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A Researcher Is Analyzing Whether the Rate of Unlawful Breaking

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A researcher is analyzing whether the rate of unlawful breaking is effected by the percent of poverty, percent male, percent nonwhite, and the percent of individuals between the ages of 15 and 24 residing in the census block. Given the following SPSS output, what would the researcher conclude? State the null and alternative hypotheses, discuss the F-statistic, the R2, R2 change, the beta coefficients, and which variable significantly affect the dependent variable.
 Mod  R  Adjust  Std. Error  R  Sig. F  Squar  ed R  of the  Square  el  S  e  Square Estimate  Change  F Change  df1  df2  Change 1.195a.038.0311843.30459.0385.2942267.0062407 b1661531723.1231012820.2722265.000\begin{array}{|l|l|l|l|l|l|l|l|l|l|}\hline\\\hline \text { Mod } & & \text { R } & \text { Adjust } & \text { Std. Error } & \text { R } & & & &\text { Sig. F }\\&&\text { Squar } & \text { ed R } & \text { of the } & \text { Square } & & & \\\text { el } & \text { S } & \text { e } & \text { Square} & \text { Estimate } & \text { Change } & \text { F Change } & \text { df1 } & \text { df2 } & \text { Change } \\\\\hline 1 & .195^{\mathrm{a}} & .038 & .031 & 1843.30459 & .038 & 5.294 & 2 & 267 & .006 \\2 & 407 \mathrm{~b} & 166 & 153 & 1723.12310 & 128 & 20.272 & 2 & 265 & .000\\\hline\end{array} a. Predictors: (Constant), percent nonwhite, percent poverty
b. Predictors: (Constant), percent nonwhite, percent poverty, percent 15 to 24, percent male  Model Sum of Squares  df  Mean Square  F  Sig. 1 Regression 35977621.414217988810.7075.294.006b Residual 907205072.7662673397771.808 Total 943182694.1792692 Regression 156357090.146439089272.53713.165.000c Residual 786825604.0332652969153.223 Total 943182694.179269\begin{array}{|l|l|l|l|l|l|} \hline \text { Model }& \text {Sum of Squares } & \text { df } & \text { Mean Square }& \text { F }& \text { Sig. } \\\hline 1 \text { Regression } & 35977621.414 & 2 & 17988810.707 &5.294 & .006^{\mathrm{b}} \\\text { Residual } & 907205072.766 & 267 & 3397771.808 \\\text { Total } & 943182694.179 & 269 & \\\hline 2 \text { Regression } & 156357090.146 & 4 & 39089272.537&13.165 & .000^{\mathrm{c}} \\\text { Residual } & 786825604.033 & 265 & 2969153.223 \\\text { Total } & 943182694.179 & 269 & \\\hline\end{array} a. Dependent Variable: rateofubev
b. Predictors: (Constant), percent nonwhite, percent poverty
c. Predictors: (Constant), percent nonwhite, percent poverty, percent 15 to 24, percent male  Unstandardized  Standardized  Coefficients  Coefficients  Model  B  Std. Error  Beta 1 (Constant) 76.230258.075 percentpoverty 27.2768.606.239 percentnonwhite 5.5444.157.1012 (Constant) 5080.5291006.335 percentpoverty 21.2548.173.186 percentnonwhite 2463.991.004 percent15to24 39.46717.899.126 percentmale 116.90619.763.340t Sig. .295.7683.169.0021.334.1835.049.0002.601.010.062.9512.205.0285.916.000\begin{array}{l} \begin{array}{|l|l|l|l|}\hline &\text { Unstandardized }&&\text { Standardized }\\ &\text { Coefficients }& &\text { Coefficients }\\\hline \text { Model }& \text { B } & \text { Std. Error }&\text { Beta } \\\hline1\text { (Constant) } & 76.230 & 258.075 \\\text { percentpoverty } & 27.276 & 8.606&.239 \\\hline \text { percentnonwhite } & -5.544 & 4.157&-.101 \\\hline 2\text { (Constant) } & -5080.529 & 1006.335 \\\text { percentpoverty } & 21.254 & 8.173 &.186\\\hline \text { percentnonwhite } & 246 & 3.991 &.004 \\\text { percent15to24 } & -39.467 & 17.899&-.126 \\\text { percentmale } & 116.906 & 19.763&.340\\\hline\end{array}\begin{array}{l|l|}\hline\\\\ t & \text { Sig. } \\\hline .295 & .768 \\3.169 & .002 \\\hline-1.334 & .183 \\\hline-5.049 & .000 \\2.601 & .010 \\\hline .062 & .951 \\\hline-2.205 & .028 \\5.916 & .000 \\\hline\end{array}\end{array} a. Dependent Variable: rateofubev

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*H0 : β1, β2, β3, β4 = 0 or R2 = 0
H1 : β1, β2, β...

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