...Overall, Residual Dreams is a well produced and well acted short film, with several strong aspects that make it stand out in the world of student films. These aspects primarily include locations and acting. However, some aspects, primarily choreography and, at times, acting, revealed it to be a less than professional endeavor . By far this film’s strongest aspect was its locations. The gallery space at the beginning is beautiful. It is light and airy, a perfect contrast to the break up that occurs within it. The bar location was also wonderfully selected. It was spacious and visually interesting for a scene with fairly simple camera work. My favorite location was definitely the abandoned lot or construction space (I could not quite tell which...
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...CONTROLLING THE RATE OF RETURN Return on investment is normally used to judge the managerial performance in an investment center. Managers therefore try to control and improve the ROI of their investment center. Rate-of-return regulation was used most regularly to determine reasonable prices for goods supplied by utility companies. This regulation is considered fair due to the fact that they give the company the opportunity to recover costs incurred by providing consumers with their goods or services while simultaneously protecting consumers from paying exorbitant prices that would provide these companies with monopolistic profits. Under this method of regulation, government regulators examine the firm's base rate, cost of capital, operating expenses, and overall depreciation in order to estimate the total revenue needed for the firm to fully cover its expenses. The goal of rate-of-return regulation is for the regulator to evaluate the effects of different price levels on potential earnings for a firm in order for consumers to be protected while ensuring investors receive a "fair" rate of return on their investment. An investment center manager can improve ROI in basically three ways. To illustrate how an investment center manager can improve ROI by making the use of three methods mentioned above consider the following example: Example: The following data represents the results of an investment center of the operations of a company for the most recent month. Net operating...
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...glm(formula = count ~ persons + child + factor(camper), family = poisson) Deviance Residuals: Min 1Q Median 3Q Max -6.8096 -1.4431 -0.9060 -0.0406 16.1417 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -1.98183 0.15226 -13.02 <2e-16 *** persons 1.09126 0.03926 27.80 <2e-16 *** child -1.68996 0.08099 -20.87 <2e-16 *** factor(camper)1 0.93094 0.08909 10.45 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 2958.4 on 249 degrees of freedom Residual deviance: 1337.1 on 246 degrees of freedom AIC: 1682.1 >plot(ftv,res1,ylim=c(-7,17),xlab="Fitted Values",ylab="Deviance Residuals",main="Deviance Residuals-Posiion Model") > qqnorm(res1) Number of Fisher Scoring iterations: 6 > round(cbind(exp(m1$coeff),exp(cbind(m1$coeff-qnorm(0.975)*sqrt(diag(vcov(m1))),m1$coeff+qnorm(0.975)*sqrt(diag(vcov(m1)))))),2) [,1] [,2] [,3] (Intercept) 0.14 0.10 0.19 persons 2.98 2.76 3.22 child 0.18 0.16 0.22 factor(camper)1 2.54 2.13 3.02 b) > res1<-residuals.glm(m1,"deviance") > ftv<-m1$fitted.values plot(ftv,res1,ylim=c(-7,17),xlab="Fitted Values",ylab="Deviance Residuals",main="Deviance Residuals-Posiion Model") c) round( cbind( + unlist(lapply(split(count, factor(camper):factor(child))...
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...:28451 | Max. :225.00 | Max. :260.00 | Max. :68549 | 2) a) Yes, The p-value is 9.72e^-12. Much lower than Tyler’s 10% significant level. | Value | Prediction | Lower | Upper | 1 | Minimum | 18809.35 | 14777.04 | 22841.66 | 2 | Mean | 23063.73 | 19182.71 | 26944.74 | 3 | Max | 26739.02 | 22744.55 | 30733.49 | b) c) See above d) Greater fuel volumes could translate as greater number of customers. With greater numbers visiting the gas station, there is a greater chance the customer will visit the store. 3) Residuals: Min 1Q Median 3Q Max -4955.8 -1750.4 -232.4 1464.2 4730.6 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 22142.385 275.018 80.513 < 2e-16 *** TV 12.193 3.874 3.147 0.00219 ** Radio 5.195 2.700 1.924 0.05726 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2138 on 98 degrees of freedom Multiple R-squared: 0.2544, Adjusted R-squared: 0.2391 F-statistic: 16.72 on 2 and 98 DF, p-value: 5.673e-07 a) Yes, they both fall under Tyler’s 10% level of significance. Value | Prediction | Lower | Upper | TV=40, Radio =80 | 23045.72 | 18782.89 | 27308.54 | | tv | radio | Amount | 300 |...
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...http://www.eco.uc3m.es/~jgonzalo/teaching/EconometriaII/cointegration.htm Cointegration and Error Correction Definition: If there exists a stationary linear combination of nonstationary random variables, the variables combined are said to be cointegrated. | | The old woman and the boy are unrelated to one another, except that they are both on a random walk in the park. Information about the boy's location tells us nothing about the old woman's location. | | The old man and the dog are joined by one of those leashes that has the cord rolled up inside the handle on a spring. Individually, the dog and the man are each on a random walk. They cannot wander too far from one another because of the leash. We say that the random processes describing their paths are cointegrated. | The notion of cointegration arose out of the concern about spurious or nonsense regressions in time series. Specifying a relation in terms of levels of the economic variables, say , often produces empirical results in which the R2 is quite high, but the Durbin-Watson statistic is quite low. This happens because economic time series are dominated by smooth, long term trends. That is, the variables behave individually as nonstationary random walks. In a model which includes two such variables it is possible to choose coefficients which makeappear to be stationary. But such an empirical result tells us little of the short run relationship between yt and xt. In fact, if the two series are...
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...Econometric Methods FIN5EME Semester 1, 2013 Assignment 2 * Cobb-Douglas cost function: TCi = µQiβ2 pi1β3 pi2β4 pi3β5 (1) Where, TCi= Total Cost for firm i Q= Output of firm i pi1= Wage Rate pi2= Rental Price of Capital pi3= Fuel Price * Taking the natural log of equation (1) log(TCi)= β1 + β2 log(Qi) + β3 log(pi1) + β4 log(pi2) + β5 log(pi3) + ei (2) where β1= (logµ) and ei= error term. * Eviews Output of the log-log model is as follows: Dependent Variable: LOG(TC) | | | Method: Least Squares | | | Date: 06/12/13 Time: 12:47 | | | Sample: 1 145 | | | | Included observations: 145 | | | | | | | | | | | | | Variable | Coefficient | Std. Error | t-Statistic | Prob. | | | | | | | | | | | C | -3.526503 | 1.774367 | -1.987471 | 0.0488 | LOG(Q) | 0.720394 | 0.017466 | 41.24448 | 0.0000 | LOG(WAGE) | 0.436341 | 0.291048 | 1.499209 | 0.1361 | LOG(CAPITAL) | -0.219888 | 0.339429 | -0.647819 | 0.5182 | LOG(FUEL) | 0.426517 | 0.100369 | 4.249483 | 0.0000 | | | | | | | | | | | R-squared | 0.925955 | Mean dependent var | 1.724663 | Adjusted R-squared | 0.923840 | S.D. dependent var | 1.421723 | S.E. of regression | 0.392356 | Akaike info criterion | 1.000578 | Sum squared resid | 21.55201 | Schwarz criterion | 1.103224 | Log likelihood | -67.54189 | Hannan-Quinn criter. | 1.042286 | F-statistic | 437.6863 | Durbin-Watson stat...
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...Stage 1 Physics Practical- motion Aim: To investigate the speeds of different moving objects. Hypothesis: If the ball is heavier then it will longer to cover a distance, therefore it will travel at a slower speed. Variables: Independent- The independent variable that was established before the practical was the weight of the ball. Dependent- Where the dependent variable relying on the independent variable was the time taken for the ball to travel a certain distance. Controlled-The controlled variable that were established within the motion practical where distance covered, the surface the ball was travelling on and the force that was applied to the ball. Materials: The materials that are needed to complete this particular practical successfully are a- -Four stopwatches -measuring tape -graphics calculator -chalk -Three different weights for balls Diagram of practical set up Method: 1. The materials were gathered. (see materials) 2. A distance of eight metres in two metre intervals was measured and marked. 3. One person stood at the beginning preparing to roll the ball, while four others stood with a stopwatch. 4. The stopwatches were started as the ball was released. 5. The stopwatch was clicked to stop as it pasted the intervals. 6. Data was collected for each time completing the practical. Results: | Time taken (s) | Tennis Ball | Dodge Ball | Basketball | Distance covered (m) | | | | | 2 |...
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...Deep Residual Learning for Image Recognition Kaiming He Xiangyu Zhang Shaoqing Ren Microsoft Research Jian Sun Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8× deeper than VGG nets [41] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1 , where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation. 20 10 56-layer test error (%) 20 Abstract training...
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...ECO 302 WK 4 ASSIGNMENT 1 THE SOLOW GROWTH MODEL To purchase this visit here: Contact us at: http://www.activitymode.com/product/eco-302-wk-4-assignment-1-the-solow-growth-model/ SUPPORT@ACTIVITYMODE.COM ECO 302 WK 4 ASSIGNMENT 1 THE SOLOW GROWTH MODEL ECO 302 WK 4 Assignment 1 - The Solow Growth Model Write a four to six (4-6) page paper that answers the following: 1. Discuss the three (3) basic assumptions of the Solow Growth Model and analyze their compatibility with real-world economic conditions. 2. Analyze the effects of an increase in population growth on the growth rate of capital per worker. More Details of the Question are hidden... Activity mode aims to provide quality study notes and tutorials to the students of ECO 302 WK 4 Assignment 1 The Solow Growth Model in order to ace their studies. ECO 302 WK 4 ASSIGNMENT 1 THE SOLOW GROWTH MODEL To purchase this visit here: Contact us at: http://www.activitymode.com/product/eco-302-wk-4-assignment-1-the-solow-growth-model/ SUPPORT@ACTIVITYMODE.COM ECO 302 WK 4 ASSIGNMENT 1 THE SOLOW GROWTH MODEL ECO 302 WK 4 Assignment 1 - The Solow Growth Model Write a four to six (4-6) page paper that answers the following: 1. Discuss the three (3) basic assumptions of the Solow Growth Model and analyze their compatibility with real-world economic conditions. 2. Analyze the effects of an increase in population growth on the growth rate of capital per worker. More Details of the Question are hidden...
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...The existence of residual, discounted cash flow and period by period simply gives the developer a means of maximising their profits. Comment. The application of residual, discounted cash flow and period-by-period approaches to development appraisal simply gives developers opportunity to maximise their profit. Residual method is based on a simple economic concept – land value is a surplus after estimated development costs (including expected profit) have been deducted from the estimated value of the completed development. It does not deal with sufficient sensitivity with costs and income occurring at different stages of development. In practice the method is first employed in its simplest form and then the complexity level increases as development plans crystallise. Discounted Cash flow (DCF) approaches are widely used in development appraisal to accurately reflect the timing of development expenditure and revenue so that the finance costs can accurately reflect the net cash flows or amount that needs to be borrowed at each stage of the development. For large schemes with a lengthy development period, or for even larger schemes where phased development is likely, the effect of inflation needs. DCF assumes 100% equity funding so no interest calculations but it is unlikely in practice. It also does not allow for separate developer’s profit. The ‘Period by Period’ method (PBP) employs a simple allocation of costs and values to time slots. Negative cash flows typically give way...
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...company’s net income. Note that the p-value is not very impressive, 0.174, and the slope of the population regression equation could very easily be 0, at least based on this sample of years. For a year in which total revenues are $18 billion, the equation would estimate net income as $2.009 billion. The Minitab printout is shown below. Regression Analysis: NetIncome versus TotRev The regression equation is NetIncome = 0.21 + 0.0999 TotRev Predictor Coef SE Coef T P Constant 0.211 1.041 0.20 0.846 TotRev 0.09990 0.06475 1.54 0.174 S = 0.482407 R-Sq = 28.4% R-Sq(adj) = 16.5% Analysis of Variance Source DF SS MS F P Regression 1 0.5539 0.5539 2.38 0.174 Residual Error 6 1.3963 0.2327 Total 7 1.9502 Predicted Values for New Observations New Obs Fit SE Fit 95% CI 95% PI 1 2.009 0.220...
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...going to deal with in this report, there are 20,640 observations of 8 explanatory variables labeled X1, X2, X3, X4, X5, X6, X7, X8 and 1 dependent variable labeled Y. All of the 9 variables are continuous. II. Method of analysis To check the model appropriateness assumption, we need to make sure the functional form is correct. The residual plot will show the pattern suggesting the form of an appropriate model. To check the validity of the constant variance assumption, we need to examine residual plots. A residual plot with a horizontal band appearance suggests that the spread of the error terms around 0 is not changing much as the horizontal plot value increases. Such a plot tells us that the constant variance assumption approximately holds. To check the independence assumption, we need to detect if any positive autocorrelation or negative autocorrelation exist. If a plot of the time-ordered residuals has a random pattern, the error terms have little or no autocorrelation. In such a case it is reasonable to conclude that the independence assumption holds. To check the normality assumption, we need to construct a normal plot of the residuals. If the normality assumption holds, the normal plot should have a...
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...Making Decisions Based on Demand and Forecasting Latonya Woodrow Instructor Name: Dr.Samuel F. Onipede ECO 550 –Managerial Economics and Globalization July 21, 2013 College Students buy pizza in large quantities for a cheap price, but if the prices were to increase, then these same students may look for similar alternatives that will not empty their wallets. These are possible alternatives that offer a large quantity of food at a reasonable price that can affect the demand of pizza. However, monitoring the costs of the competing fast food restaurants in the Charlotte, North Carolina, area will allow Domino’s Pizza to offer certain specials and pizza deals to the community that can keep their demand at a high rate. A market demand analysis is used to help understand how much consumer demand there is for a given product or service. This type of analysis will help determine if a business can successfully enter a market and generate enough revenue and profit to maintain the business. One must identify the market and the growth potential. Domino’s Pizza was incorporated in 1963 and has been franchising since 1967. A traditional Domino’s store is located in shopping centers and/or strip malls with appropriate parking for delivery vehicles and walk-in customers for carry-out services...
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...Introduction ThyssenKrupp Ag employs 17,000 employees in 80 countries that are passionate and are experts in developing solution for sustainable progress. The company manages global growth with innovations and technical progress along with using finite resources in a sustainable way. ThyssenKrupp pushes the company to evolve which helps them to meet global challenges of the future with their innovation solutions. The company’s main activities are the development and marketing of people moving equipments, so they have been invited to submit a proposal for the development and installation of a series of cycle trains for a new airport terminal. These trains will be designed to carry out the same job such as the capacity/load, distance and time frames. The company must take in consideration factors that will affect the efficiency of the train which includes the weight of passengers and their luggage. Data from similar trains located at different airports are put together the amount of time it takes the trains to travel three thousand feet. The technical department and I are asked to aid in the development of a model to help calculate the length of time it will take for one of the company’s standard trains to travel three thousand feet in an installation. Part 1 Time 20 22 19 28 30 29 20 Passenger 66 80 60 102 115 100 70 Time 19 21 24 23 28 25 20 Passenger 65 70 85 80 100 96 71 Time 21 26 19 28 30 22 25 Passenger 75 88 60 99 110 88 90 Table 1 The regression...
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...forecast value. A^ (hat) will be placed above a value to indicate that it is being forecast. The forecast value for Yt is Yt^. The accuracy of a forecasting technique is frequently judged by comparing the original series Y1, Y2, ... with the series of forecast values Y^1, Y^ 2, .... Basic Forecasting Notation Basic forecasting notation is summarized as follows. Yt = value of time series at period t t = forecast value of Yt et = Yt - Yt^ = residual, or forecast error Several methods have been devised to summarize the errors generated by a particular forecasting technique. Most of these measures involve averaging some function of the difference between an actual value and its forecast value. These differences between observed values and forecast values are ofteri referred to as residuals. A residual is difference between an actual value and its forecaste Equation 3.6 is used to compute the error, or residual, for each forecast period. et = Yt - Yt^ et = forecast error in time...
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