Analysis of Birth Weight and Its Association with Various Predictors: ANOVA and Multiple Regression

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Analysis of Birth Weight and Its Association with Various Predictors: A Statistical Approach Using ANOVA and Multiple Regression Excel → Data Analysis → One way ANOVA 1. Perform an ANOVA, and report on whether you found evidence that birth weight could be associated with the remaining variables (possibly omitting ID number). Ho:μ 1 =μ 2 =μ 3 =μ 4 H a : Not all are same Using Excel, we get Anova: Single Factor SUMMARY Groups Count Sum Average Variance black 1115 453 0.41 0.24 educ 1115 13677 12.27 4.40 smoke 1115 846 0.76 0.18 gestation 1115 43308 38.84 7.14 bwt 1115 3589783 3219.54 401344.84 ANOVA Source of Variation SS df MS F P - value F crit Between Groups 9172131824 4 2293032956.03 28566.02 0.00 2.37 Within Groups 447111477.1 5570 80271.36 Total 9619243301 5574 Since p - value=0.00 < 0.05, it falls rejection region, the null hypothesis would be rejected and conclude that there is an evidence birth weight could be associated with the remaining variables . 2. Perform a multiple regression, and report on whether you found evidence that birth weight could be associated with the remaining variables. Excel → Data Analysis → Regression Using Excel, we get SUMMARY OUTPUT Regression Statistics Multiple R 0.726513 R Square 0.527821 Adjusted R Square 0.526119 Standard Error 436.1074 Observations 1115 ANOVA
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