global warming. The following output presents the average amounts (in metric tons) of carbon dioxide emissions for the years 1999-2006 per person in the United States and per person in the rest of the world in an effort to determine if non-US per person emissions can help predict US per person emissions. US 20.2 20 198 SUMMARY OUTPUT 19.6 Regression Statistics 19.4 Multiple R 0.51622776 19.2 R Square 0.2664911 19 Adjusted R Square 0.2175905 188 Standard Error 0.29668345 Observations 17 186 3.1 3.2 3.3 3.4 35 3.6 3.7 3.8 39 ANOVA df SS MS F ignificance F Regression 1 0.479683973 0.479684 5.44965 0.0338864 Residual 15 1.320316027 0.088021 Total 16 1.8 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Uper 95.0% 19.7831797 25.8195291 Intercept 22.8013544 1.416019771 16.10243 7.1E-11 19.78318 25.819529 0.410961292 -2.33445 0.033886 -1.8353112 -0.0834247 -1.8353112 -0.08342469 Non-US -0.95936795 What is the formula needed to predict US emissions in metric tons if the non-US emissions is 3.4? Oŷ = 22.8 – 0.96(3.4) Oy = 22.8(3.4) – 0.96 Oy = 22.8 – 0.96(3.4) Oŷ = 22.8(3.4) – 0.96 State the correlation value that best respresents the plot presented. O 0.516 O 0.266 -0.516 -0.266

global warming. The following output presents the average amounts (in metric tons) of carbon dioxide emissions for the years 1999-2006 per person in the United States and per person in the rest of the world in an effort to determine if non-US per person emissions can help predict US per person emissions. US 20.2 20 198 SUMMARY OUTPUT 19.6 Regression Statistics 19.4 Multiple R 0.51622776 19.2 R Square 0.2664911 19 Adjusted R Square 0.2175905 188 Standard Error 0.29668345 Observations 17 186 3.1 3.2 3.3 3.4 35 3.6 3.7 3.8 39 ANOVA df SS MS F ignificance F Regression 1 0.479683973 0.479684 5.44965 0.0338864 Residual 15 1.320316027 0.088021 Total 16 1.8 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Uper 95.0% 19.7831797 25.8195291 Intercept 22.8013544 1.416019771 16.10243 7.1E-11 19.78318 25.819529 0.410961292 -2.33445 0.033886 -1.8353112 -0.0834247 -1.8353112 -0.08342469 Non-US -0.95936795 What is the formula needed to predict US emissions in metric tons if the non-US emissions is 3.4? Oŷ = 22.8 – 0.96(3.4) Oy = 22.8(3.4) – 0.96 Oy = 22.8 – 0.96(3.4) Oŷ = 22.8(3.4) – 0.96 State the correlation value that best respresents the plot presented. O 0.516 O 0.266 -0.516 -0.266

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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= 12.3 The Regression Equation X E Rounding Numbers Calculator X xyzHomework Assessment + → C A A https://www.xyzhomework.com 80% Listed below are paired data consisting of amounts spent on advertising (in millions of dollars) and the profits (in 3- millions of dollars). Determine if there is a significant linear correlation between advertising cost and profit . Use a significance level of 0.01 and round all values to 4 decimal places. Advertising Cost Profit 3 21 4 22 18 6. 23 30 8 33 23 10 31 Ho: p = 0 Ha: p # 0 Find the Linear Correlation Coefficient r= Find the p-value p-value = The p-value is O Less than (or equal to) a O Greater than a The p-value leads to a decișion to Reject Ho O Accept Ho O Do Not Reject Ho signific voters p. The n The conclusion is There is a significant positive linear correlation between advertising expense and profit. O There is a significant negative linear correlation between advertising expense and profit. There ie n cianifiannt linaar corralation…
Subject : Statestic
Carbon dioxide is produced by burning fossil fuels such as oil and natural gas, and has been connected to global warming. The following output presents the average amounts (in metric tons) of carbon dioxide emissions for the years 1999-2006 per person in the United States and per person in the rest of the world in an effort to determine if non-US per person emissions can help predict US per person emissions. US 20.2 20 19.8 SUMMARY OUTPUT 19.6 19.4 Regression Statistics Multiple R 0.51622776 19.2 R Square 0.2664911 19 Adjusted R Square Standard Error 0.2175905 18.8 0.29668345 Observations 17 18.6 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 39 ANOVA df MS F iignificance F Regression 1 0.479683973 0.479684 5.44965 0.0338864 Residual 15 1.320316027 0.088021 Total 16 1.8 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 22.8013544 1.416019771 16.10243 7.16-11 19.78318 25.819529 19.7831797 25.8195291 Non-US -0.95936795 0.410961292 -2.33445 0.033886…
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