We have the age distribution in percentages for the whole United States according to the 2010...
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We have the age distribution in percentages for the whole United States according to the 2010 US Census in the following table. Besides, we have the age distribution in individuals for a simple random sample of 4500 New Yorkers in 2010. At a = 0.03, you will need to test if the New York sample's age distribution is the same as the U.S. overall age distribution in 2010. (a)[1] Form the null and alternative hypotheses Ho and H₁. Make a sketch of the test. (b)[1] Specify appropriate equations for the x² statistic and degrees of freedom df. (c)[1] Based on the U.S. overall age distribution, calculate expected frequencies. (d) [2] Given the expected and observed frequencies, calculate the x² statistic and its df. (e) [1] Given a = 0.03 and df, use CHISQ.INV(**) to look up for the critical value x²*. (f)[2] Compare x² statistic with x²* critical to reject or accept Ho. Interpret your result. (g)[2] Sketch and find the p-value of the test. Comment on the p-value and its implication. Whole US (%) NY Sample (People) 6.54% 6.59% 6.70% 7.14% 6.99% 6.83% 6.47% 6.54% Age Range Under 5 years 5 to 9 years 10 to 14 years 15 to 19 years 20 to 24 years 25 to 29 years 30 to 34 years 35 to 39 years 40 to 44 years 45 to 49 years 50 to 54 years 55 to 59 years 60 to 64 years 65 to 69 years 70 to 74 years 75 to 79 years 80 to 84 years 85 years and over TOTAL 0.99 2.66838 df 17 CHISQ.INV(a*,df) 33.40866 CHISQ.DIST(x²*,df,1) 0.00003 6.77% 7.36% 7.22% 6.37% 5.45% 4.03% 3.01% 2.37% 1.86% 1.76% 100.00% 0.97 5.33675 18 30.84473 0.00177 0.97 7.28600 17 29.52268 0.02047 268 270 281 317 328 321 297 291 315 339 330 287 248 180 136 110 91 91 4500 0.95 10.67350 18 28.86930 0.09231 0.90 10.67350 17 24.76904 0.12698 We have the age distribution in percentages for the whole United States according to the 2010 US Census in the following table. Besides, we have the age distribution in individuals for a simple random sample of 4500 New Yorkers in 2010. At a = 0.03, you will need to test if the New York sample's age distribution is the same as the U.S. overall age distribution in 2010. (a)[1] Form the null and alternative hypotheses Ho and H₁. Make a sketch of the test. (b)[1] Specify appropriate equations for the x² statistic and degrees of freedom df. (c)[1] Based on the U.S. overall age distribution, calculate expected frequencies. (d) [2] Given the expected and observed frequencies, calculate the x² statistic and its df. (e) [1] Given a = 0.03 and df, use CHISQ.INV(**) to look up for the critical value x²*. (f)[2] Compare x² statistic with x²* critical to reject or accept Ho. Interpret your result. (g)[2] Sketch and find the p-value of the test. Comment on the p-value and its implication. Whole US (%) NY Sample (People) 6.54% 6.59% 6.70% 7.14% 6.99% 6.83% 6.47% 6.54% Age Range Under 5 years 5 to 9 years 10 to 14 years 15 to 19 years 20 to 24 years 25 to 29 years 30 to 34 years 35 to 39 years 40 to 44 years 45 to 49 years 50 to 54 years 55 to 59 years 60 to 64 years 65 to 69 years 70 to 74 years 75 to 79 years 80 to 84 years 85 years and over TOTAL 0.99 2.66838 df 17 CHISQ.INV(a*,df) 33.40866 CHISQ.DIST(x²*,df,1) 0.00003 6.77% 7.36% 7.22% 6.37% 5.45% 4.03% 3.01% 2.37% 1.86% 1.76% 100.00% 0.97 5.33675 18 30.84473 0.00177 0.97 7.28600 17 29.52268 0.02047 268 270 281 317 328 321 297 291 315 339 330 287 248 180 136 110 91 91 4500 0.95 10.67350 18 28.86930 0.09231 0.90 10.67350 17 24.76904 0.12698
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a The null hypothesis Ho is that the New York samples age distribution is the same as the US overall age distribution in 2010 The alternative hypothes... View the full answer
Related Book For
Statistics Unlocking the Power of Data
ISBN: 978-1118583104
1st edition
Authors: Robin H. Lock, Patti Frazer Lock, Kari Lock Morgan, Eric F. Lock, Dennis F. Lock
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