Please help answer these 3, they all go together!

The next 4 questions are based on the plotted data and SPSS output tables for a study of the bivariate relationships among family income, family size, and proportion of family members covered by health insurance. 1.0 1.0 Family size N W Prop. Insured 0.5 Prop. Insured 0.5 0.0 0.0 0 100 200 0 Income (x $1000) 200 Income (x $1000) 0 1 2 3 4 Family size Correlations Family Proportion Family Descriptives Income of Family Size Mean Std. Dev N (x $1000) Covered Family Family Pearson r 1 -.100 907 Income 105.0 59.160 20 Income Sig. (2-tailed) 675 000 (x $1000) (x $1000 ) N 20 20 20 Family Pearson r -.100 - -.161 2.56 .909 20 Family Size Sig. (2-tailed) 675 498 Size N 20 20 20 Proportion of Proportion Pearson r 907 -.161 1 Family 667 345 20 of Family Sig. (2-tailed) 000 498 Covered N 20 20 20What is the most serious potential limitation for comparing what pair of variables has the strongest association in the previous question? Select one: SPSS did 2-tailed significance tests, but they should be 1tailed No causeeffect relationships can be inferred. The large difference in range for Family Income and Family Size invalidates the calculated correlation coefficient Pearson correlation underestimates the curvilinear association of Family Income and Family Size What conclusion best fits the analysis? Select one: Having a high income results in more complete family insurance coverage Families with higher incomes have more complete insurance coverage Families with lower incomes tend to be larger and have less complete insurance coverage Larger family sizes are associated with less complete health insurance coverage, and the functional significance of this association is large If family income were measured in raw dollars instead of thousands of dollars (see labels in tables and pots above), what impact would it have on the calculated Pearson correlation coefficients? Select one: O a. it would not change the correlations O b. it would change the correlations in unpredictable ways O c. it would increase the correlations O d. it would decrease the correlations