Faith and Reason in the Enlightenment Essay -- The Enlightenment in Eu

In a time when faith and hard labor kept the majority of society alive, the introduction of reason by the Enlightenment was initially perceived as a threat. People had focused on their faiths and grasped the traditions and rituals of their dogmas. The Enlightenment introduced the possibility of faith and reason coinciding and cooperating to form a more civilized and equal society to replace the Old Regime, and the changes lasted far after the period of the Enlightenment. Leading up to the Enlightenment Prior to the Enlightenment, England and France instituted Old Regime societies in which three distinct classes of people embraced religion as the foundation of their lives. Each caste had a different lifestyle, with the clergy enjoying the upper class, the nobility in the position of influence, and the vast majority of the people trapped in the hardship of the Third Estate. The clergy was different in the Protestant Church than in the Catholic Church because the Catholics had only to obey the Pope while the Protestant Church was run by the monarch. None of the clergy paid many royal taxes, but still owned much of the land. Since the clergy was a high class, it was beneficial for some of the offspring of the nobility to join the clergy in order to receive higher status. The nobility as a whole controlled much money and power while maintaining constant struggle with the crown over governmental power. The Third Estate worked to live and had no freedom except for their religious beliefs. They believed that they were at the mercy of the land and of an overpowering Creator. The Old Regime was characterized in large part by conflicts between countries and within countries over religious matters. It w... ...ove their minds. European society that was once stuck in the Old Regime lifestyle grew in many facets with the introduction of reason and enlightenment. Although initially reluctant, the societies of the Old Regime embraced the thoughts of the Enlightenment, the conflict between faith and reason began to subside as people learned that they could practice both. References 1 Donald Kagan. The Western Heritage Brief Edition:Volume II Since 1648. (Upper Saddle River: Pretence Hall, 1999), 313. 2 Kagan 298 3 Perry Rogers. Aspects of Western Civilization: Problems and Sources in History 3rd ed. (Upper Saddle River: Pretence Hall, 1997), 12. 4 Rogers, 15. 5 Kagan, 317. 6 Peter Gay. Age of Enlightenment. (New York: Time Life Books, 1966), 32. 7 Kagan, 402. 8 Kagan, 329. 9 Gay, 56. 10 Gay, 54. 11 Rogers, 102.

Funeral home visit

Fullerton college students had a privilege to visit Mennonite Memorial Park and Mortuary. When we arrived the park, its beautiful scenery amazed us. The weather was a little chilly and windy. Since there was a scheduled funeral service on the day that we visited, we were escorted to the reception hall where we had a chance to meet the manager. The manager thoroughly went over proper procedures needed to prepare embalming and funerals.Among all the things that we newly learned from this visit, most interesting thing was how the embalming process works. They utilize special chemicals to look the deceased as natural as possible. I thought embalming was a very meaningful work since It is the last time that a family will physically see the deceased and it helps a family create last memories about the deceased. I agreed when the manager said their Job is not to be emotionally involved with a family but to help them figure out the directions ND plans about next steps after a loved on passes away.I cannot imagine how challenging it can be for staff members in Mennonite Memorial Park and Mortuary to deal with families on a daily basis who have loss or about to have loss. I felt that being passionate and dedicated to compassionately embracing families in the community is one of the most important aspects to be part of the mortuary. It was a very meaningful visit and helped me think about how to deal with and death and dying from the lens of a family who had lost a loved one.

Error Correction Model

Introduction Exchange rates play a vital role in a county's level of trade, which is critical to every free market economies in the world. Besides, exchange rates are source of profit in forex market. For this reasons they are among the most watched, analyzed and governmentally manipulated economic measures. Therefore, it would be interesting to explore the factors of exchange rate volatility. This paper examines possible relationship between EUR/AMD and GBP/AMD exchange rates. For analyzing relationship between these two currencies we apply to co-integration and error correction model.The first part of this paper consists of literature review of the main concepts. Here we discussed autoregressive time series, covariance stationary series, mean reversion, random walks, Dickey-Fuller statistic for a unit root test. * The second part of the project contains analysis and interpretation of co-integration and error correction model between EUR/AMD and GBP/AMD exchange rates. Considering t he fact, that behavior of these two currencies has been changed during the crisis, we separately discuss three time series periods: * 1999 2013 * 1999 to 2008 * 2008 to 2013. ——————————–Autoregressive time series A key feature of the log-linear model’s depiction of time series and a key feature of the time series in general is that current-period values are related to previous period values. For example current exchange rate of USD/EUR is related to its exchange rate in the previous period. An autoregressive model (AR) is a time series regressed on its own past values, which represents this relationship effectively. When we use this model, we can drop the normal notation of Y as the dependent variable and X as the independent variable, because we no longer have that distinction to make.Here we simply use Xt. For instance, below we use a first order autoregression for the variable Xt. Xt=b0+b1*Xt-1+? t Covariance stationary series To conduct valid statistical inference we must make a key assumption in time series analysis: We must assume that the time series we are modeling is Covariance Stationary. The basic idea is that a time series is covariance stationary, if its mean and variance do not change over time. A covariance stationary series must satisfy three principal requirements. Expected value of the time series must be constant and finite in all periods. * Variance should be constant and finite. * The covariance of the time series with itself for a fixed number of periods in the past or future must be constant and finite. So, we can summarize if the plot shows the same mean and variance through time without any significant seasonality, then the time series is covariance stationary. What happens if a time series is not covariance stationary but we use auto regression model? The estimation results will have no economic meaning.For a non-covariance- stationary time series, est imating the regression with the help of AR model will yield spurious results. Mean Reversion We say that time series shows mean reversion if it tends to fall when its level is above its mean and rise when its level is below its mean. If a time series are currently at its mean reverting level, then the model predicts, that the value of the time series will be the same in the next period Xt+1=Xt. For an auto regressive model, the equality Xt+1 = Xt implies the level Xt = b0 + b1 * Xt or Xt = b0 / (1 – b1)So the auto regression model predicts that time series will stay the same if its current value is b0/(1 – b1), increase if its current value is below b0 / (1 – b1), and decrease if its current value is above b0 / (1 – b1). Random Walks A random walk is a time series in which the value of the series in one period is the value of the series in the previous period plus an unpredictable error. Xt = Xt-1 + ? t, E(? t)=0, E(? t2) = ? 2, E(? t, ? s) = 0 if t? s Th is equation means that the time series Xt is in every period equal to its value in the previous period plus an error term, ? , that has constant variance and is uncorrelated with the error term in previous periods. Note, that this equation is a special case of auto correlation model with b0=0 and b1=1. The expected value of ? t is zero. Unfortunately, we cannot use the regression methods on a time series that is random walk. To see why, recall that if Xt is at its mean reverting level, than Xt = b0/ (1 – b1). As, in a random walk b0=0 and b1=1, so b0/ (1 – b1) = 0/0. So, a random walk has an undefined mean reverting level. However, we can attempt to convert the data to a covariance stationary time series.We create a new time series, Yt, where each period is equal to the difference between Xt and Xt-1. This transformation is called first-differencing. Yt= Xt – Xt-1 = ? t, E (? t) = 0, E (? t2) = ? 2, E (? t, ? s) = 0 for t? s The first-differenced variable, Yt, i s a covariance stationary. First note, that Yt=? t model is an auto regressive model with b0 = 0 and b1 = 0. Mean-reverting level for first differenced model is b0/ (1 – b1) = 0/1 = 0. Therefore, a first differenced random walk has a mean reverting level of 0. Note also the variance of Yt in each period is Var(? ) = ? 2. Because the variance and the mean of Yt are constant and finite in each period, Yt is a covariance stationary time series and we can model it using linear regression. Dickey-Fuller Test for a Unit Root If the lag coefficient in AR model is equal to 1, the time series has a unit root: It is a random walk and is not covariance stationary. By definition all random walks, with or without drift term have unit roots. If we believed that a time series Xt was a random walk with drift, it would be tempting to estimate the parameters of the AR model Xt = b0 + b1 * Xt -1 + ? using linear regression and conduct a t-test of the hypothesis that b1=1. Unfortunately, if b1=1 , then xt is not covariance stationary and the t-value of the estimated coefficient b1 does not actually follow the t distribution, consequently t-test would be invalid. Dickey and Fuller developed a regression based unit root test based on a transformed version of the AR model Xt = b0 + b1 * Xt -1 + ? t. Subtracting xt-1 from both sides of the AR model produces xt- xt-1=b0+(b1-1)xt-1+ ? t or xt-xt-1 = b0 + g1xt-1+ ? t, E(? ) = 0 where gt = (b1-1). If b1 = 1, then g1 = 0 and thus a test of g1 = 0 is a test of b1 = 1. If there is a unit root in the AR model, then g1 will be 0 in a regression where the dependent variable is the first difference of the time series and the independent variable is the first lag of the time series. The null hypothesis of the Dickey-Fuller test is H0: g1 =0 that is, that the time series has a unit root and is non stationary and the alternative hypothesis is Ha: G1 ; 0, that the time series does not have a unit root and is stationary.To conduct the test, on e calculates a t- statistic in the conventional manner for g(hat)1 but instead of using conventional critical values for a t- test, one uses a revised set of values computed by Dickey and Fuller; the revised set of critical values are larger in absolute value than the conventional critical values. A number of software packages incorporate Dickey- Fuller tests. REGRESSIONS WITH MORE THAN ONE TIME SERIES Up to now, we have discussed time-series models only for one time series. In practice regression analysis with more than one time-series is more common.If any time series in a linear regression contains a unit root, ordinary least square estimates of regression test statistics may be invalid. To determine whether we can use linear regression to model more than one time series, let us start with a single independent variable; that is, there are two time series, one corresponding to the dependent variable and one corresponding to the independent variable. We will then extend our discuss ion to multiple independent variables. We first use a unit root test, such as the Dickey-Fuller test, for each of the two time series to determine whether either of them has a unit root.There are several possible scenarios related to the outcome of these test. One possible scenario is that we find neither of time series has a unit root. Then we can safely use linear regression to test the relations between the two time series. A second possible scenario is that we reject the hypothesis of a unit root for the independent variable but fail to reject the hypothesis of a root unit for the independent variable. In this case, the error term in the regression would not be covariance stationary.Therefore, one or more of the following linear regression assumptions would be violated; 1) that the expected value of the error term is 0. 2 that the variance of the error term is constant for all observations and 3) that the error term is uncorrected across observations. Consequently, the estimated regressions coefficients and standard errors would be inconsistent. The regression coefficient might appear significant, but those results would be spurious. Thus we should not use linear regression to analyze the relation between the two time series in this scenario.A third possible scenario is the reverse of the second scenario: We reject the hypothesis of a unit root for the dependent variable but fail to reject the hypothesis of a unit root for the independent variable. In the case also, like the second scenario, the error term in the regression would not be covariance stationary, and we cannot use linear regression to analyze the relation between the two time series. The next possibility is that both time series have a unit root. In this case, we need to establish where the two time series are co-integrated before we can rely on regression analysis.Two time series are co-integrated if a long time financial or economic relationship exists between them such that they don’ t diverge from each other without bound in the long run. For example, two time series are co-integrated if they share a common trend. In the fourth scenario, both time series have a unit root but are not co-integrated. In this scenario, as in the second and third scenario above, the error term in the linear regression will not be covariance stationary, some regressions assumptions will be violated, the regression coefficients and standard errors will not be consistent, and we cannot use them for the hypothesis tests.Consequently, linear regression of one variable on the other would be meaningless. Finally, the fifth possible scenario is that both time series have unit root, but they are co-integrated in this case, the error term in the linear regression of one term series on the other will be covariance stationary. Accordingly, the regression coefficients and standard errors will be consistent, and we can use them for the hypothesis test. However we should be very cautious in interp reting the results of regression with co-integrated variables.The co-integrated regression estimates long term relation between the two series but may not be the best model of the short term relation between the two series. Now let us look at how we can test for co-integration between two time series that each have a unit root as in the last two scenarios above. Engle and Granger suggest this test: if yt and xt are both time series with a unit root, we should do the following: 1) Estimate the regression yt = b0 + b1xt + ? t 2) Test whether the error term from the regression in Step 1 has a unit root coefficients of the regression, we can’t use standard critical values for the Dickey – Fuller test.Because the residuals are based on the estimated coefficients of the regression, we cannot use the standard critical values for the Dickey- Fuller test. Instead, we must use the critical values computed by Engle and Granger, which take into account the effect of the uncertaint y about the regression parameters on the distribution of the Dickey- Fuller test. 3) If the (Engle – Granger) Dickey- Fuller test fails to reject the null hypothesis that the error term has a unit root, then we conclude that the error term in the regression is not covariance stationary.Therefore, the two time series are not co-integrated. In this case any regression relation between the two series is spurious. 4) If the (Engle- Granger) Dickey- Fuller test rejects the null hypothesis that the error term has a unit root, then we conclude that the error term in the regression is covariance stationary. Therefore, the two time series are co-integrated. The parameters and standard errors from linear regression will be consistent and will let us test hypotheses about the long – term relation between the two series. .If we cannot reject the null hypothesis of a unit root in the error term of the regression, we cannot reject the null hypothesis of no co-integration. In this sc enario, the error term in the multiple regressions will not be covariance stationary, so we cannot use multiple regression to analyze the relationship among the time series. Long-run Relationship For our analysis we use EUR/AMD and GBP/AMD exchange rates with respect to AMD from 1999 to 2013 with monthly bases. After estimating the normality of these time series we found out that the normality has rejected.We got right skewness result and to correct them we used log values of exchange rates. Studying the trade between Armenia and Europe or Great Britain we found out that there is almost no trade relationship between them. Besides we assume, that Armenian Central Bank keeps floating rate of AMD. Taking into consideration these two factors the impact of AMD is negligible to have an essential influence on EUR/GBP rate. That is why we assume that the next models we will build show the relation between EUR and GBP. Graph 1 represents movement of EUR/AMD ; GBP/AMD since 1999 to 2013.From it we can assume that these two currencies have strong long run relationship until Global Financial Crisis. As a result of shock in 2008 the previous relationship has been changed. However, it seems to be long term co-movement between the currencies. To accept or reject our conclusions we examine exchange rates until now including Global Financial Crisis, without crisis and after crisis. Co-integration of period from 1999 to 2013 To be considered as co-integrated the two variables should be non-stationary. So the first step in our model is to check the stationarity of variables by using Augmented Dickey-Fuller Unit Root Test.EViews has three options to test unit-root: * Intercept only * Trend and Intercept * None From the first graph it is visible, that the sample average of EUR/AMD time series is greater than 0, which means that we have an intercept and it should be included in unit-root test. Although, series goes up and down, data is not evolving around the trend, we do not have increasing or decreasing pattern. Besides, we can separately try each of the components and include trend and intercept, if they are significant. In the case of EUR/AMD the appropriate decision is only intercept. Table 1. 1Table 1. We see it from the Table 1. 1, where Augmented Dickey-Fuller test shows p-value of 0. 1809 and as we have decided to use 5% significance level, Null Hypothesis cannot be rejected, which means there is a unit root. So, EUR/AMD exchange rate time-series is non-stationary. The same step should be applied with GBP/AMD exchange rates. We have estimated it and found out, that Augmented Dickey-Fuller test p-value is 0. 3724, which gives us the same results, as in the previous one: the variable has unit root. Since, the two variables are non-stationary, we can build the regression model yt = b0 + 1xt + ? t (Model 1. 1) and use et residuals from this model. So, the second step is to check stationarity for these residuals. Here we should use Eagle Granger 5% critic al value instead of Augmented Dickey Fuller one, which is equal to -3. 34. Comparing this with Augmented Dickey-Fuller t-Statistic -1. 8273. Here minus signs should be ignored. So, comparing two values, we cannot reject Null Hypothesis, which means residuals have unit-root, they are non-stationary. This outcome is not desirable, which means the two variables are not co-integrated.Co-integration till crisis period (1999-2008) Referring back to graph 1, we assume that in 1999-2013 time series two variables are not co-integrated because of shock related to financial crisis. That is why it will be rational first to exclude data from 2008 to 2013 and then again check co-integration between two variables. Here the same steps should be applied as in checking co-integration for time series from 1999 to 2013. For time series from 1999 to 2008, for EUR/AMD exchange rate, Augmented Dickey-Fuller test p-value is 0. 068. From the p-value it is clear that we cannot reject Null Hypothesis, which m eans it has a unit root. Having unit root means EUR/AMD exchange rate time-series is non-stationary. Now we should test stationarity of GBP/AMD exchange rates. The Augmented Dickey-Fuller test p-value is 0. 2556, which means the variable is non-stationary. Since, the two variables are non-stationary, we should build the regression model and using residuals check stationarity. Table 2. 1 In the table above Augmented Dickey Fuller t-test is 3. 57 and so greater than Eagle-Granger 5% significance level critical value 3. 34. That is why we can reject Null Hypothesis and accept Alternative Hypothesis, which means that residuals in regression model has no unit root. Consequently, they are stationary and we can conclude, that EUR/AMD and GBP/AMD time series are co-integrated: have long run relationship. As the variables such as EUR/AMD and GBP/AMD are co-integrated, we can run the error correction model (ECM) as below D(yt) = b2 + b3*D(xt) + b4*Ut-1 +V (Model 1. 2) * D(yt) and D(xt) are fi rst differenced variables b2 is the intercept * b3 is the short run coefficient * V white noise error term * Ut-1 is the one period lag residual of ? t . Ut-1 is also known as equilibrium error term of one period lag. This Ut-1 is an error correction term that guides the variables of the system to restore back to equilibrium. In other words, it corrects this equilibrium. The sign before b4 or the sign of error correction term should be negative after estimation. The coefficient b4 tells as at what rate it corrects the previous period disequilibrium of the system.When b4 is significant and contains negative sign, it validates that there exists a long run equilibrium relationship among variables. After estimating Model 1. 2, short run coefficient value b3 has been 1. 03 and was found significant. And b4, the coefficient of error term has been 5. 06 percent meaning that system corrects its previous dis-equilibrium at a speed of 5. 06% monthly. Moreover, the sign of b4 is negative and s ignificant indicating that validity of long run equilibrium relationship between EUR and GBP.Co-integration during crises period (2008-2013) Now is the time to check stationarity of variables in the period after crisis by the same way as we did above. From the ADF test it is clear that the two variables are non-stationary, after which we can construct ADF ; Eagle Granger test for residuals. However, because of ADF t-statistic is smaller, than Eagle Granger critical value, we could not reject that the residuals have unit-root. So, they are non-stationary and co-integration does not exist between the two currencies.

Definition and Examples of Preterit(e) Verbs

Definition and Examples of Preterit(e) Verbs In traditional grammar, the preterit(e)  is the simple past tense of the verb, such as walked or said.  In English, the preterit(e) is typically formed by adding the suffix -ed or -t to the base form of a verb. This form is sometimes referred to as the dental preterit(e). The term is usually spelled preterit in American English, preterite in British English. Examples ofPreterit(e) Verbs They  jumped and laughed and  pointed at the solemn guards.(Terry Goodkind, Temple of the Winds, 1997)I removed the crucible from the wire stand and  poured the  silver. Some of the metal ran into the mold, some of it spilled over the outside, and some of it adhered to the crucible.(John Adair,  The Navajo and Pueblo Silversmiths, 1944)We  climbed  the mountain sides, and  clambered  among sagebrush, rocks and snow.(Mark Twain,  Roughing It, 1872)Ben snatched the squash from her, sprinted across the living room, tripped over a toy hed left there and  spilt the  entire contents of the glass over the sofa.(Sarah Morgan,  The Christmas Marriage Rescue, 2015)I ate his liver with some fava beans and a nice chianti.  Ã‚  (Anthony Hopkins as Hannibal Lecter in The Silence of the Lambs, 1991)  During many of the group sessions, the women and I painted, glued, cut, pasted, talked, listened, ate, drank, laughed, cried, and engaged in collaborative processes of ref lection and action.​(Alice McIntyre, Women in Belfast: How Violence Shapes Identity. Praeger Publishers, 2004) Backshifting Tense [Another] use of the preterite shows up in indirect reported speech. Notice the contrast between has and had in this pair. [37i] Kim has blue eyes. [original utterance: present tense][37ii] I told Stacy that Kim had blue eyes. [indirect report: preterite] If I say [i] to Stacy, I can use [ii] as an indirect report to tell you what I said to Stacy. Im repeating the content of what I said to Stacy, but not the exact wording. My utterance to Stacy contained the present tense form has, but my report of it contains preterite had. Nonetheless, my report is entirely accurate. This kind of change in tense is referred to as backshift. The most obvious cases of backshift are with verbs of reporting that are in the preterite, like told or said. (Rodney Huddleston and Geoffrey K. Pullum, A Students Introduction to English Grammar. Cambridge University Press, 2006) The Preterite and the Present-Perfect - [W]ith most verbs the difference between the form of the present perfect and the form of the preterite is slight in present-day English, especially in informal speech, which explains why in a long-term perspective the distinction may eventually be lost. . . . Reference to distinct past time without any obvious kind of anchoring has emerged as an area where usage is far from settled in present-day English. The selection of the preterite in such cases appears to be on the increase . . ..(Johan Elsness, The Perfect and the Preterite in Contemporary and Earlier English. Mouton de Gruyter, 1997)- [T]he systematic marking of perfect aspect in LModE [Late Modern English] has partially relieved the simple Preterite of its burden of indicating past time. Since perfectivity implies the completion of an event prior to the actual time of utterance, a Present Perfect form carries an automatic implication of pastness. The actual point of completion in past time may be very close, as in (18), or vaguely more distant, as in (19). (18) Ive just eaten my dinner.(19) John Keegan has written a history of war. . . . [T]he growing acceptability of the vague degree of pastness in sentences such as (19) indicates that LModE may be starting on the road that led the Perfect to replace the Simple Past in a number of Romance languages. (Jacek Fisiak, Language History and Linguistic Modelling. Mouton de Gruyter, 1997) EtymologyFrom the Latin, to go by Pronunciation: PRET-er-it Also Known As: simple-past tense Alternate Spellings: preterite

View Message LinkedIns New Frustrating Email Functionality

View Message LinkedIns New Frustrating Email Functionality Today I opened up my inbox and saw I had a message from a LinkedIn contact. â€Å"Maybe he’s interested in my services!† I thought. Of course, I couldn’t tell what he had written, because all the subject line said was, â€Å"John sent you a new message.† And all the body of the email had was a line saying â€Å"You have 1 new message† – along with John’s head shot and partial headline. I had to click â€Å"View Message† to find out what the heck this guy wanted to talk about. I was already angry by the time I clicked â€Å"View Message† to be brought to the LinkedIn website. And when I got there, I discovered that his message said †¦ ready for this? †¦. â€Å"Thanks.† Yep, I had wished John a happy birthday and he was thanking me. That was it. If I had been able to see this message in my email inbox, all would be well. I could have deleted it and someday gotten around to replying to John with a thumbs up – or not. But as of about a month ago, LinkedIn is forcing us to go to their website to read our mail. Strangely, I haven’t found anyone talking about this on the interwebs. Am I the only person who doesn’t like this change? I mean, I don’t usually write rants in my weekly blog, but this new messaging functionality is not working for me. Here’s What I Don’t Like: I have to click on a message and go to LinkedIn without knowing whether I want to read the message. It’s often a waste of time. Flagging messages for follow-up has become less integrated. I can flag the message in my inbox, but when I want to follow up, I need to go to the LinkedIn platform to remind myself what the conversation was. LinkedIn seems to be pushing people to subscribe to LinkedIn Premium in order to have their messages seen. The thing is, if someone sends me an inMail, I can view the entire message. So I’m encouraged to send inMails, which are only available through LinkedIn Premium, instead of sending regular messages which can’t be read from people’s inboxes. I find myself not wanting to click, not wanting to go to LinkedIn. Since it’s my job, I do it. But what about the people receiving my messages? Will they open them? I’m afraid fewer and fewer of my non-inMail communications will be read as people get tired of blindly clicking on â€Å"View Message.† LinkedIn didn’t send any notification that I know of to their subscribers letting us know about this change. For a while after the Microsoft merger, communications from LinkedIn seemed to have gotten better. They were announcing changes before they happened! Recently, however, there have been no announcements, no notifications. I don’t like being in the dark, especially as someone whose job it is to advise people about changes in the LinkedIn platform. Take Action Am I alone here? Anyone else who is peeved by this change? Or has the collective LinkedIn community thrown its hands up in the air on this one? In the past, when enough people have complained about a change, LinkedIn has reverted back to the preferred functionality. Perhaps we can change the way our emails are appearing? If you’re behind me, please let LinkedIn (and others) know! Here’s How to Send Feedback to LinkedIn To send your feedback to LinkedIn, visit LinkedIn Help at https://www.linkedin.com/help/linkedin/solve/feedback and suggest they improve this feature. You could write something like this: Area of Feedback: Message Notifications Your Question: LinkedIn, please change the message notifications back to the way they were. I would like to see full messages from my connections in my email inbox again. I dont want to have to click through to my LinkedIn account to view their message. Thank you! Share on Social Media If you want to share this article via social media, you might like to use the following: What are your thoughts on LinkedIns new empty message notifications in your mailbox? I personally dont want to log in to see my messages. LinkedIn, #changeitback! #linkedinhelp #linkedinfail @LinkedInHelp @LinkedIn  https://goo.gl/wHzDHn If you want to see more LinkedIn tips and information like this right in your inbox, sign up for my LinkedIn Professional Writing e-list. And if youre ever interested in working with me on your LinkedIn profile or other strategies, check out my  20-minute live LinkedIn profile review. Its a great place to start!

Analyst Management Summary Report of Vodafone UK Essay

Analyst Management Summary Report of Vodafone UK - Essay Example Such a thorough assessment and comparative study with one of its peers, namely France Telecom-Orange, would enable one to recommend whether or not to make an investment of ?1Million in the stocks of Vodafone. Company Profile Vodafone is a global telecommunication company, operating in above 30 nations across the world and with more than 404 million customers (Vodafone, 2012). The company is listed in the London Stock Exchange and has 49,180.6 million shares outstanding (Bloomberg, 2012). ... Additionally, the company had been continuously giving out dividends to its shareholders since the last four years. Financial Ratio Analysis The liquidity position of an organization can be evaluated with the assistance of its current ratio and quick ratio. These ratios establish the organization’s capacity to meet its short-term liabilities. The current ratio can be determined as the ratio of the current assets to the current liabilities of the company, while the quick ratio is computed by dividing the quick assets by the current liabilities. It should be noted that the quick asset of an organization cosist of its cash, receivables and short term marketable investments (Brigham & Ehrhardt, 2010). The liquidity ratios of Vodafone are as follows: Year Mar-11 Mar-12 Current Ratio 0.63 0.83 Quick Ratio 0.61 0.81 The analysis of a company’s capability to generate cost-effective sales by means of its resources can be assessed by means of its profitability ratios. These ratio s include the gross profit margin, the net profit margin as well as the returns on equity and assets of the company. The gross profit margin of a firm is the ratio of its gross profit to revenue, while net profit margin is the ratio of net profit to revenue. Then again, the return on equity (ROE) of a company is the value of net income as a percentage of total shareholders’ equity while the return on asset is the value of net income as a percentage of its total assets (Brigham & Ehrhardt, 2010). The profitability ratios of Vodafone are as follows: Year Mar-11 Mar-12 Gross Profit Margin 32.84% 32.04% Net Profit Margin 17.37% 14.99% Return on Equity 9.10% 9.04% Return on Asset 5.27% 4.98% The solvency position of a company can be determined by means of

European Law Essay Example | Topics and Well Written Essays - 1750 words

European Law - Essay Example there are The European Committee, The European Court of justice, The European Court of Accounts, The Economic and Social Committee, The Committee of the regions, The European Ombudsman, The European Bank of Investments and The European Central Bank as the institution the EU (Dumitru). The European Parliament is the apex body of the European Union. It represents the citizens of its 27-member countries who are European Union citizens by virtue of their respective country’s membership in the Union. The Parliament members are directly elected by their countries’ people to safeguard their interests in the E.U. Every E.U. citizen is entitled to vote from any E.U. member state they live. Thus every E.U. citizen has right to contest in the election for the E.U.Parliament held once in five years. Thus, the Parliament represents the democratic aspirations of its people through as many as 736 elected members from 27 Member States. Each Member State has a defined number of members to represent and there are seven political parties called political groups transcending the identities of individual member countries besides independently elected members (non-attached) without allegiance to any of the political groups. The Parliament’s General Secretariat is in Luxembourg. While its plenary sessions are held in Strasbourg, Committee meetings take place in Brussels. Parliament is vested with the power for enactment of European legislation. As the laws are passed by the elected members of the Member States, they lend legitimacy to the enactments and become binding on the Member States. As the European Council also has legislative power, the Parliament passes legislation through co-decision with the council. In some subjects, the Council alone can enact but with the consultation of the Parliament. In certain matters of admitting countries as members, Parliament’s assent s required. Second, it supervises the E.U institutions’ functioning. Its supervision over the E.U.