Which of the following conditions might cause problems with the parameter estimates of a general linear model? A Categorical independent variables B dummy variables non-linear terms in the model D non-normal error terms E multicollinearity F interaction between explanatory variables G variance inflation factorUse the following table to answer the questions. . . . Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr > F Model 263.65496 237.69 <.0001 error corrected total root mse r-square dependent mean adj r-sq coeff var how many observations were in this dataset a b c d e f medical study was conducted to the relationship between infants systolic blood pressure and two explanatory variable weight kgm age days use output below answer question. sas system . reg procedure model: model1 variable: bp number of read used analysis variance sum source df squares square value pr> F Model 2 1521.53295 760.76647 126.35 <.0001 error corrected total root mse r-square dependent mean adj r-sq coeff var parameter estimates standard variable df estimate t value pr> |t| |95% Confidence Limits Intercept 57.26444 3.79855 15.08 <.0001 age weight consider a hypothesis test about the slope of explanatory variable p1. what would be result ho: b1="7" vs. ha: use do not reject null is different than b c significantly d medical study was conducted to relationship between infants systolic blood pressure and two kgm days output below answer question. . sas system reg procedure model: model1 dependent variable: bp number observations read used analysis variance sum mean source df squares square f value pr> F Model 4 1551.65799 387.91450 75.81 <.0001 error corrected total root mse r-square dependent mean adj r-sq coeff var parameter estimates standard variable df estimate t value pr> (t| |95% Confidence Limits Intercept 9.59475 20.77533 0.46 0.6492 -33.74183 52.93134 age 11.57625 4.41855 2.62 0.0164 2.35933 20.79317 weight 23.62043 11.83013 2.00 0.0597 -1.05679 48.29765 age_squared -0.64922 0.50638 -1.28 0.2145 -1.70551 0.40708 weight_squared -2.83546 1.72449 -1.64 0.1158 -6.43270 0.76177 Which explanatory variables have a statistically significant impact on infants' blood pressure? Choose all the correct answers. Use a = 0.05.\f