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In 2016, Trump won Georgia with 51.0% of the vote compared to Clinton's 45.9% with more than 211,000 votes separating them. In 2016, Clinton received 1,877,963 votes and Trump received 2,089,104. In 2020, Biden's tabulated votes (2,474,507) were much greater than 10. Clinton's in 2016. Trump's votes also increased to 2,461,837. The Biden and Trump percentages of the tabulations were 49.5% and 49.3%, respectively.

In 2016, Trump won Georgia with 51.0% of the vote compared to Clinton's 45.9% with more than 211,000 votes separating them. In 2016, Clinton received 1,877,963 votes and Trump received 2,089,104. In 2020, Biden's tabulated votes (2,474,507) were much greater than 10. Clinton's in 2016. Trump's votes also increased to 2,461,837. The Biden and Trump percentages of the tabulations were 49.5% and 49.3%, respectively.

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Question A) Using the following statement, explain what statistical test the scholar was trying to complete (a test for 2 population mean, a test for 2 population proportions etc.). Do not write your answer as a null and alternative hypothesis, but as a statement: In 2016, Trump won Georgia with 51.0% of the vote compared to Clinton's 45.9% with 10. more than 211,000 votes separating them. In 2016, Clinton received 1,877,963 votes and Trump received 2,089,104. In 2020, Biden's tabulated votes (2,474,507) were much greater than Clinton's in 2016. Trump's votes also increased to 2,461,837. The Biden and Trump percentages of the tabulations were 49.5% and 49.3%, respectively. 11. I tested the hypothesis that the performance of the two Democrat candidates were statistically similar by comparing Clinton to Biden. I use a Z-statistic or score, which measures the number of standard deviations the observation is above the mean value of the comparison being made. I compare the total votes of each candidate, in two elections and test the hypothesis that other things being the same they would have an equal number of votes.? I estimate the variance by multiplying the mean times the probability of the candidate not getting a vote. The hypothesis is tested using a Z-score which is the difference between the two candidates' mean values divided by the square root of the sum of their respective variances. I use the calculated Z- score to determine the p-value, which is the probability of finding a test result at least as extreme as the actual results observed. First, I determine the Z-score comparing the number of votes Clinton received in 2016 to the number of votes Biden received in 2020. The Z-score is 396.3. This value corresponds to a confidence that I can reject the hypothesis many times more than one in a quadrillion times' that the two outcomes were similar.
Transcribed Image Text:Question A) Using the following statement, explain what statistical test the scholar was trying to complete (a test for 2 population mean, a test for 2 population proportions etc.). Do not write your answer as a null and alternative hypothesis, but as a statement: In 2016, Trump won Georgia with 51.0% of the vote compared to Clinton's 45.9% with 10. more than 211,000 votes separating them. In 2016, Clinton received 1,877,963 votes and Trump received 2,089,104. In 2020, Biden's tabulated votes (2,474,507) were much greater than Clinton's in 2016. Trump's votes also increased to 2,461,837. The Biden and Trump percentages of the tabulations were 49.5% and 49.3%, respectively. 11. I tested the hypothesis that the performance of the two Democrat candidates were statistically similar by comparing Clinton to Biden. I use a Z-statistic or score, which measures the number of standard deviations the observation is above the mean value of the comparison being made. I compare the total votes of each candidate, in two elections and test the hypothesis that other things being the same they would have an equal number of votes.? I estimate the variance by multiplying the mean times the probability of the candidate not getting a vote. The hypothesis is tested using a Z-score which is the difference between the two candidates' mean values divided by the square root of the sum of their respective variances. I use the calculated Z- score to determine the p-value, which is the probability of finding a test result at least as extreme as the actual results observed. First, I determine the Z-score comparing the number of votes Clinton received in 2016 to the number of votes Biden received in 2020. The Z-score is 396.3. This value corresponds to a confidence that I can reject the hypothesis many times more than one in a quadrillion times' that the two outcomes were similar.
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