The Relative Uncertainty Formula and How to Calculate It

The relative uncertainty or relative error  formula is used to calculate the uncertainty of a measurement compared to the size of the measurement. It is calculated as: relative uncertainty absolute error / measured value If a measurement is taken with respect to a standard or known value, calculate relative uncertainty as follows: relative uncertainty absolute error / known value Absolute error is the range of measurements in which the true value of a measurement likely lies. While absolute error carries the same units as the measurement, relative error has no units or else is expressed as a percent. Relative uncertainty is often represented using the lowercase Greek letter delta (ÃŽ ´). The importance of relative uncertainty is that it puts error in measurements into perspective. For example, an error of /- 0.5 centimeters may be relatively large when measuring the length of your hand, but very small when measuring the size of a room. Examples of Relative Uncertainty Calculations Example 1 Three 1.0 gram weights are measured at 1.05 grams, 1.00 grams, and 0.95 grams. The absolute error is  ± 0.05 grams.The relative error (ÃŽ ´) of your measurement is 0.05 g/1.00 g 0.05, or 5%. Example 2 A chemist measured the time required for a chemical reaction and found the value to be 155 /- 0.21 hours. The first step is to find the absolute uncertainty: absolute uncertainty 0.21 hoursrelative uncertainty Δt / t 0.21 hours / 1.55 hours 0.135 Example 3 The value 0.135 has too many significant digits, so it is shortened (rounded) to 0.14, which can be written as 14% (by multiplying the value times 100). The relative uncertainty (ÃŽ ´) in the measurement for the reaction time is: 1.55 hours /- 14% Sources   Golub, Gene, and Charles F. Van Loan. Matrix Computations – Third Edition. Baltimore: The Johns Hopkins University Press, 1996.Helfrick, Albert D., and William David Cooper. Modern Electronic Instrumentation and Measurement Techniques. Prentice Hall, 1989.

Technology in the Schools - 1166 Words

According to a PBS Learning Media national survey that took place in 2013, 7 in 10 K-12 teachers stated that educational technology allows them to do much more than they ever could do for the education of their students. (Melissa Mills) But are their opinions supported by facts? Modern technology hasn’t always been a mainstay in the classroom, but in recent decades our education system has been reforming to rely more heavily on technology to create a better learning environment for all students. This recent reformation of teaching styles and addition of tools has been studied very thoroughly in recent years, and there are several different conclusions that people have drawn about the benefits and drawbacks of making technology a major aspect of the modern classroom. Some studies seem to prove that technology in the classroom allows for students to get more engaged in their learning, while other studies show that technology is distracting students and hindering their ability t o learn. These discrepancies in results in the studies have led to a strong willed debate that has been going on for a long time now in the private and public education system. In 2011, The New York Times did a major article on the subject of technology in the classroom. They studied classrooms from all around the nation to get evidence for their article. One teacher that they interviewed was Ms. Furman, a teacher at Aprende Middle School. Ms. Furman told them a story about how computers were notShow MoreRelatedTechnology in Schools913 Words   |  4 Pages2011 Cool Tools for School Technological advancements have been as simple as the invention of the wheel to as complex as the invention of the automobile. Scientific advancement and technological breakthroughs have been a part of societies across the world. Tools that people created throughout the ages have been put to use by people of various professions. Scientists, doctors, lawyers and countless others in different lines of work have utilized the benefits of technology. The use of computersRead MoreTechnology in Schools1433 Words   |  6 Pagesto be taught using teaching techniques that are decades, even centuries old (Steinberg par. 2)? Computers in school impact, not only a person’s education, but their whole life: Financial aspects, physical conditions, and their self-esteem. A person’s finances would be affected due to the cost of the computer, the software for computers, and maintenance of the computer hardware. Some schools require that the students pay for the computer themselves. This is an obvious problem because computers areRead MoreImpact Of Technology On Schools And Schools1424 Words   |  6 Pages Introduction: Technology in Schools Through the course of this paper which will discuss the how affective technology can be utilized in schools. The main reason for technology in schools is simply improving the comminutions between the students, parents, teachers, administrators and staff. Now there are many ways in which they can reach someone or find out information whether it’s by phone, by the schools website, by email, by the schools television station, or directly. These multiple ways makeRead MoreSchool Of Engineering And Technology1714 Words   |  7 PagesCONSTRUCTION Submitted To AMITY SCHOOL OF ENGINEERING AND TECHNOLOGY Guided By: Submitted By: Mr. N.B Mishra Ali Bakshi (Faculty of Civil Engineering) Enrollment No. A2315811003 Amity School Of Engineering Technology Read MoreUse of Technology in School949 Words   |  4 Pages1.The name and a description of the school, and grade or program observed. The name of the schools is Sterne School located in San Francisco, California.The school has a philosophy of developing customized learning plans for each of the sixth to twelfth grade students with a focus on their strength areas, while identifying the areas for development and also guiding them on an excellent path in a private and yet supporting atmosphere. 2.A description of the range of children included in theRead MoreThe Use Of Technology In Schools724 Words   |  3 Pagesassessments combined with the notion that educators should increase the use of technology within their classroom, a study was conducted to determine the impact of technology on test scores. To be more specific the study was conducted to determine the impact that the use of technology had on a select group of at-risk students in 9th grade English Literature. The conditions for this study was that the at-risk students be provided technology while their non-at-risk counterparts would not. The hypothesis for theRead MoreThe Art Of Technology And Schools Essay2115 Words   |  9 PagesArt of Technology in Schools All types of technology are becoming more prevalent everywhere. Everyone has a tablet, laptop, smart phone, iPad or some type of technology in this day and age. It seems as if kids are gaining more access to these technologies because of their schools. In schools students are being given technology for homework and online textbooks now. Students are also getting exposure to technology as teaching aids in schools as well. Many people believe that technology in classRead MoreTechnology in Schools Is an Asset2331 Words   |  9 PagesImagine what schools would look like without all of the technology they have now. No computers, televisions, projectors or smart boards, just a piece of chalk and a black board. Is that the best way of teaching? Or is technology a major factor in the success of students today? According to a local principal â€Å"there is a technology gap and the schools must address this concern. It is critical to prepare our students to meet the demands of the 21st century and technology is on the forefront of job developmentRead MoreSchool Of Business And Technology3216 Words   |  13 Pages Breastfeeding Sara Scheffler English Composition I Tristan Benson July 1st 2015 McCann School of Business and Technology If you’re religious, breastfeeding has been around since Adam and Eve; it has been around since our ape ancestors if you’re into evolution. Any way that you look at it, any mammalian species simply cannot and would not have survived without breastfeeding. In modern US culture there seems to be a stigma around breastfeeding. I personally cannot understand this. WeRead MoreAs Some Schools Plunge Into Technology, Poor Schools Are1257 Words   |  6 PagesAs some schools plunge into technology, poor schools are left behind. (2012, January 24). Retrieved March 10, 2017. In this article, the authors indicate that students in high-poverty schools lack education because of the absence of technology in the schools. They explained that students who do not have the experience with technology fall behind academically compared to wealthier students. They discussed the importance of technology for the students’ learning and they believe that without technology

Econometrics for Corporate Social Responsibility

Question: Describe about panel data- fixed and random effects. Answaer: The methodology advocates the other aspects that contributes to the paper. The methodology applied in paper is panel data, which includes both temporal heterogeneity and the individual heterogeneity. The former allows controlling the macro economic effects whereas the latter allows controlling the individualistic characteristics of the firms (Hsiao, 2014). Moreover, the methodology used considers the dynamic performance of firms. However, review studied the firm performance and its effect of CSR based on cross sectional data. On the other hand, panel data has been taken to elaborate the association between CSR and firm performance to solve the problem of inherent endogeneity so that the results obtained are robust as well as consistent in nature (Madorran Christina, 2016). However, CSR and firm performance have received much consideration in the literature. Panel data has been carried out in the study to test the hypothesis on the methodology that allows taking into account both the temporal as well as unobservable individual heterogeneity. However, individual heterogeneity at one point becomes difficult to study the firm level features that directly affect the controlled firm value (Kessler, 2014). This technique not only provides a complete information regarding the testing of hypothesis but also allows the stated facets to be taken into account. The outcomes obtained are more pertinent than the cross sectional data used in the literature. Nevertheless, one-period lagged performance has been used in order to include the dynamic performance indicators to measure the persistence level of performance of the parameter. CSR is an indicator of the firm i in t-1 years. The lag has been introduced in the model because CSR efforts do not immediately affect the firm performance rather the result is reviewed later. The choice of methodology has been performed to achieve the significant objectives based on the efficiency of the results. However, panel data applied in this context has been incorporated due to four reasons. Firstly, it includes dynamic characteristics of the variables in the underlined analysis. Secondly, the times effects has been used as a variable of the year to introduce undetected individual effect in the individual heterogeneity. Hansen Over identification Test - The dynamic model can be estimated based on two generalized step of moments that not only provide an efficient yet consistent estimator but also addresses the impending endogeneity in the model. The instruments in lags in independent variable are considered from t 1 whereas the lags in dependent variable are considered from t 2 (O'Neill Hanrahan, 2016). However, Hansens instruments validates the statistic results through the over identification chi square distribution. The test involves degrees of freedom that are equal to the number of over-identifying limitations. Nonetheless, in order to remove the individual effects it is important that individual effects be associated with the remaining variables based on the first differences of the variables (Corredor Goi, 2011). Error Correction Procedure by Windmei - On the other hand, the model estimation for error correction is carried out on the small samples. The correlation in error terms m1 and m2 is for serial correlation in order of 1 and 2 respectively that has been calculated using fist differences in the residuals. These tests are asymptotically distributed as N (0, 1) under the statement of no serial correlation in null hypothesis and m2 is calculated following Arellano (Roodman, 2015). Wald Statistics On the other hand, the coefficients offer the Walds statistic based on z1 and z2. z1 statistic is used for shared significance of the model whereas z2 is used for shared significance of the time dummies. However, both the statistics just like Windmei Error Collection Procedure, the statistics are normally distributed as under null hypothesis as a chi square of no joint significance. However, all these statistics are executed using STATA, statistical tool (Hernndez-Cnovas, Mnguez-Vera Snchez-Vidal, 2014). Alternatively, the other way of evaluating a longitudinal or cross sectional data is with the help of pooled OLS regression treats the regression of y on x using controlled variables. However, it ignores the structure followed by the panel data such that the pooled regression omits the invariant unobserved variables from the study. Hence, it is important that all the unobserved variables be controlled for the probable influence by the generalized least square random effect regression model (GLS-RE). GLS-RE regression with OLS assumes to have no correlation between explanatory variables as well as error terms but it takes into consideration unobserved variables that will not only be constant over time but also will vary between companies while others may not be just constant between companies but might vary over time (Hagen Waldeck, 2014). However, as stated, it is observed that GLS-RE ought to violate the auto regression correlation of the least square. Comparatively, GLS-RE regression is appropriate to control the influence of the time-constant variables that often face the problem of serial correlation in the error term. The assumptions that hold efficient in the given nature of the data such that it follows the order to analyse the use of RE estimator. The error term e is a composite error terms that comprises of both time-varying (mit) as well as time-constant (ai) firm specific variable such that e as ai mit are uncorrelated through time. The unobserved effects (mit) and idiosyncratic errors (ai) are assumed to be uncorrelated with independent variables across all time periods (strict exogeneity) The unobserved as well as observed variables are randomly drawn from the certain distribution. Furthermore, in order to ensure the assumption, the researcher has forecasted the residuals with the predict command to plot a histogram of r for visual analysis. However, to ensure the results validity as well as clarity, the test of Shapiro-Wilk W-test had been conducted. The W statistics in the test specifies normality if the value of W is close or equal to one given that the null hypothesis states that r is normally distributed. The results and null hypothesis will not be rejected if the p value comes greater than 0.05 (Hanusz, Tarasinska Zielinski, 2016) The validity of the results can further be proven by other tests. Tests like Breusch-Pagan Lagrange multiplier (LM) is needed to check the validity of random-effect estimator. This test has been designed not only to check the efficiency and validity of the RE models but has also been designed to test the efficiency in conducting unobserved heterogeneity. In this particular test, the square of the residuals (error term) are regressed on the independent variables. The null hypothesis for the particular tests states that the variance across the regression equation is zero (Ho: 0; no substantial dissimilarity across units) (Baltagi, Feng Kao, 2012). Moreover, if the test failed to reject the null hypothesis then RE model will be considered not suitable for the evaluation. Conversely, the test assumes to favor Random Effect Estimator if test is significant at 1% confidence level. The regression in LM tests are run according to the standard error that will spontaneously modify all standard errors as well as p-values looking for any possible problem of outliers, heteroskedasticity and other irregularities and lately mend the rationality of the results. As dependent variable helps in acknowledging the financial performance of the firm. As a result, the factors are controlled thoroughly such that it does not affect the financial performance. However, those variables are included that not only affect the financial performance of the firms but also controls the unobservable with fixed combination effects like firm, year and industry. The econometric specification will be discussed in the following section below. To choose a multivariate statistical method, the research starts form the OLS (Ordinary Least Square) specification in which firstly, the firms performance will be specified (Performit) as a function that is linear in nature with vector X of explanatory/ independent variables from time t of the firm i additional to the error term uit. As provided by the panel structure of the data, the major possibility arises that all the error terms will be correlated with firms over time. This kind of serial correlation paves way to spurious regression results (Wooldridge, 2015). To deal with serial correlation the model will now turn into a dynamic longitudinal model. This model will incorporate lags in linear autoregressive dynamics of dependent variable as regressor to encompass performance for within-firm persistence. Explicitly, to incorporate lag of one-year in the dependent variable then the within-firm AR (1) process will be followed in every specification. The within-firm serial correlations addresses the dynamics of AR(1) such that there is probability that error term (ei) across time will not be independent. However, any effect related to time-dependence that is not included in X will incorporated in the error term. However, the earlier researches have studied that there are many macro-economic factors that are linked with performance constituting of changes in systemic, government policy as well as macro-economic shocks. There is a need to measure all these effects with the corresponding time componenet so that it can be considered in the error term (eit). As a result, this methodical component will lead to correlation in the error terms that will violate the assumptions of OLS. Theoretically, the decomposition of error term (eit) into fixed time effects vector such that it will be labelled as Zt where Zt will symbolize dummy variables of the year with v.i.t as vector term and i.i.d as normal error terms with mean equal to zero. The equation will represent the eit decomposition (Bai Wang, 2015). As a final point, there is still a strong possibility that vector term (v.i.t) will not be independent of within industries or firms. This states that due to any occurrence if these firms operate in a different manner methodically due to transient factors that are long term in nature then there will be unobserved heterogeneity. However, to correct this, the fixed effects will come into play in industry/ firm according to the specifications (Ertur Musolesi, 2012). However, the final economic specification can be summarized based on within-firm AR (1) dynamics, fixed industry/ firm and fixed effects in year to control the features that are not restrained by other variables that might associate with the performance of the firm (Huang, 2013). This possible benefit of the approach is that it not only controls for unobserved heterogeneity without measuring the source of heterogeneity but also eliminates bias and provides strong estimates is statistical results. The possible drawback of this approach is that it is difficult to specify as well as identify different individual factors that will affect the dependent variable. Nevertheless, the goal is to not to test these effects or control the effects then the tradeoff will be accepted. The concerns relating to unobserved heterogeneity and autocorrelation are associated with panel data. However, to solve these issues, standard techniques are applied. Initially, Hausman test was conducted to provide a proper estimation method but the results depicted correlation issue between regressors and error (Marvel Pitts, 2014). Nonetheless, to solve this issue, firm fixed effects models were suggested over the random effects model for statistical evaluation. However, to test the validity as well as the efficiency of the findings, the outcomes reported were associated and compared to the random effects model results. As a result, the results obtained were lower from the perspective of random effect model based on the magnitude, significance levels as well as largely consistent coefficients. Therefore, the reported estimates are considered conservative using the fixed effects model. However, with any further correction in the potential autocorrelation might lead to biased param eter estimates in time series data and might vary over time to control the effects that assumes to be constant across firms and by adding year dummy variables, a time fixed effects was also incorporated. References Bai, J., Wang, P. (2015). Econometric Analysis of Large Factor Models. Baltagi, B. H., Feng, Q., Kao, C. (2012). A Lagrange Multiplier test for cross-sectional dependence in a fixed effects panel data model.Journal of Econometrics,170(1), 164-177. Corredor, P., Goi, S. (2011). TQM and performance: Is the relationship so obvious?.Journal of Business Research,64(8), 830-838. Ertur, C., Musolesi, A. (2012).Spatial autoregressive spillovers vs unobserved common factors models. A panel data analysis of international technology diffusion(No. 2012/9). INRA UMR CESAER, Centre d'Economie et Sociologie appliques l'Agriculture et aux Espaces Ruraux. Hagen, T., Waldeck, S. (2014).Using panel econometric methods to estimate the effect of milk consumption on the mortality rate of prostate and ovarian cancer(No. 03). Frankfurt University of Applied Sciences, Faculty of Business and Law. Hanusz, Z., Tarasinska, J., Zielinski, W. (2016). SHAPIROWILK TEST WITH KNOWN MEAN.REVSTATStatistical Journal,14(1), 89-100. Hernndez-Cnovas, G., Mnguez-Vera, A., Snchez-Vidal, J. (2014). Ownership structure and debt as corporate governance mechanisms: an empirical analysis for Spanish smes.Journal of Business Economics and Management, 1-17. Hsiao, C. (2014).Analysis of panel data(No. 54). Cambridge university press. Huang, S. (2013).Board tenure and firm performance. working paper, INSEAD Business School. Kessler, R. C. (2014).Linear panel analysis: Models of quantitative change. Elsevier. Madorran, C., Garcia, T. (2016). Corporate social responsibility and financial performance: the spanish case.Revista de Administrao de Empresas,56(1), 20-28. Marvel, J., Pitts, D. (2014). What We Talk About When We Talk About Management Effects: A Substantively Motivated Approach to Panel Data Estimation.International Journal of Public Administration,37(3), 183-192. O'Neill, S., Hanrahan, K. (2016). The capitalization of coupled and decoupled CAP payments into land rental rates.Agricultural Economics,47(3), 285-294. Roodman, D. (2015). xtabond2: Stata module to extend xtabond dynamic panel data estimator.Statistical Software Components. Wooldridge, J. M. (2015).Introductory econometrics: A modern approach. Nelson Education.

Natural and Man Made Disasters free essay sample

A  natural disaster  is a major adverse event resulting from  natural processes  of the Earth. A natural disaster can cause loss of life or property damage, and typically leaves some economic damage in its wake, the severity of which depends on the affected populations  resilience, or ability to recover. TYPES OF NATURAL DISASTERS: 1-EARTHQUAKES: An  earthquake  is the result of a sudden release of energy in the  Earths  crust  that creates  seismic waves. At the Earths surface, earthquakes manifest themselves by vibration, shaking and sometimes displacement of the ground. -VOLCANIC ERUPTIONS: The effects include the  volcanic eruption  itself that may cause harm following the explosion of the volcano or the fall of rock. Second,  lava  may be produced during the eruption of a volcano.. Third,  volcanic ash   may form a cloud, and settle thickly in nearby locations. 3-FLOODS: A  flood  is an overflow of an expanse of water that submerges land. We will write a custom essay sample on Natural and Man Made Disasters or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page It causes great damage to buildings, wildlife and humans. 4-LIMNIC ERUPTIONS: A  limnic eruption  occurs when a gas, usually  CO2, suddenly erupts from deep lake water, posing the threat of suffocating wildlife, livestock and humans. Such an eruption may also cause  tsunamis  in the lake as the rising gas displaces water. 5-TSUNAMI: Tsunamis can be caused by undersea earthquake. 6-BLIZZARD: Blizzards are severe  winter storms  characterized by heavy snow and strong winds. 7-CYCLONIC STORMS: Cyclone,  tropical cyclone,  hurricane, and  typhoon  are different names for the same phenomenon a cyclonic storm system that forms over the oceans. 8-DROUGHTS: Drought is unusual dryness of soil, resulting in crop failure and shortage of water for other uses, caused by significantly lower rainfall than average over a prolonged period 9-HAILSTORMS: Hailstorms are falls of rain drops that arrive as ice, rather than melting before they hit the ground. 10-HEATWAVES: A heat wave is a period of unusually and excessively hot weather. 11-TORNADO- A  tornado  is a violent, dangerous, rotating column of air that is in contact with both the surface of the earth and a  cumulonimbus cloud  or, in rare cases, the base of a  cumulus cloud. It is also referred to as a  twister  or a  cyclone [pic] [pic] [pic] 5 worst natural disasters: 1-South East Asia Tsunami The deadliest tsunami in the world happened on December 26, 2004 in the Indian Ocean hitting Southeast Asia and killing 186,983 people 2- Shenshi , China Earthquake A killer quake that hit Shenshi , China on February 2, 1556 killed 820,000 people and leaving thousands of homeless families. 3- Cyclone of 1970: The Cyclone of 1970 that hit Bangladesh on November 13, 1970 killed about 1 million people. This cyclone with winds of over 190 km/h is also known as Bhota Cyclone. It’s the worst cyclone in recorded history. 4- Mediterranean Earthquake: A killer earthquake that rocked Near East and the Mediterranean region on May 20, 1202 killed 1. 1 million people and leaving thousands of homeless families. It’s the deadliest earthquake so far. 5- The Yellow River or Huang He flood in China This terrible flood destroyed billion worth of properties and livelihood and killed 3. 7 million people in August 1931. It is the worst flood in recorded history. MAN MADE DISASTERS: TYPES: Arson Arson is the criminal intent of setting a  fire  with intent to cause damage. Terrorism One definition means a violent action targeting civilians exclusively. It causes great damage to human life and economic conditions as well. Industrial Hazards Industrial disasters occur in a commercial context, such as  mining disasters. They often have an  environmental impact. Structural Collapse Structural collapses are often caused by engineering failures. Many people die during stru†¦ Aviation An aviation incident is an occurrence other than an accident, associated with the operation of an aircraft, which affects or could affect the safety of operations, passengers, or pilots. Railroad A railroad disaster is an occurrence associated with the operation of a passenger train which results in substantial loss of life. Space Disasters Space disasters, either during operations or training, have killed around 20 astronauts and cosmonauts, and a much larger number of ground crew and civilians. These disasters include either malfunction on the ground, during launch, or in orbit with technology, Radiation Contamination When nuclear weapons are detonated or nuclear containment systems are otherwise compromised, airborne radioactive particles (nuclear fallout) can scatter and irradiate large areas. Not only is it deadly, but it also has a long-term effect on the next generation for those who are contaminated. worst man made disasters: 1-bhopal gas tragedy India had on December 2, 1984 when the Union Carbide India Limited pesticide plant  sprang a gas leak. Over 500,000 people were exposed to methyl isocyanine gas and other chemicals. Thousands of people died within the first hours of the leak, but estimates between  5,000 to upwards of 16,000 deaths  resulted from the leak overall. 2-Deepwater Horizon Oil Spill, Gulf of Mexico: It started on April 20, 2010 when an explosion on BPs Deepwater Horizon oilrig killed 11 workers, injured 17 others, and left the well gushing oil.