The leadership style of Catherine the Great Research Paper

The leadership style of Catherine the Great - Research Paper Example The empress Catherine II is one of the brightest phenomena of the Russian history. The first half of her life, up to the accession to the throne, is represented as a series of fortunes, which did not depend on personal qualities of the modest German princess. But she managed to play the results of each of her successes in the best way, showing the rare ability to overcome any constraining circumstances as well as a moral ban, if was necessary for the achievement of a specific goal. The second half of the biography of Catherine The Great, a Russian Empress, is a series of the episodes, which arose spontaneously, not due to someones conscious will, and quite often had the most improbable endings. A motley round dance of events, in which the wisest plans and projects stuck and broke up, and instinctively made situational decisions often led to historically important consequences. This chaotic kaleidoscope developed a majestic picture of an eminence of the Russian Empire. Having headed t he triumph of her second homeland, the Empress Catherine II occupied a deserved place among historical heroes (Cronin, 1978). Catherine was not tall, but always held her chin highly lifted, and her back ideally straight. One her glance was enough to show his place to an impudent. â€Å"The double doors opened and the Empress appeared. I have said that she was quite small, and yet on the days when she made her public appearances, with her head held high, her eagle-like stare and a countenance accustomed to command, all this gave her such an air of majesty that to me she might have been Queen of the World; she wore the sashes of three orders, and her costume was both simple and regal; it consisted of a muslin tunic embroidered with gold fastened by a diamond belt, and the full sleeves were folded back in the Asiatic style. Over this tunic she wore a red velvet dolman with very short sleeves. The bonnet which held her white hair was not decorated with ribbons, but with the

A Study On Business Forecasting Statistics Essay

A Study On Business Forecasting Statistics Essay The aim of this report is to show my understanding of business forecasting using data which was drawn from the UK national statistics. It is a quarterly series of total consumer credit gross lending in the UK from the second quarter 1993 to the second quarter 2009. The report answers four key questions that are relevant to the coursework. In this section the data will be examined, looking for seasonal effects, trends and cycles. Each time period represents a single piece of data, which must be split into trend-cycle and seasonal effect. The line graph in Figure 1 identifies a clear upward trend-cycle, which must be removed so that the seasonal effect can be predicted. Figure 1 displays long-term credit lending in the UK, which has recently been hit by an economic crisis. Figure 2 also proves there is evidence of a trend because the ACF values do not come down to zero. Even though the trend is clear in Figure 1 and 2 the seasonal pattern is not. Therefore, it is important the trend-cycle is removed so the seasonal effect can be estimated clearly. Using a process called differencing will remove the trend whilst keeping the pattern. Drawing scattering plots and calculating correlation coefficients on the differenced data will reveal the pattern repeat. Scatter Plot correlation The following diagram (Figure 3) represents the correlation between the original credit lending data and four lags (quarters). A strong correlation is represented by is showed by a straight-line relationship. As depicted in Figure 3, the scatter plot diagrams show that the credit lending data against lag 4 represents the best straight line. Even though the last diagram represents the straightest line, the seasonal pattern is still unclear. Therefore differencing must be used to resolve this issue. Differencing Differencing is used to remove a trend-cycle component. Figure 4 results display an ACF graph, which indicates a four-point pattern repeat. Moreover, figure 5 shows a line graph of the first difference. The graph displays a four-point repeat but the trend is still clearly apparent. To remove the trend completely the data must differenced a second time. First differencing is a useful tool for removing non-stationary. However, first differencing does not always eliminate non-stationary and the data may have to be differenced a second time. In practice, it is not essential to go beyond second differencing, because real data generally involve non-stationary of only the first or second level. Figure 6 and 7 displays the second difference data. Figure 6 displays an ACF graph of the second difference, which reinforces the idea of a four-point repeat. Suffice to say, figure 7 proves the trend-cycle component has been completely removed and that there is in fact a four-point pattern repeat. Question 2 Multiple regression involves fitting a linear expression by minimising the sum of squared deviations between the sample data and the fitted model. There are several models that regression can fit. Multiple regression can be implemented using linear and nonlinear regression. The following section explains multiple regression using dummy variables. Dummy variables are used in a multiple regression to fit trends and pattern repeats in a holistic way. As the credit lending data is now seasonal, a common method used to handle the seasonality in a regression framework is to use dummy variables. The following section will include dummy variables to indicate the quarters, which will be used to indicate if there are any quarterly influences on sales. The three new variables can be defined: Q1 = first quarter Q2 = second quarter Q3 = third quarter Trend and seasonal models using model variables The following equations are used by SPSS to create different outputs. Each model is judged in terms of its adjusted R2. Linear trend + seasonal model Data = a + c time + b1 x Q1 + b2 x Q2 + b3 x Q3 + error Quadratic trend + seasonal model Data = a + c time + b1 x Q1 + b2 x Q2 + b3 x Q3 + error Cubic trend + seasonal model Data = a + c time + b1 x Q1 + b2 x Q2 + b3 x Q3 + error Initially, data and time columns were inputted that displayed the trends. Moreover, the sales data was regressed against time and the dummy variables. Due to multi-collinearity (i.e. at least one of the variables being completely determined by the others) there was no need for all four variables, just Q1, Q2 and Q3. Linear regression Linear regression is used to define a line that comes closest to the original credit lending data. Moreover, linear regression finds values for the slope and intercept that find the line that minimizes the sum of the square of the vertical distances between the points and the lines. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .971a .943 .939 3236.90933 Figure 8. SPSS output displaying the adjusted coefficient of determination R squared Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 17115.816 1149.166 14.894 .000 time 767.068 26.084 .972 29.408 .000 Q1 -1627.354 1223.715 -.054 -1.330 .189 Q2 -838.519 1202.873 -.028 -.697 .489 Q3 163.782 1223.715 .005 .134 .894 Figure 9 The adjusted coefficient of determination R squared is 0.939, which is an excellent fit (Figure 8). The coefficient of variable ‘time, 767.068, is positive, indicating an upward trend. All the coefficients are not significant at the 5% level (0.05). Hence, variables must be removed. Initially, Q3 is removed because it is the least significant variable (Figure 9). Once Q3 is removed it is still apparent Q2 is the least significant value. Although Q3 and Q2 is removed, Q1 is still not significant. All the quarterly variables must be removed, therefore, leaving time as the only variable, which is significant. Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 16582.815 866.879 19.129 .000 time 765.443 26.000 .970 29.440 .000 Figure 10 The following table (Table 1) analyses the original forecast against the holdback data using data in Figure 10. The following equation is used to calculate the predicted values. Predictedvalues = 16582.815+765.443*time Original Data Predicted Values 50878.00 60978.51 52199.00 61743.95 50261.00 62509.40 49615.00 63274.84 47995.00 64040.28 45273.00 64805.72 42836.00 65571.17 43321.00 66336.61 Table 1 Suffice to say, this model is ineffective at predicting future values. As the original holdback data decreases for each quarter, the predicted values increase during time, showing no significant correlation. Non-Linear regression Non-linear regression aims to find a relationship between a response variable and one or more explanatory variables in a non-linear fashion. (Quadratic) Model Summaryb Model R R Square Adjusted R Square Std. Error of the Estimate 1 .986a .972 .969 2305.35222 Figure 11 Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 11840.996 1099.980 10.765 .000 time 1293.642 75.681 1.639 17.093 .000 time2 -9.079 1.265 -.688 -7.177 .000 Q1 -1618.275 871.540 -.054 -1.857 .069 Q2 -487.470 858.091 -.017 -.568 .572 Q3 172.861 871.540 .006 .198 .844 Figure 12 The quadratic non-linear adjusted coefficient of determination R squared is 0.972 (Figure 11), which is a slight improvement on the linear coefficient (Figure 8). The coefficient of variable ‘time, 1293.642, is positive, indicating an upward trend, whereas, ‘time2, is -9.079, which is negative. Overall, the positive and negative values indicate a curve in the trend. All the coefficients are not significant at the 5% level. Hence, variables must also be removed. Initially, Q3 is removed because it is the least significant variable (Figure 9). Once Q3 is removed it is still apparent Q2 is the least significant value. Once Q2 and Q3 have been removed it is obvious Q1 is under the 5% level, meaning it is significant (Figure 13). Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 11698.512 946.957 12.354 .000 time 1297.080 74.568 1.643 17.395 .000 time2 -9.143 1.246 -.693 -7.338 .000 Q1 -1504.980 700.832 -.050 -2.147 .036 Figure 13 Table 2 displays analysis of the original forecast against the holdback data using data in Figure 13. The following equation is used to calculate the predicted values: QuadPredictedvalues = 11698.512+1297.080*time+(-9.143)*time2+(-1504.980)*Q1 Original Data Predicted Values 50878.00 56172.10 52199.00 56399.45 50261.00 55103.53 49615.00 56799.29 47995.00 56971.78 45273.00 57125.98 42836.00 55756.92 43321.00 57379.54 Table 2 Compared to Table 1, Table 2 presents predicted data values that are closer in range, but are not accurate enough. Non-Linear model (Cubic) Model Summaryb Model R R Square Adjusted R Square Std. Error of the Estimate 1 .997a .993 .992 1151.70013 Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 17430.277 710.197 24.543 .000 time 186.531 96.802 .236 1.927 .060 time2 38.217 3.859 2.897 9.903 .000 time3 -.544 .044 -2.257 -12.424 .000 Q1 -1458.158 435.592 -.048 -3.348 .002 Q2 -487.470 428.682 -.017 -1.137 .261 Q3 12.745 435.592 .000 .029 .977 Figure 15 The adjusted coefficient of determination R squared is 0.992, which is the best fit (Figure 14). The coefficient of variable ‘time, 186.531, and time2, 38.217, is positive, indicating an upward trend. The coefficient of ‘time3 is -.544, which indicates a curve in trend. All the coefficients are not significant at the 5% level. Hence, variables must be removed. Initially, Q3 is removed because it is the least significant variable (Figure 15). Once Q3 is removed it is still apparent Q2 is the least significant value. Once Q3 and Q2 have been removed Q1 is now significant but the ‘time variable is not so it must also be removed. Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 18354.735 327.059 56.120 .000 time2 45.502 .956 3.449 47.572 .000 time3 -.623 .017 -2.586 -35.661 .000 Q1 -1253.682 362.939 -.042 -3.454 .001 Figure 16 Table 3 displays analysis of the original forecast against the holdback data using data in Figure 16. The following equation is used to calculate the predicted values: CubPredictedvalues = 18354.735+45.502*time2+(-.623)*time3+(-1253.682)*Q1 Original Data Predicted Values 50878.00 49868.69 52199.00 48796.08 50261.00 46340.25 49615.00 46258.51 47995.00 44786.08 45273.00 43172.89 42836.00 40161.53 43321.00 39509.31 Table 3 Suffice to say, the cubic model displays the most accurate predicted values compared to the linear and quadratic models. Table 3 shows that the original data and predicted values gradually decrease. Question 3 Box Jenkins is used to find a suitable formula so that the residuals are as small as possible and exhibit no pattern. The model is built only involving a few steps, which may be repeated as necessary, resulting with a specific formula that replicates the patterns in the series as closely as possible and also produces accurate forecasts. The following section will show a combination of decomposition and Box-Jenkins ARIMA approaches. For each of the original variables analysed by the procedure, the Seasonal Decomposition procedure creates four new variables for the modelling data: SAF: Seasonal factors SAS: Seasonally adjusted series, i.e. de-seasonalised data, representing the original series with seasonal variations removed. STC: Smoothed trend-cycle component, which is smoothed version of the seasonally adjusted series that shows both trend and cyclic components. ERR: The residual component of the series for a particular observation Autoregressive (AR) models can be effectively coupled with moving average (MA) models to form a general and useful class of time series models called autoregressive moving average (ARMA) models,. However, they can only be used when the data is stationary. This class of models can be extended to non-stationary series by allowing differencing of the data series. These are called autoregressive integrated moving average (ARIMA) models. The variable SAS will be used in the ARIMA models because the original credit lending data is de-seasonalised. As the data in Figure 19 is de-seasonalised it is important the trend is removed, which results in seasonalised data. Therefore, as mentioned before, the data must be differenced to remove the trend and create a stationary model. Model Statistics Model Number of Predictors Model Fit statistics Ljung-Box Q(18) Number of Outliers Stationary R-squared Normalized BIC Statistics DF Sig. Seasonal adjusted series for creditlending from SEASON, MOD_2, MUL EQU 4-Model_1 0 .485 14.040 18.693 15 .228 0 Model Statistics Model Number of Predictors Model Fit statistics Ljung-Box Q(18) Number of Outliers Stationary R-squared Normalized BIC Statistics DF Sig. Seasonal adjusted series for creditlending from SEASON, MOD_2, MUL EQU 4-Model_1 0 .476 13.872 16.572 17 .484 0 ARMA (3,2,0) Original Data Predicted Values 50878.00 50335.29843 52199.00 50252.00595 50261.00 50310.44277 49615.00 49629.75233 47995.00

causes of the great depression Essay -- essays research papers

The Great Depression was a decade of poverty for many United States citizens. Starting in 1929, The Great Depression was a rough time not only for the U.S. but for many other countries. There are many causes for the Depression but the main cause was the combination of the greatly unequal distribution of wealth throughout the 1920's and the extensive stock market speculation(Gusmorino, 1). Other causes were the unsteadiness of the stock market, short signed economic policies, overdependence on mass production, consumer spending, advertising, welfare capitalism, and high tariff. The effect on the country of the imbalance in the economy threw the U.S. into an era of negativity. How did the United States go from the â€Å"roaring twenties† to The Great Depression? It was all based on deflation and the crash of the economy. A good example of uneven distribution of wealth was Henry Ford’s yearly income of $14 billion the same year that the average income was $750. Another contributor to the uneven distribution was the government. Calvin Coolidge favored businesses therefore favoring the wealthy who invested in these businesses. For an economy to function properly, total demand must equal total supply. What happened in the 1920's was an oversupply of goods. This resulted in the middle-class needing more but not being able to afford more while the upper-class didn’t want to buy more goods. Three quarters of the U.S. population spent almost all of their yearly incomes to purchase consum...

Case of Thabo Meli V R

THABO MELI v R Fact of the case : The defendants had taken their intended victim to a hut and plied him with drink so that he became intoxicated. They then hit the victim around the head, intending to kill him. In fact the defendants only succeeded in knocking him unconscious, but believing the victim to be dead, they threw his body over a cliff. The victim survived but died of exposure some time later. The defendants were convicted of murder, and appealed to the Privy Council on the ground that there had been no coincidence between mens rea and actus reus in order to put them liable for murder.Principle of the case : Approach use is the series of acts. This approach involves treating a series of distinct act as continuent parts of a larger transaction. Liability may be attached where at some point in the series of acts, the accused has the necessary mens rea even if the mens rea does not coincide precisely in time with act causing death. Argument by the appellant: The appellant cont ended that the two acts done were separate acts.The first act was done accompanied by mens rea which did not caused the death but the second act that caused death. They argued that the second act was not accompanied by mens rea, therefore, they were not guilty of murder. Defence by the respondent : it appears from the medical evidence that the injuries which deceased received in the hut were not sufficient to cause the death and that the final cause of his death was exposure where he was left at the foot of the krantz.There is no doubt that the accused set out to do all these acts in order to achieve their plan. Judgment of the case : It was impossible to divide up what was really one series of acts; the crime was not reduced from murder to a lesser crime, merely because the appellants were under some misapprehension for a time during the completion of their criminal plot; and, therefore, the appellants were guilty of murder.

Are Punchlines Necessary for Ads? Essay

Punch lines are the need for advertising the product as they are necessary for a product to be unique and be different. For example: â€Å"The complete man† which makes us remember the company Raymonds. Punch lines are important to make the customer remember about the product identity. It helps to recall a product easily. But it cannot rule the advertisement as ultimately the customer remembers quality and cost. Customer only sees whether he is getting the satisfactory service or not. Also after service of a product offered by company is important if the company is good, gets reputation and rules the market. Only attractive punchlines will not help. For example, if I say â€Å"Paanch matalab chota coke†, it will suddenly remind you the ad of coca cola (even though it’s not on air now). So, this way punch line increases the recall value of the ad but if your ad or positioning of the product is not perfect then you can’t expect your advt programme to be successful just on the basis of punchlines. It is very difficult to find out the right Punchline. Marketers have to select right words to form that sentences which can correctly express the positioning strategy of the brand. A bad Punchline can kill a good ‘ad’. If the Punchline strikes customers as attractive due to repeated exposure it ‘changes’ the mindset of the customer creating new set of beliefs. The Punchline represents the values of the company, benefits, attributes, features, quality, cost, special technology. If we really want to appreciate the value of Punchlines, then imagine an advertisement without any Punchline. It looks like a dumb. So basically the Punchline is the voice of the brand, which primarily gives out the minimum momentum, thrust to push the brand in the mind of the customer. A punch line has to have an element of surprise in it. Humour is also an essential aspect of advertising because a dose of laughter instantly connects the masses with a campaign. The main objective of advertising is to appeal to the consumer and a punch line should always be linked with the product. Also, there has to be something new, something which the people can connect with instantly. The best punch line strikes a chord with people and creates magic. But, certain good companies such as â€Å"Colgate† don’t have got a punch line but still it is ruling the market for years. Basically, the work of punch line is to own a space in the minds of customer and create some easy recall of the ad. But it takes a lot of imagination to come up with something as simple yet as effective as gale ki khichkhich from the Vicks campaign. Such was the power of this simple line that now, irritation in throat is called khichkhich by a majority of people. And it instantly connects the feeling to Vicks. This linking of products, or brand recall, is what makes a punch line successful. Products come and go, but punch lines always stay.