State AL AK AZ AR CA CO CT DE FL GA HI ID IL IN IA KS KY LA ME MD MA MI MN MS MO MT NE NV NH NJ NM NY NC ND OH OK OR PA RI SC SD TN TX UT VT VA WA OBR (in per pop (in Mill ED (bachelorIN (median househAGE (under age 18 in perSP (spending on Obesity in thousa 33 4.8 22.6 43.2 23 2505 25.7 0.7 27.5 70.7 25.6 459 26 6.7 26.9 49.7 24.4 2113 34.5 2.9 20.1 40.7 24 1254 25 38.8 30.7 61 23.9 15223 20.5 5.3 37 58.4 23.5 1637 25.6 3.5 36.5 69.4 21.8 1719 26.9 0.9 28.9 59.8 22 592 25.2 19.8 26.4 46.9 20.6 8079 29.1 10 28 49.1 24.9 4226 23.6 1.4 30.1 67.4 21.9 470 26.8 1.6 25.1 46.7 26.5 550 28.1 12.8 31.4 56.7 23.5 6368 31.4 6.5 23.2 48.2 24.1 3520 30.4 3.1 25.7 51.8 23.4 1435 29.9 2.9 30.3 51.3 25 1327 31.3 4.3 21.5 43 23.1 2372 34.7 4.6 21.8 44.8 24.1 2383 28.4 1.3 27.9 48.4 19.7 767 27.6 5.9 36.8 73.5 22.7 3032 22.9 6.7 39.4 66.8 20.8 3511 31.1 9.9 25.9 48.4 22.7 5349 25.7 5.4 32.6 59.8 23.6 2800 34.6 2.9 20.1 39 24.7 1586 29.6 6 26.2 47.3 23.1 3196 24.3 1 28.7 46.2 22.1 379 28.6 1.8 28.5 51.6 24.9 1002 26.2 2.8 22.4 52.8 23.7 1048 27.3 1.3 33.7 64.9 20.5 594 24.6 8.9 35.8 71.6 22.7 4447 27.1 2 25.8 44.9 24.3 663 23.6 19.7 33.2 58 21.6 11114 29.6 9.9 27.3 46.3 23.2 4599 29.7 0.7 27.2 53.7 22.5 371 30.1 11.5 25.2 48.3 22.9 6896 32.2 3.8 23.5 45.3 24.6 1721 27.3 3.9 29.7 50.2 21.8 1678 29.1 12.7 27.5 52.2 21.3 6997 25.7 1 31.3 56.3 20.4 566 31.6 4.8 25.1 44.7 22.6 2291 28.1 0.8 26.2 49.4 24.6 409 31.1 6.5 23.8 44.2 23 3656 29.2 26.9 26.7 51.9 26.6 10262 24.3 2.9 30.3 58.8 30.9 953 23.7 0.6 34.8 54.2 19.6 291 27.4 8.3 35.2 63.9 22.6 3387 26.8 7 25.7 59.4 22.9 2977 WV WI WY 33.8 29.7 24.6 1.8 5.7 0.5 18.3 26.8 24.7 41 52.4 57.4 20.6 22.8 23.6 sources http://www.ncsl.org/research/health/obesity-statistics-in-the-united-states.aspx http://quickfacts.census.gov/qfd/index.html 1171 3078 203 (spending on Obesity in thousands $) OBR POP ED IN AGE SP % of population that is obese Population in Millions Education as percentage of population who have obtained a bachelor's degree or higher Median Household Income ($) in thousands Percentage of population under the age of 18 Spending in thousands of dollars on obesity elor's degree or higher A select group of parents have decided that obesity is caused by the consumption of Ice Cream and nothing else. They have now started campaigning the U.S. Government to stop the production and sale of Ice Cream on the national level. This is all on the hopes to prevent childhood obesity and what they believe to be the main contributor to adulthood obesity, some states have resisted. You think this is a little extreme and have decided to do some digging into what the facts are and what might be the ramifications, before you jump to any conclusions. As you know correlation doesn't imply causation, and data speaks volumes. On a side tangent, you find the following financial statistics that in order for the U.S. Government to combat Ice Cream sales, it needs to spend $51 Billion to pay for the war on Ice Cream, but by enacting this law against Ice Cream the federal government also losses out on $46.7 Billion in Ice Cream annual tax revenue. The economist inside of you says this is more like a $97.7 Billion financial burden on the U.S. tax payers, which you guess is less than the TARP (Troubled Asset Relief Program) bailout which authorized $700 billion in purchasing illiquid mortgage backed securities (although this was true only $426 Billion was used, but still quite a tidy sum of money). You still think that this seems like a huge financial fumble for the U.S. Government to enact such a law, with no real justification; you are inspired to gather some data about the real results of Obesity and Children, and other factors. So you regress back to the problem you want to address and to provide a detailed report to the advisers/policy makers who will decide the fate of Ice Cream. 3) In the process of gathering data you find the following variables: OBR (Obesity rate as a percentage) hint: you may need to make this a proportion (decimal) if using a proportion test, along with any other percentages!, POP (Population in millions), EDU (Education, percentage of population with a bachelor's degree or higher), MHI (Median Household Income in thousands of dollars), AGE (percentage of population under the age of 18), SP (spending on obesity in thousands). In the attempt to find answers to obesity you stumble across articles that say educated and wealthy tend to be fit and healthy. This prompts you to be interested in the relationships between: i) Obesity and Education (OBR & EDU), ii) Obesity and Income (OBR & MHI). Find the following for the each of the above: a) Linear correlation coefficient, and elaborate on it. What does the correlation coefficient mean, not the definition but what does it mean in these cases? b) Plot a scatterplot for each of the two relationships. Does it agree with your above linear correlation coefficient. (hint: if it doesn't maybe you need to redo it). NB : Please show correlation with scatter plot and interpretation of problem. Thank you! c) Do you think MHI and EDU are positively or negative correlated? How can you find out? What is the Correlation r value between them? (Show it, do it! Remember you're presenting this, not just blowing by the answer and saying \"no\" or \"yes\"). 4) Suppose the same group who wants to outlaw Ice Cream read an article that says it can lower the obesity rate to below 17%, if their law was enacted. Identify the null and alternative hypothesis and test the claim using =0.01 (hint: you need a critical value, a test statistic, and some of the summary statistics you got from above, additional hint: probably find the average for the U.S., this is your sample proportion). By testing this claim you are seeing if this is statistically different than what the current obesity rate is, and whether the law should be enacted. In other words if it's not significant do you think we should enact it? Use nontechnical terms as well to interpret