Bryant/Smith Case Study 49

We want to determine the primary factors that cloak position wickedness rates in the United States. The statistical analysis of the breeding involves multiple arrested development analysis. Questions we will answer ar as follows: 1.What ar the primary determinants of property crimes in the United States? 2.What would you care to know about property crime rates that give chase endnot be answered by this data set? 3.How does population assiduousness affect property crime rates? Is this expected? quaternary regress analysis can be used to baffle property crime in United States. The regression modeling suggested is of the form. Crimes = b0+b1Pincome + b2Dropout +b3Pubaid+b4density+ b5Kids+ b6Prescip+ b7unemploy+ b8 Urban present bi (i =0,1,..8) are known as the regression coefficients . They are estimated by the method of least squares. bi measure the bear on of unit change in the independent variable on crime. The estimated values of re gression coefficients are given as follows: Variablescoefficientsstd. wrongdoingt (df=41)p-value Intercept-642.50301024.03-0.627.5339 PINCOME-0.01830.0773-0.237.8136 DROPOUT81.292621.98583.698.0006 PUBAID-113.714478.7000-1.445.1561 DENSITY-1.98410.7299-2.718.0096 KIDS1.10381.44850.762.4504 PRECIP1.582111.16360.142.8880 UNEMPLOY-46.383079.6479-0.582.5635 URBAN64.

391510.93035.8916.18E-07 Therefor the model can be written as follows: Crimes = -642.5030 -0.0183 Pincome + 81.2926 Dropout -113.7144 Pubaid -1.9841 Density +1.1038 Kids+ 1.5821 Prescip - 46.3830 Unemploy+ 64.3915Urban The adequacy of the model is persistent by the R2 value (Coefficient of determination) . R2 measure the section of variability in crim! e that can be explained by the regression model. vex Summary R²0.690 Adjusted R²0.630 R0.831 Std. Error749.394 Thus 69.0 % variability in the model can be explained using the regression model. The significance of regression coefficients are tested using a Student t...If you want to detect a full essay, graze it on our website: OrderEssay.net

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