1. The effects of scaling data on OLS statistics Suppose a data set of 1388 observations (N = 1388) was analyzed using OLS to examine the factors that influence the birth weight of children. The regression results are as follows, with standard errors in parentheses: bwght 116.97-0.46cigs + 0.93 faminc (1.05) (0.09) (0.03) where bwght birth weight, in ounces cigs = number of cigarettes smoked by the mother while pregnant, per day famine annual family income, in thousands of dollars R² = 0.021 SSR = (10,000) SER = (1,000) Suppose you would like to measure the dependent variable bwght in grams, rather than ounces. Let bwghtg be the birth weight measured in grams. Hint: There are approximately 28.35 grams per ounce. Without rerunning the regression, you know that after transforming your regression to change the units of the dependent variable, your new slope parameter 2 would be 0.93 multiplied by 28.35. When bwghtg is used as the dependent variable, the residuals should simply be the residuals from the regression with bwght as the dependent variable, 28.35. Thus the squared residuals become u 803.7225. This is reflected in the relationship between SSR and SER. Save & Continue Suppose you would like to measure the dependent variable bwght in grams, rather than ounces. Let bwghtg be the birth weight measured in grams ely 28.35 grams per ounce. Hint: There plus Vithout reru multiplied by ssion, you know that after transforming your regression to change the units of the dependent variable, your new slope arameter divided by 93 multiplied by 28.35. minus When bwght ariable, etween SSR and SER. dependent variable, the residuals should simply be the residuals from the regression with bwght as the dependent 28.35. Thus the squared residuals become u 803.7225. This is reflected in the relationship Suppose you would like to measure the dependent variable bwght in grams, rather than ounces. Let bwghtg be the birth weight measured in grams. Hint: There are approximately 28.35 grams per ounce. multiplied by minus Without rerunning the regression, you know that after transforming your regre parameter 2 would be 0.93 multiplied by▼ 28.35. the units of the dependent variable, your new slope divided by plus When bwghtg is used as the dependent variable, the residuals should simply be variable, 28.35. Thus the squared residuals become u rom the regression with bwght as the dependent 803.7225. This is reflected in the relationship between SSR and SER.