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Use the data to (a) find the coefficient of determination r2r^2 and interpret the result, and (b) find the standard error of estimate ses_e, and interpret the result. The U.S. voting age populations (in millions) and the number of ballots cast (in millions) for the highest office in federal elections for nine nonpresidential election years are shown in the table. The equation of the regression line is y^=0.276x+18.881\hat{y}=0.276 x+18.881

Voting age population, x166.0177.9186.2195.3205.3Ballots cast in federal     elections, y67.665.067.975.172.5Voting age population, x215.5225.5236.0245.7 Ballots cast in federal     elections, y78.483.889.181.7 \begin{matrix} \text{Voting age population, x} & \text{166.0} & \text{177.9} & \text{186.2} & \text{195.3} & \text{205.3}\\ \text{Ballots cast in federal} & \text{ } & \text{ } & \text{ } & \text{ } & \text{ }\\ \text{elections, y} & \text{67.6} & \text{65.0} & \text{67.9} & \text{75.1} & \text{72.5}\\ \text{Voting age population, x} & \text{215.5} & \text{225.5} & \text{236.0} & \text{245.7} & \text{ }\\ \text{Ballots cast in federal} & \text{ } & \text{ } & \text{ } & \text{ } & \text{ }\\ \text{elections, y} & \text{78.4} & \text{83.8} & \text{89.1} & \text{81.7} & \text{ }\\ \end{matrix}

Question

identify the claim and state H0H_0 and HaH_a.

Assume the samples are random and independent, the populations are normally distributed, and the population variances are equal. The well-being index is a way to measure how people are faring physically, emotionally, socially, and professionally, as well as to rate the overall quality of their lives and their outlooks, for the future. The table shows the well-being index scores for a sample of states from four regions of the United States. At α=0.10\alpha=0.10, can you reject the claim that the mean score is the same for all regions?

NortheastMidwestSouthWest61.761.661.064.063.661.860.863.062.661.463.163.561.863.262.363.262.161.760.561.863.562.962.062.6 62.961.362.5 63.760.562.8  62.362.5  61.5   63.1 \begin{matrix} \text{Northeast} & \text{Midwest} & \text{South} & \text{West}\\ \text{61.7} & \text{61.6} & \text{61.0} & \text{64.0}\\ \text{63.6} & \text{61.8} & \text{60.8} & \text{63.0}\\ \text{62.6} & \text{61.4} & \text{63.1} & \text{63.5}\\ \text{61.8} & \text{63.2} & \text{62.3} & \text{63.2}\\ \text{62.1} & \text{61.7} & \text{60.5} & \text{61.8}\\ \text{63.5} & \text{62.9} & \text{62.0} & \text{62.6}\\ \text{ } & \text{62.9} & \text{61.3} & \text{62.5}\\ \text{ } & \text{63.7} & \text{60.5} & \text{62.8}\\ \text{ } & \text{ } & \text{62.3} & \text{62.5}\\ \text{ } & \text{ } & \text{61.5} & \text{ }\\ \text{ } & \text{ } & \text{63.1} & \text{ }\\ \end{matrix}

Solution

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The null hypothesis states that all population means are equal:

H0:μ1=μ2=μ3=μ4H_0: \mu_1=\mu_2=\mu_3=\mu_4

The alternative hypothesis states the opposite of the null hypothesis:

H1: not all of μ1,μ2,μ3,μ4 are equalH_1:\text{ not all of } \mu_1, \mu_2, \mu_3, \mu_4\text{ are equal}

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