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The data gathered can be used to perform a number of operations such as mean, standard deviation, variance, correlation etc. Therefore it is safe to say that real data makes it possible to make quantitative classifications. That is why we can say that real data makes it possible to run statistical analysis.
The research has been carried out on the results of 2008 American Presidential Elections. The Exhibit 1 shows actual results of elections. The tables are divided according to percentage lead of each president according to states. The data in Exhibit 2 shows pre election polls for each candidate. The data in Exhibit 2 two has been divided according to agencies which had delivered results or conduction these pre election polls. Column D in Exhibit 2 reflects leads to each respective president in states of polls. The data presented is real in nature for Exhibit 2. This is because the format is percentages of actual responses received from the public. Exhibit 1 also shows actual historical data as the responses are shows as percentage of total votes received by each presidential candidate.
In column E of exhibit 2 we have prepared another category denoted by numbers. This is a better way to convert real scale to nominal scale and then convert it to percentage to get a solution. The number ‘2’ represents a tie, ‘1’ lead of Obama and ‘0’ lead of McCain. If we calculate the percentage of ‘1’ to the entire population we can calculate how many polls considered Obama to win the elections. The percentage of polls that showed Obama as the winner were 71% where as only 10% predicted a tie of votes. This shows another quality of nominal data that it has to be converted into percentages to reach an analysis. The presidential elections did show a victory for president Obama which reveals that analysis using real data was successful in predicting election results.
Nominal data can lead to only qualitative
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This is realized through predictive analysis data mining, which offers the users, impactful insights throughout the organization (Greene, 2012). Predictive analytics is where statistics and mathematics integrate to business and marketing to establish patterns in data and extrapolating the patterns to future business cases and issues, so as to reduce costs, improve response rates, increase the efficiency of processes and consequently boost revenue levels.
Governments have minimized this bias by giving their central banks a legal independence. The flat money system adoption has resulted to the present rise in the inflation rate of many countries. The rise in inflation tends to differ from one nation to the other.
The Federal Reserve has taken unprecedented proceedings in the financial markets since the economy was plunged into financial crisis. Remarkable examples consist of lending in excess of $1.5 trillion to monetary institutions and buying almost $1.25 trillion of mortgage-backed securities to make the financial system stable.
The Comité Consultatif International Téléphonique et Télégraphique (CCITT) is an association that has specified the particular rules for the standard data compression. There are different methods intended for
Mu stands for the true value of the mean ( for the data to have insignificant differences the mean =0).
T-tests and ANOVA are the recommended ways of establishing whether there exist significant differences between the means of two or more inputs. In this case the aim
ped May know specific behaviors of her students, thus learning what needs each one requires, with different needs, it made her view each student as a unique individual with different strengths and weaknesses. During collection of her data, she will have a clear picture of her
She also observed that Ms. Steward had less work redirecting the students, if she increased eye contact with them. Sienna in her second observation indicates that giving the students something to look forward kept them in. In her