system was funded by the School of Medicine and housed in the Academic Computing Health Sciences group, which is distinct from the medical center’s IT group. With considerable assistance and cooperation from data owners and stewards, legacy data from several different sources were loaded into a single relational database and periodically updated. Authorized users accessed the CDR through a standard Web browser and viewed or downloaded data to their personal computers for further analysis. Initially, emphasis was placed on getting the CDR running as quickly as possible and with a minimum of resources; consequently, extensive transformation of data to an enterprise data model was not performed. The CDR project team consists of 2. 5–3. 0 FTEs (full-time equivalents)— one developer, one developer-database administrator, and portions of analyst, clinician, and administrative FTEs. To date, the costs of developing and operating the CDR have been approximately $200,000 per year, underwritten by the School of Medicine. Over the course of the project, there have been signi? cant enhancements to the user interface, incorporation of additional data sources, and the development of an integrated data model. There has also been increasing interest in using the CDR to serve a broader audience than researchers and to support management and administrative functions—â€Å"to meet the challenge of providing a way for anyone with a need to know—at every level of the organization—access to accurate and timely data necessary to support effective decision making, clinical research, and process improvement. In the area of education, the CDR has become a core teaching resource for the Department of Health Evaluation Science’s master’s program and for the School of Nursing. Students use the CDR to understand and master informatics issues such as data capture, vocabularies, and coding, as well as to perform Case Study: A Data Warehouse for an Academic Medical Center 167 exploratory analyses of healthcare questions. Starting in Spring 2001, the CDR will also be introduced into the university’s undergraduate medical curriculum. System Description Following is a brief overview of the CDR application as it exists at the University of Virginia. System Architecture. The CDR is a relational data warehouse that resides on a Dell PowerEdge 1300 (Dual Intel 400MHz processors, 512MB RAM) running the Linux operating system and Sybase 11. 9. 1 relational database management system. For storage, the system uses a Dell Powervault 201S 236GB RAID Disk Array. As of October 2000, the database contained 23GB of information about 5. 4 million patient visits (16GB visit data, 7GB laboratory results). Data loading into Sybase is achieved using custom Practical Extraction and Report Language (Perl) programs. CDR Contents. The CDR currently draws data from four independent systems (see Table 1). In addition, a number of derived values (for example, number of days to next inpatient visit, number of times a diagnostic code is used in various settings) are computed to provide summary information for selected data elements. Data from each of these source systems are integrated into the CDR’s data model. In addition to the current contents listed in Table 1, users and the CDR project team have identi? ed additional data elements that might be incorporated Table 1. Contents of the CDR Type of Data Inpatient, outpatient visits Source of Data Shared Medical Systems Description Patient registration and demographic data, diagnoses, procedures, unit and census information, billing transactions, including medications, costs, charges, reimbursement, insurance information Physician billing transactions from inpatient and outpatient visits, diagnoses, and procedures Laboratory test results Available Dates Jul 1993–Jun 2000 Professional billing Laboratory results Cardiac surgery IDX billing system HL-7 messages from SunQuest Lab System Cardiac surgery outcomes data (de? ned by Society of Thoracic Surgeons Oct 1992–Jun 2000 Jan 1996–Jun 2000 Clinical details for thoracic surgery cases Jul 1993–Jun 2000 168 Einbinder, Scully, Pates, Schubart, Reynolds into the CDR, including microbiology results, discharge summaries (and other narrative data), outpatient prescribing information, order entry details, and tumor registry information. As of October 2000, we have just ? nished incorporating death registry data from the Virginia Department of Health into the CDR. These data will provide our users with direct access to more comprehensive mortality outcomes data than are contained in local information systems, which generally are restricted to an in-hospital death indicator. User Interface. The user interface runs in a standard Web browser and consists of a data dictionary, a collection of common gateway interface (CGI) programs implemented using the â€Å"C† programming language, and JavaScriptenabled HTML pages. Structured query language (SQL) statements are generated automatically in response to point-and-click actions by the user, enabling submission of ad hoc queries without prior knowledge of SQL. The SQL queries are sent to the CGI programs that query the database and return results in dynamically created HTML pages. The entire process is controlled by the contents of the data dictionary, which is used to format SQL results, set up HTML links for data drill-down, and provide on-line help. Data may be downloaded immediately into Microsoft Excel or another analysis tool on the user’s workstation. Query Formulation. Most CDR users use the Guided Query function to retrieve data. This process involves three steps: 1. De? ne a population of interest by setting conditions, for example, date ranges, diagnostic codes, physician identi? ers, service locations, and lab test codes or values. 2. Submit the query, specifying how much data the CDR should return (all matching data or a speci? ed number of rows). 3. After the CDR returns the population of interest, use the Report Menu to explore various attributes of the population on a case-by-case or group level. Custom reports can also be de? ned, and the results of any report can be downloaded into Microsoft Excel, Access, or other analysis tool. Generally, the query process requires several iterations to modify the population conditions or report options. In addition, â€Å"browsing† the data may help the user generate ideas for additional queries. We believe that it is helpful for end users to go through this query process themselves—to directly engage the data. However, many users, especially those with a pressing need for data for a meeting, report, or grant, prefer to use CDR team members as intermediaries or analysts. To date, we have attempted to meet this preference, but as query volume increases, our ability to provide data in a timely manner may fall off. Security. A steering committee of clinicians guided the initial development of the CDR and established policies for its utilization and access. Only authorized users may log onto the CDR. To protect con? dentiality, all patient and physician identifying information has been partitioned into a â€Å"secure† Case Study: A Data Warehouse for an Academic Medical Center 169 database. Translation from or to disguised identi? ers to or from actual identi? ers is possible but requires a written request and appropriate approval (for example, from a supervisor or the human investigations committee). All data transmitted from the database server to the user’s browser are encrypted using the secure Netscape Web server, and all accesses to the database are logged. In addition, CDR access is restricted to personal computers that are part of the â€Å"Virginia. edu† domain or that are authenticated by the university’s proxy server. Evaluation Understanding user needs is the basis for improving the CDR to enable users to retrieve the data independently and to increase usage of the CDR at our institution. Thus, assessing the value of the CDR—how well we meet our users’ needs and how we might increase our user base—has been an important activity that has helped guide planning for changes and enhancements and for allocation of our limited resources. Efforts to evaluate the CDR have included several approaches: †¢ Monitoring user population and usage patterns †¢ Administering a CDR user survey †¢ Tracking queries submitted to the CDR and performing follow-up telephone interviews Usage Statistics. Voluntary usage of an IS resource is an important measure of its value and of user satisfaction. 5 However, usage of a data warehouse is likely to be quite different than for other types of information resources, such as clinical information systems. A clinical system is likely to be used many times per day; a data warehouse may be used sporadically. Thus, although we monitor system usage as a measure of the CDR’s value, we believe that frequency of usage cannot be viewed in isolation in assessing the success of a data warehouse. Since the CDR went â€Å"live,† more than 300 individuals have requested and obtained logon IDs. As of September 30, 2000, 213 individuals had logged on and submitted at least one query. This number does not include usage by CDR project team members and does not re? ect analyses performed by team members for end users. Figure 1 shows the cumulative number of active users (those who submitted a query) and demonstrates a linear growth pattern.