New World Meets the Old World-Spanish Settlement in Texas Essay

New World Meets the Old World-Spanish Settlement in Texas - Essay Example The various changes forced the two cultures to adapt due to changes brought by conflicting cultures but the Native American culture was the most affected. This, the paper focuses on the impacts that arose from the conquest of Texas by the Spaniards and the degree to which the locals suffered from cultural deterioration and imposing of new culture (Bolton, 2011). The Mayans, Aztecs, and the Toltecs occupied the western hemisphere, which formed major societies in Texas before invasion of the Spaniards. The Native Americans were initially hunters and fruit gatherers. With the invasion of the Spaniards, it brought three different worlds of Africa, Europe, and America into contact. In 1492, there were tribal extermination and individuals due to clashes in culture between the Native Americans and the Spaniards. This caused many deaths due to deadly diseases, which were brought in by the European newcomers. If disease and deaths moved haphazardly, from one Spaniard to another, Christianity was not an exception as it went through the same direction. In America, early encounters involved early missionaries, which included both the Protestants and Catholics who worked towards converting the natives to the new Christian faith. Reverend John Elliot who worked as the bible translator in 1663 translated the bible into Massachusetts’s language. ... One native by the name Pueblo made efforts to fight back by forming revolts in 1680. The Indian rebels made efforts to expel the Spanish colonizers. During the revolt, Pueblo attacked many missionaries, punished the Christians converts, and burned numerous churches. While these effects affected the Spaniards, education and trade were moving in two directions. Europeans were highly educated in the society. In America, the Spaniard elites were in a position to read and write. Thus, they started to spread their culture this through schools. In this regard, the Harvard College was built up in the 17th century. This was followed by the Dartmouth College, which was built in the 18th century. This college was meant to serve just a few individuals in society. The Indians who taught the natives on geography, climate, and food facilitated education. This enabled the natives to plan on planting and harvesting crops and other economic activities for their sustenance. Trading became an economic a ctivity but it was carried in two different ways. At first, the Europeans were using American land to cultivate it and ship the harvest back home while making huge profits. Though precious metals were the most profitable, there was no sign of any in Texas. The Europeans found fur, which was in plenty as the raw materials they could ship home. In South East, they found the soft hides from the whitetail deer which could be scraped, packed and later shipped back to Europe to make gloves and aprons. According to Kessell (2003), it was common for the natives to hunt animals, process their pelts and later ship to Europe. The barter trade had numerous anomalies as the Native Americans were unfamiliar to the products that came from Europe but European countries

Time Series Analysis Essay Example | Topics and Well Written Essays - 1000 words

Time Series Analysis - Essay Example This presentation can be used to model many time series procedures and as an identifying tool of a model in the auto- covariance function. ARIMA (1, 1, 1) vs. ARIMA (0, 1, 2) The ARIMA models as observed help in fitting provided data with the condition that the data is not stationary. There are many models of the ARIMA but in our case we will discuss ARIMA (1, 1, 1) and ARIMA (0, 1, 2) looking at the trees presented with relevant discussion about them. ARIMA (1, 1, 1) is also referred to as the mixed model, this is due to the fact that as depicted from the graphs by the 9 trees, we see he features of both the autoregressive and moving average models brought together to form a single model. ARIMA (1, 1, 1) which is non-linear in nature can be used to define the data set that shows unpredictable bursts, outliers and extremely flat stretches at quite irregular time intervals (Cromwell, 1994). The data may have been collected from the economic unit variables like those for the pricing of items like onion\ns and their variations in the market. The research may have also been conducted in conjunction of other extreme models like the Gaussian Mixture Transition (GMTD), Mixed Autoregressive (MAR) as well as MAR-Autoregressive Conditional Heteroscedastic (MAR-ARCH), the differences are determined and graphs depicting differences depicted as in the Trees 1-9 ARIMA (1, 1, 1). The graphs represented by the numbers and the progress show an eliminating trend with quite seasonal fluctuations as shown from the fittings in the Box-Jenkins hence residual series (Vandaele, 1983). The figures and graphs from the trees 1-9 are employed in testing for non-seasonality or seasonality in the respective stochastic trends with the appropriate filters being used through the Box-Jenkins model examining the same. Trees 1-9 show us that the Lagrange multiplier (LM) is used to define ARCH while the value parameters are quantified using Expectation maximization (EM) (Cromwell, 1994). The figur es, graphs and diagrams show a case where out of sample forecasting the first and the second steps and there after a naive approach devised in forming a conclusion. With ARIMA (0, 1, 2) on the other hand, we ask ourselves how the data would look like, and the pattern that would exist. As shown by the trees 1-9, the data is non-stationery as show by the linear filters and transfer functions indicating smoothing potentials. From the tools, that is the plots of data and both the PACF and ACF, the evidence for the claims above are vividly observable by the graphical trends and the trends by ACF of residuals, standardized residuals and p values for Ljung box (Cromwell, 1994). The models of ARIMA (0, 1, 2) as opposed to that of the ARIMA (1, 1, 1) has its parameters estimated using a statistical software with the outputs indicated on the representation showing outputs for parameter estimates, test statistics, goodness of fits, diagnostics and residuals. All the above parameters are highly non-stationery as well (Vandaele, 1983). In both the models, it is to be determined whether they fit data by correctly extracting all information and ensuring that residuals as shown are a white noise. The key measures in both the models are the ACF, standardized