Submitted By mcar

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Words 435

Pages 2

1. WHAT IS MIS?

MIS is short for management information system or management information services.

Management information system, or MIS, broadly refers to a computer-based system that provides managers with the tools to organize, evaluate and efficiently manage departments within an organization. In order to provide past, present and prediction information, a management information system can include software that helps in decision making, data resources such as databases, the hardware resources of a system, decision support systems, people management and project management applications, and any computerized processes that enable the department to run efficiently.

2. WHY IS MIS IMPORTANT FOR ORGANIZATION?

Because MIS provides several benefits to the business organization: the means of effective and efficient coordination between Departments; quick and reliable referencing; access to relevant data and documents; use of less labor; improvement in organizational and departmental techniques; management of day-to-day activities (as accounts, stock control, payroll, etc.); day-to-day assistance in a Department and closer contact with the rest of the world. MIS provides a valuable time-saving benefit to the workforce. Employees do not have to collect data manually for filing and analysis. Instead, that information can be entered quickly and easily into a computer program. As the amount of raw data grows too large for…...

...analysis was performed in two parts, using two different perspectives. The first part, preprocessing, used descriptive data analysis while the second part, exploratory analysis, used multivariate data analysis. 2.1. First part: data pre-processing During the first phase, descriptive analysis techniques were used to perform a data preprocessing. The objective was to normalize the data and construct a formal database. Thus, the first step was to run a treatment of missing data. As the practitioners’ were not obligated to answer all of the questions in the survey, in other words, the missing values in each variable are independent in the cases and occur randomly, without forecasts, the sample was identified as missing completely at random (MCAR) and for these cases, the treatment was to calculate the mean for each question, substituting in each case the missing value [28, 29]. The second step was to eliminate the outliers, which were the observations showing a substantial discrepancy toward others or were inconsistent or extreme compared to the other results [29]. In this case, we eliminated extremely positive (“6 - very high”) or negative (“1 - very low”) evaluations on a particular practice that disagreed when compared with the other cases. Finally, the third step was to perform descriptive diagnostics on the sample, such as extracting demographic and profile information, and verify if the sample size is valid for using multivariate techniques. 2.2. Second part:......

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...9% in the Norwegian Krone and somewhat in the Swiss Franc (3%), with the Pound clearly causing the most active risk (2.91%). Table 7. Currency risk decomposition for Country portfolio The factors grouped by sectors contributing most to total risk are Energy, Materials and Consumer Discretionary, with Metals and Mining being represented heavily. This is also reflected in MCAR, with Materials, Industrials and Information Technology contributing the most, and again Metals and Mining having a large impact. Active Risk management By reducing the weight in the factors with the highest positive MCAR as identified above, especially Materials, active risk could be reduced. The proceeds could then be invested in British stocks, as this would reduce the currency risk to the Pound, with a MCAR of currently -0.03%. In terms of style, investing more in Momentum and Size would reduce active risk by around 0.01% per country and style. All other MCAR values are virtually zero or positive, meaning that active risk could only be reduced by shifting out of highly positive MCAR factors and shifting into less positive MCAR factors. This analysis is vice versa true if active risk was meant to be increased instead. Risk Profile: Sector Portfolio vs. Benchmark The top 10 holdings of the portfolio are in the Industrials, Consumer Staples, Health Care, Telecommunication Services and Information Technology sectors. According to the economic analysis, both industrial and consumer staples are......

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...sales Rep | Privacy Policy 2 of 2 Friday, December 13 4:26 PM eschulte account log off PHYS 101 Exams UIUC Instructor Unit 3: Homework / Homework / Homework / Schulte, Elaine Student Homework: Hour Exam 3 Deadline: 100% until Thursday, November 21 at 8:00 AM Problems Print Assignment View Standard Exercise The Hydraulic Lift 1 2 3 4 The Mass and The Spring Standard Exercise The Pendulum Standard Exercise The Hydraulic Lift Standard Exercise Archimedes and the King's Crown Standard Exercise The Garden Hose Standard Exercise The Guitar String To work on your car you use a hydraulic lift as shown in the diagram. It confines a fluid with ρ = 1.81 kg/m3. Your car has mass Mcar = 739 kg . The lift piston has mass Mpiston = 171 kg. The input piston (on the left) is massless and has an area of 2.54 m2. The lift piston (on the right) has an area of 2.18 m2. Standard Exercise Standard Exercise The Intense Speakers 1) In Figure 1 the piston bases are at the same height. How are the pressures on lift side and the input side related? Plift = Pinput Plift < Pinput Plift > Pinput Submit Hide Solution The Speeding Car Standard Exercise Heating a Metal Strip Solution: This is a question of Pascal's Principle. Remember that F1/A1 = F2/A2. Thus Plift = Pinput. Standard Exercise Compressing a Gas 2) The lift piston base and the input piston base are at the same height. What force is required......

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...of fall [m] h = Damping distance [m] 24 E = (m.g.s) + (m.g.h) = 0 + (333.046 * 9.81 * 0.03) = 98.02Nm Damping Force = (Energy per stroke * correction force * 1000)/ Stroke = (98.02 * 2 *1000)/30 = 6534.36N --------------------------------------------------------------------------------------------------------------------------------------------------------------- Actuator Forces Assume mass of car = 2200kg speed of car = 100km/hr = 27.78m/s F1 Fact θ Time for car to stop = 3s Acceleration = 27.78/3 = 9.26m/s2 F1 = mcar * acar = 2200 * 9.26 = 20372N Let θ = 450 Fact * cosθ = F1 Fact * cos45 = 20372N Fact = 28810.36N Because there are 2 actuators, each actuator needs Fact/2 = 28810.36/2 = 14405.18N = 14.4kN 25 Bottom Actuator F2 CG y Fd Fact x mg x1 x/2 Assuming mass of car = 2200kg Speed of car = 100km/hr = 27.78m/s Time taken to reach desired speed = 5s Mass of person on seat = 80kg Acceleration = 27.78/5 = 5.556m/s2 F2 = mcar * acar = 2200 * 5.556 = 12223.2N Fact x – Fdx1 – mg(x/2) = F2y Fact(0.22) – (3267.18)(0.22) – (80)(9.81)(0.11) = (12223.3)(0.45) Fact = 28661.8N = 28.7kN 26 Detailed Drawings The design of the driving simulator is created by 3-Dimensional Modelling software. Our group has chosen to use Autodesk 3D Inventor Professional to model our conceptual design and run finite element analysis. The following are detailed drawings with explanations and problems encountered while designing the driving simulator: Final Design......

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...collision is elastic, so write the KE conservation equation, and substitute the results from above. Also note that 180.0 55.6o 50.0o 74.4o 1 2 vB vA mA sin m v 2 A A 1 2 m v 2 A A 1 2 mv 2 B B m v 2 A A mA vA o sin sin 2 mB vA mA sin mB sin 2 mB mA sin sin 2 2 2 20.0 u sin 55.6 2 sin sin 2 74.4 sin 2 50.0o 39.9 u 46. Use Eq. 7-9a, extended to three particles. 1.00 kg mA xA mB xB mC xC xCM mA mB mC 0.44 m 0 1.50 kg 0.50 m 1.10 kg 0.75 m 1.00 kg 1.50 kg 1.10 kg 47. Choose the carbon atom as the origin of coordinates. 12 u 0 16 u 1.13 10 mC xC mO xO xCM mC mO 12 u 16 u 48. Find the CM relative to the front of the car. mcar xcar mfront xfront mback xback xCM mcar mfront mback 10 m 6.5 10 11 m from the C atom. 1050 kg 2.50 m 2 70.0 kg 2.80 m 3 70.0 kg 3.90 m 1050 kg 2 70.0 kg 3 70.0 kg 2.74 m 49. Consider this diagram of the cars on the raft. Notice that the origin of coordinates is located at the CM of the raft. Reference all distances to that location. 1200 kg 9 m 1200 kg 9 m 1200 kg 9m xCM 1.04 m 3 1200 kg 6800 kg yCM 1200 kg 9 m 1200 kg 3 1200 kg 9m 6800 kg 1200 kg 9m 1.04 m y x © 2005 Pearson Education, Inc., Upper Saddle River, NJ. All rights reserved. This material is protected under all copyright laws as they currently exist. No portion of this material may be reproduced, in any form or by any means, without permission......

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...weiterführende Forschungsfragen, aber auch die Vielfalt der erhobenen Daten ermöglicht noch weitere Auswertungen: Anhand des Fünf-Faktorenmodell der Persönlichkeit können Zusammenhänge von persönlichen Ressourcen und Wohlbefinden untersucht werden und auch die EffortReward-Imbalance-Angaben, soziodemografischen Angaben und Gehaltsangaben sind noch nicht untersucht. Die fehlenden Werte fallen besonders mit Blick auf Gehalt – dies war eine optionale und keine obligatorische Angabe – ins Gewicht: Nur 53,2% der Teilnehmer haben hier eine Angabe gemacht. Fehlenden Werte wurden nicht mit SPSS durch den „ExpectationMaximization-Algorithmus“ (EM-Algorithmus) geschätzt (Dempster, Laird, & Rubin, 1977), da dieser über hervorragende Schätzeigenschaften für MCAR-Werte verfügt (Malhotra, 1987, S. 83; Schnell, 1986, S. 90-91). Beim EM-Algorithmus wird von multivariater Normalverteilung und einer einfachen Stichprobe ausgegangen, wobei Beale & Little (1975, S. 134-137) zeigen, dass die Annahme multivariater Normalverteilung nicht zwingend erforderlich ist. Die suffizienten Statistiken werden durch den Mittelwertvektor und die Kovarianzmatrix dargestellt. Im „Expectation-Schritt“ sollen die Erwartungswerte unter Schätzung des Vektors der Mittelwerte, sowie der Kovarianzmatrix gefunden werden. Im „Maximization-Schritt“ erfolgt die Schätzung der fehlenden Werte eines Falls durch lineare Regression aller vorhandenen Werte einer Variablen (Schnell, 1986, S. 90). Mit dem......

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...proportion of missing values Preparatory work – Missing values Missing data are a nuisance and can be a problem. For one, missing responses mean that the denominators for many analyses differ, which can be confusing and tiresome to explain. Also, analyses that involve multiple variables (e.g., coefficient alpha, crosstabulations, regression models) generally exclude an entire observation if it is missing a value for any variable in the analysis (this method is called listwise deletion ). Thus, an analysis involving 10 variables, even if each has only 5% missing values, could result in excluding as much as 50% of the dataset (if there is no overlap among the missing responses)! Moreover, unless data are missing completely at random (MCAR – equivalent to a pattern of missing data that would result from deleting data values throughout the dataset without any pattern or predilection whatever), then an analysis that makes no adjustment for the missing data will be biased, because certain subgroups will be underrepresented in the available data (a form of selection bias). Imputation for missing values - optional topic As theory, methods, and computing power have developed over the years, analytic methods for handling missing data to minimize their detrimental effects have improved. These _____________________________________________________________________________________________ www.epidemiolog.net © Victor J. Schoenbach 14. Data analysis and interpretation –......

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...Stock Market Performance: Literature on US Carve-outs ..............81 Table 9: Overall Hypotheses ..........................................................................................90 Table 10: Pillars of Rationales and Associated Hypotheses ...........................................91 Table 11: Data Sample: Number of Transactions.........................................................114 Table 12: Data Sample: Deal Size ................................................................................116 Table 13: Data Sample: 30 Largest Transactions .........................................................117 Table 14: Announcement: ACARs of Spin-offs and Carve-outs ..................................130 Table 15: Announcement: MCARs of Spin-offs and Carve-outs..................................132 Table 16: Announcement: ACARs in European Countries...........................................134 Table 17: Announcement: Level of Shareholder Protection and Origin of Law...........136 Table 18: Announcement: Alternative Methodologies .................................................137 Table 19: Announcement: Alternative Expected Returns.............................................139 Table 20: Announcement: Alternative Event Windows................................................140 Table 21: Announcement: ACARs of Spin-offs and Carve-outs without Outliers........140 Table 22: Announcement: Year by Year .......................................................................

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...customers. Ensures that those services are provided within the HQDA guidance, designated priorities, and approved CLS bands and coordinates with the IMCOM RD to change HQDA-approved CLS bands to either green, amber, or red. 10. Approves and submits the installation master plan consistent with HQDA long-range plans and goals through the ACOMs, ASCCs or DRUs, and IMCOM. For IMCOM installations the SC collaborates with the IMCOM RD before the SC submits the installation master plan. AR 600–20 • 6 November 2014 7 11. Approves the military construction, Army (MCA) and military construction, Army Reserve (MCAR) project priority list at the installation level. For IMCOM installations the SC collaborates with the IMCOM RD before the SC approves the MCA and MCAR project priority list for the installation. The U.S. Army Corps of Engineers executes MCA and/or MCAR projects for the Army. 12. Reviews and approves the prioritization of Family and installation programs. For IMCOM installations the SC collaborates with the IMCOM RD before the SC approves Family and installation programs for the installation. 13. Installation force protection (FP) is as follows: (a) continental United States (CONUS) SC: as directed by U.S. Army North (USARNORTH) and in coordination with the installation management headquarters (IMCOM and NonIMCOM), oversees FP on the installation;(b) outside continental United States (OCONUS) SC: in coordination with the ASCC and IMCOM is responsible......

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...Mean Cumulative Average Returns (MCARs). Step 5: Mean Average Returns The mean average returns (MARs) are calculated by averaging the ARs for the 86 portfolios for the 86 months. Where, AR(1,1) and AR(1,86) are the Average Returns for the 1 month of the holding period for the 1st and 86th portfolios respectively. AR (12,1) and AR(12,86) are the Average Returns for the 12stmonth of the holding period for the 1st and 86th portfolios respectively. Test of Significance MCARW (MCARL) indicates how much cumulated returns stocks in the winner (loser) portfolio earn on an average during 12 months in test period. If markets are efficient but weak then MCARW minus MCAR = 0 The momentum hypothesis implies that MCARw minus MCAR >0 The two tests -- MCAR and MAR are used to test the hypothesis. The test of MCAR verifies significance of momentum returns and show if the returns grow stronger or are reversed at some stage during holding period on a cumulative basis. The test of MAR helps one to identify on a monthly basis whether the momentum returns are getting built up or reversed. Results and Analysis We have analysed the results of the 6 x 12 strategy using MCAR and MAR tests Mean Cumulative Average Returns Test As shown below, the winner portfolio delivers a 1.64 percent return in the first month of testing period that goes on increasing to 12.14 percent in the 12th month of the testing period. Similarly, the MCAR of loser portfolio in the 1st month...

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...maximum-likelihood methods are proven to produce unbiased estimates of the population values, thus improving both the accuracy and often the statistical power of results. AMOS 5.0, which is used in this study, represents a direct approach that is based on maximum likelihood (ML) estimation (Byrne, 2001; Arbuckle, 1996). Byrne (2001) demonstrated that despite 25 percent data loss in a sample, the overall 2 and the goodness-of-fit statistics such as RMSEA and CFI are relatively close. These findings provide strong evidence for the effectiveness of the direct ML approach in addressing the problem of missing data values. The strength lies in the consistency and efficiency of ML estimates when the unobserved values are Missing Completely At Random (MCAR), provides unbiased estimates when the unobserved values are Missing At Random (MAR) and ML estimates provide the least bias when the missing values are Non-ignorable Missing At Random (NMAR) (Byrne, 2001; Enders, 2001). However as noted earlier, the missingness of data in this study was because certain measures simply did not apply to particular respondents. Therefore, literally there are no missing data in this problem. As according to Schafer & Graham (2002), if responses to these measures were available from some other respondents, the observations may denote responses for those who claimed that the measures were applicable and the missing ones represents hypothetical responses for those who think that the measures were not......

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...-0.02855 3.72257 -2.86618 -0.46862 1.92911 -9.6252 -0.1576 1.0372 MCAR -1.77592 -0.26729 -0.47102 -2.76028 -0.70467 -0.86513 -9.0945 -0.4797 -1.0807 Results Discussion: To examine the order of integration here the DF, the ADF and PP tests are applied to the variables per capita real GDP (PCGDP), growth rate of real GDP (GRATE), total market capitalization (MCAP) and ratio of market capitalization to real GDP (MCAR). From the estimated results of these three tests, it has been found that for all variables, the null hypothesis of unit root can not be rejected at 5% level of significance. Thus it can be concluded that all the variables are non-stationary. Thus, the innovation of these series will be permanent and have a long-run effect for economic development in Bangladesh economy. Also, it is very much necessary to enquiry the second order unit root in each series. The test results for the second order unit root are reported below; Table 2: The Dickey-Fuller (DF), Augmented Dickey-Fuller (ADF) and Philips-Perron (PP) Tests Results for 2nd Order Unit Root 5 DF Test ADF Test PP Test Case 2 Case 3 Case 1 Case 2 Case 3 Case 1 Case 2 Case 3 -4.9218* PCGDP 3.6970* 2.2826** 4.3536* 2.0699** 26.7848** 14.8666** 10.1636* -7.3385* -5.9808* -5.0346* -40.8074* -40.3329* GRATE -7.3047* 7.3806* 5.4913* 39.5603* -2.6463* MCAP 3.7878** 3.8557* 2.89357* 3.8918** 3.9693* 22.8793** 22.7684** 14.7232* -3.1452* -2.944* -22.8290* MCAR 3.7921** 3.8632* 3.8967** 3.9704* 22.8948** 16.8294* Results......

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