The two classics provide a comprehensive analysis of diverse roles of women. In Medea, for instance, the Medea employs figurative speech to explore the social injustices that repeatedly touches on the social aspects of women. From such a position the audience is allowed to examine the position of a woman in the society. The position in this case touches on woman subordination to man and this are inextricable from the innate features surrounding the social order during this period in Greece.
Hence, in Medea the audience is permitted to explore the suffering of a woman who has been rejected, twisted and abused because of her gender. In essence, this demonstrates how woman was treated in the Greek society. What this signifies is that the woman is painted as a social misfit and this resulted in Medea going against the social prescription of a married woman.
However, when we examine the approaches employed by Antigone it becomes obvious that she Antigone was a reflection of a woman at home. However, the conflict that ensued between her and Creon provides a new dimension to the entire setting of the plot. Consider the fact that Creon is depicted as a tyrant in the play. This is testified in the manner he handles the issue regarding the burial of Polyneices, Antigones slain brother (Fagles 142).
From a tragic perspective his rigid stance on the social injustices makes him to be a villain. When Media is exiled one is left pondering what would it be for a woman to live as a villain. This concept paints Media as a heroine while in Antigone Creon is depicted as a ruthless villain.
All in all, despite their differences the two characters can be explored in a similar light. Consider the fact that Creon employed ruthless tactics to guard his position in the society. Equally, Medea utilized her personality to speak against the social injustices that touched on woman affairs in the society.
The way Medea is portrayed as submissive equally matches Creon persistence in protecting his position as the leader. Also, Medea stands out as the symbolism of early aspects of feminism. And this compares with Creon approach to the social life examined in Antigone. Looking at the two characters it is thus possible to argue there exists another angle of examining them both as being heroes rather than being villains in their respective social standings.
For instance, Creon handled Antigone and rejected the idea of her burying her brother for as a leader he considered doing so would encourage revolt against the ruling class, while on the other hand Medea stood strong in conviction that being submissive gave her strength to live as a woman. Despite the contrast and the approach employed in both Greek tragedies the two characters do share diverse but unique similarities (Fagles 154).
Get your 100% original paper on any topic done in as little as 3 hours Learn More In both cases justice is the core element that has been examined. Looking at the character depicted by Creon we find the crude power generated by greed while Medea reveals the strengths of being a woman despite the social challenges. In such a situation it becomes essential to assert that Creon symbolized evil and that is why he is depicted as a villain. However, he had a soft spot that made him to be a hero.
Work Cited Fagles, Robert. The Three Theban Plays. New York: Penguin, 1999.
Human Relations – Walmart Essay
Nursing Assignment Help Part a
Correlation analysis differs from regression analysis in that: 1) it determines if a relationship exists between two variables, and 2) if the relationship exists, it identifies it. Regression analysis on the other hand, attempts to predict the value of a dependent variable by using a single or multiple independent variables.
The correlation coefficient tells of the strength of the relationship between two variables (Doanne and Seward, 2007, 490). Additionally, if it is a positive value e.g. 0.1 it tells that the relationship is positive and if it is a negative value e.g. -0.1 it tells that the relationship is negative.
The quick rule for the significance of a correlation at α = 0.05 is |r| > 2 / √ n. The limitation for this rule is that it can only be used for α = 0.05.
Get your 100% original paper on any topic done in as little as 3 hours Learn More For i = 1…n, the sums needed to calculate a correlation coefficient are: 1) the sum of the product of xiyi, 2) the sum of xi, and 3) the sum of yi.
The two ways of testing the significance of a correlation coefficient are: 1) using the t-test, where the test statistic, r, is r√((1 – r²)/(n – 2)) , and 2) the Z-test, where Z = ln[|(r 1)/r-1)|]/2 .
From the question, Let x = weekly pay, y = income tax and n = 35. From the ANOVA table, the fitted regression equation is ŷ = 30.7963 0.0343x.
From the output table, the degrees of freedom are 33 and the critical value at α =0.05 from appendix D is 2.035.
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Since the p-value = 0.0161 is less than α = 0.05 we conclude that the correlation coefficient, r, is zero and accept the slope value of 0.0343.
With a 95% level of confidence, the true slope lies inside the closed interval 0.0101 and 0.0584.
Now, t = 2.889 and t² = 8.35 = F. This is the verification.
From the table, since r² = 0.202 then the regression equation, ŷ = 30.7963 0.0343x, explains 20.2 % of the correlation that exists between the two variables.
Not sure if you can write a paper on Human Relations – Walmart by yourself? We can help you for only $16.05 $11/page Learn More Question 12.50
From the question, X = total assets ($ billions), Y = total revenue ($ billions), and n = 64.
From the regression output, the fitted regression equation is ŷ = 6.5763 0.0452x.
The regression output shows that the degrees of freedom are 62. The critical value at α = 0.05 is 1.999.
Now, from the regression output t = 80813, which is much greater than the critical value of 1.999. Thus, the conclusion is that, the true slope ≠ 0.
With a 95% level of confidence, the true slope lies inside the closed interval 0.0342 and 0.0563.
Now, t=8.183. t²= 66.96 ≈ F. Thus F = t² for the slope.
X1 as the predicting value accounts for more than half of the variability of y. Additionally, the true slope of the line is not zero. From these two facts we conclude that X1 is a good predictor of y.
From the stepwise regression analysis and the results we note that: 1) addition of variables causes an increase in r², which is normal behaviour, and 2) the addition of infant mortality causes r² to decrease in tiny amounts. From these observations, we can conclude that: 1) the variables used in the analysis do not give the model sufficient exploratory power.
A likely reason for such a case to occur is that the variables in the analysis might contain to a significant extent the same information. 2) The addition of variables GDPcap and Literate causes LifeExp and InfMort to be no longer highly significant.
The results of the regression analysis describe a highly predictive regression model. This is because the independent variables in the analysis explain 81.1% of the variability of the dependent variable.
The graph is shown is shown in Appendix A.
America was in war in the 1960’s and 1970’s thus the high number of aviation shipments during these periods is a result of increased creation of war planes
A fitted trend would not be helpful for the above data as no trend reveals itself whether linear or not.
The graph is shown in appendix B. Again, a fitted trend would not be helpful for the above data as no trend reveals itself whether linear or not.
The best trend model for the data is shown in Appendix C where the regression equation is, ŷ = 182.21x – 362294 and r² = 0.7257. The forecast for 2004 is given by substituting x with 2004 in the regression equation. Thus, the forecast is 182.21 ×2004 – 362294 = 2854.84. The reason why it is good to ignore the earlier years is so that we get a sub-dataset that can be properly analyzed using an appropriate regression model.
Quiz question one.
The table in Appendix D gives the respective starting salary averages (in thousands of dollars) for each gender in each of the MBA majors. Additionally, it also gives the starting salary averages (in thousands of dollars) for each of the MBA majors.
From the averages in this table, the dean can conclude that: 1) males are paid more than females in each of the MBA majors except marketing where the opposite is true. 2) Finance is the highest paying MBA major followed by Accounting followed by Marketing and Management is the least paying MBA major. 3) Both males and females should major in Finance if they wish to maximize their starting salaries.
Quiz question two.
Let p1 be the proportion of male doctors who took 325 mg asprin tablet. Thus, p1 = 104/11307 = 0.00942. Let p2 be the proportion of male doctors who took placebo. Thus, p2 = 189/11304 = 0.01713.
The null hypothesis is: H0 = p1 – p2 ≥ 0, which when interpreted means that the proportion having heart attacks is not significantly lower for male doctors who took 325 mg aspirin tablet than for male doctors who took placebo. The alternative hypothesis is : H1 = P1 – P2 < 0, which when translated means that the proportion having heart attacks is significantly lower for male doctors who took 325 mg aspirin tablet than for male doctors who took placebo.
The test for these hypotheses is two-tailed. Let z be the test statistic, thus z = z = (p1 – p2) – D0 / σp1-p2 where σp1-p2 = √ p (1 – p)(1/n1 1/n2). From the question and the hypotheses set, n1 = 11307, n2 = 11304, D0 = 0, p = 293/22611 = 0.013. Therefore z = ((0.00942 – 0.01713) – 0) / √ (0.013 (1 – 0.013)(1/11307 1/11304)) = -5.00. The p-value for z is 0.000000285.
Since the p-value is less than 0.001 we have extremely strong evidence that H0 if false and thus we accept H1 and conclude that, the proportion having heart attacks is significantly lower for male doctors who took 325 mg aspirin tablet than for male doctors who took placebo.
References Doanne, D. P. and Seward, L. E. (2007). Applied statistics in business and economics. (1st ed). McGraw-Hill/ Irwin: New York. 490
Appendix Appendix A
Figure 1. Graph for data on U.S. Manufactured General Aviation Shipments, 1966–2003
Figure 2. Graph for data on U.S. Manufactured General Aviation Shipments, 1992–2003
Figure 3. Graph showing fitted trend model for data on U.S. Manufactured General Aviation Shipments, 1992–2003
Table 1. Averages for each gender and MBA major
Accounting Finance Management Marketing Male 96.2 99.9 80.27 74.23 Female 84.2 82.9 60.23 78.1 Average 90.2 91.4 70.25 76.17