To what extent was the increase in electoral support for the Nazi Party in the years 1928-33 the result of effective propaganda and electioneering? By Slmran_D To what extent was the increase in electoral support for the Nazi Party in the years 1928-33 the result of effective propaganda and electioneering? The years 1928 to 1933 were very significant for the Nazi Party and their leader, Adolf Hitler. After the attempted Munich Putsch, the Nazi Party had well and truly entered the political spotlight of German politics and had successfully re-established Itself after Hitler was eleased trom prison In 1924.
Following on trom being so heavily In the public eye, the Nazi Party had a nse in support due to Increased awareness of the party, but 1928 marked a steep increase in this. There are many factors that contributed to this increase in electoral support for the Nazi Party from 1928-1933. but it was largely due to effective propaganda and electioneering. Nazi propaganda was under leadership of Joseph Goebbels. who was able to identify the concerns of all sectors within the German population and use this to create a remarkable propaganda scheme.
An example of this is where workers were addressed with posters depicting endless queues of unemployed people. suggesting that Hitler would be able to abolish unemployment – a miracle that was readily accepted by this group. As well as targeting specific groups, Goebbels was able to manipulate a variety of propaganda techniques, from using posters to nursery rhymes to the radio. The Nazi Party presented an image that appealed to everyone through their use of propaganda.
It iS because of propaganda that they were able to convince the public of their ability to ransform the country to one ridden Hith debts, political instability and embarrassment to a global superpower that was successful in every aspect, Even now, Nazi propaganda iS still viewed as a remarkable achievement for the Nazi Party and is undoubtedly one Of the main reasons Why Nazi support Increased from 1928-1933. Electioneering is the other main reason for the increase in electoral support. In terms of Nazi policy itself, although it was controversial, Hitler was careful to be very careful about certain points – especially regarding religion.
The 25 Point Programme of the Party depicted that It represents the point of view of postlue Christianity, whereas in reality the Nazi Party stood for the complete opposite. With the majority of Germans being Christian (2/3 of these were Catholic and 113 Protestant), the Nazi’s could not afford to alienate the Church and risk repelling the majority of the electorate. Throughout their electioneering, the Nazi’s used the SA to intimidate the opposition and sometimes even carry out such violent attacks that political figures. specially Communist politicians. were unable to continue their own electioneering.
This allowed the Nazi party to reduce their opposition and allowed them to manipulate the public further using their effective propaganda. As the Nazi Party organised propaganda rallies to build up electoral support, they also organised pl hy that the party was extremely popular and therefore encouraged voters that might otherwise vote for another larger party to support the Nazi’s instead. These propaganda rallies were used to persuaded all of Germany to vote for Hitler and his arty because Hitler was able to constantly travel around the country using an airplane.
This electioneering meant that he was able to directly address potential supports and use propaganda to convince them to vote Nazi. It could be claimed that Hitler himself contributed largely to the electoral success of the Nazi Party. Many women found Hitler aesthetically attractive and emphasis was placed on his bright blue eyes and his friendly nature towards children. Men found Hitler as a man to admire – he was charismatic and some newspapers even labelled him ‘Hitler the Superman’.
Above all, Hitler was a brilliant auditor and had the ability to captivate audiences that was unmatched by no other politician and delivered speeches with such power that it was hard not to be swept in by his manipulative and misleading words. However, it could not be said that Hitler himself contributed to the increase in electoral support to a larger extent than propaganda and electioneering. Another factor that could be argued to have resulted in the increase in electoral support is the very climate of Germany during that time.
Following on from the failure of war in 1918, Germany was stampeded with crises after crises. In economic terms, the funding of the war had resulted in inflation and forced Germany to borrow loans from the USA in order to pay the E6600 million in reparations. This meant that when America suffered from the Wall Street Crash, Germany was hit by Depression in 1926. Living standards plummeted and Germany was on the verge of a civil war. Consequently, the German people looked towards extremist parties to provide them ith the revolutionary change needed to return Germany to a least a partial stability.
Again, whilst this was a big factor in increase of Nazi support, the situation in Germany was balancing out, especially as the effects of hyperinflation were weakening by 1925. Therefore, the hardships that Germany faced during this period cannot be accountable for the increase in the Nazi Partys electoral success compared to the propaganda and electoral success that was constant throughout 1928-1933. In conclusion, the increase in electoral support for the Nazi Party in the ears 1928-33 was the result of effective propaganda and electioneering to a far extent.
A variety of factors contributed to this increase, including Hitler’s personality and the downfall of Germany during the post-war years. However, the strongest factors were persistently the Nazi’s propaganda campaign that was led by Goebbels and their electioneering methods. Through this, they were able to specifically target groups within the electorate and develop the Nazi image itself which drew support from voters and caused the German people to trust them to restore Germanys former status.
FORECASTING USING R LANGUAGE 6 Running Head: FORECASTING USING R LANGUAGE 1
FORECASTING USING R LANGUAGE 6
Running Head: FORECASTING USING R LANGUAGE 1
Forecasting Using R Language
Economic Forecasting using R language
HW 8 (Due to Shi by 8:30 a.m. on April 8, 2019)
Importing monthly housing starts data from January 1946 to November 1993
data <- read_csv("data.csv")
The deterministic trend model (estimate) is obtained as follows:
x <- diff(log(data$HSTARTS))
ts.plot(x,col=”blue”,main=”Time series plot for the Monthly housing starts data”)
The time series plot of the housing data exhibit a pattern of fluctuations throughout the period 1946-1993.
Interpretation of the Intercept Estimates and Interpretation of the Slope Coefficients
The regression model is fitted on the monthly housing data and the corresponding coefficients for every month are obtained. The values are transformed to a linear function by applying logarithms to the housing starts so that a linear regression model could be fitted.
lm(formula = ln ~ as.factor(month))
Min 1Q Median 3Q Max
-1.00641 -0.18697 0.02146 0.18645 0.65542
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.86080 0.04205 115.593 <2e-16 ***
as.factor(month)2 -0.08424 0.05755 -1.464 0.1438
as.factor(month)3 -0.06496 0.05887 -1.103 0.2703
as.factor(month)4 -0.04392 0.05887 -0.746 0.4559
as.factor(month)5 -0.03029 0.06196 -0.489 0.6251
as.factor(month)6 -0.12853 0.05887 -2.183 0.0294 *
as.factor(month)7 -0.12695 0.06011 -2.112 0.0351 *
as.factor(month)8 -0.09845 0.05887 -1.672 0.0950 .
as.factor(month)9 -0.08072 0.05831 -1.384 0.1668
as.factor(month)10 -0.09162 0.06237 -1.469 0.1424
as.factor(month)11 -0.14053 0.05916 -2.375 0.0179 *
as.factor(month)12 -0.13518 0.05731 -2.359 0.0187 *
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2913 on 575 degrees of freedom
Multiple R-squared: 0.0211, Adjusted R-squared: 0.002375
F-statistic: 1.127 on 11 and 575 DF, p-value: 0.3373
From the estimates of the seasonal model, the intercept is highly significant since it has a p-value less than 0.05, level of significance. The coefficients for June, July, November and December are also significant in explaining the variation in the housing starts. The fitted model shows that most of the coefficients are not significant at 0.05 levels; therefore it is clear that the model may not give a accurate representation of the housing data.
b) Testing the hypothesis that there is no seasonal variation, we have;
H0: There is no seasonal variation
H1: There is seasonal variation
Using the p-value from the results below and comparing it with 5% standard confidence level, we have;
One Sample t-test
t = 85.374, df = 586, p-value < 2.2e-16
Alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
Mean of x
Now having enough evidence at 5% level of significance we therefore reject the null hypothesis that there is no seasonal variation. The data exhibits seasonal variation where the period of the cycle is one month.
Auto.arima(x = data$HSTARTS, order = c(0, 1, 1), seasonal = list(order = c(0, 1,
1), period = 12))
s.e. 0.0409 0.0308
Sigma^2 estimated as 119.1: log likelihood = -2196.7, aic = 4399.4
The fitted ARMA (0,1,1) model showing predicted versus actual values.