(C) (d) (e) (f) The dataset \"ps3_randhie_a.dta\" contains data associated with Panel A of Tables 1.3 and 1.4. Generate a variable for total hospital spending, equal to the sum of ination-adjusted dollars spent on inpatient care (inpdol_inf) and outpatient care (outsum_inf). Calculate the difference in average hospital spending between people who report being in excellent health (exc_health) versus those who report being in bad health (bad_health). Conrm your answer using the regress command. Is this difference statistically signicant at the 5% level? As described in MM Chapter 1, the RAND HIE had many small treatment groups - in fact, the variable plan in your dataset shows that there were 24 different groups. We're just going to use 4 broad categorizations though. Dene a new variable \"plantype\" that divides these into 4 larger categories as follows. Plan Type 1 (\"Free\") is plan 24; Plan Type 2 (\"Individual Deductible\") is plans 1 and 5; Plan Type 3 (\"Cost-Sharing\") is plans 9-23, inclusive; and Plan Type 4 (\"Catastrophic\") is plans 2-4 and plans 6-8, both inclusive. What is the average hospital spending in each group? [The averages should replicate what is in Table 14, Panel A.] Is the difference in hospital spending between Plan Types 1 and 4 signicant at the 5% level? [Standard errors are calculated differently in MM, so those will be different from Table 1.4.] The dataset \"ps3_randhie_b.dta\" contains data associated with Panel B of Tables 1.3 and 1.4. The four plan types have already been dened in this dataset (variable plantype), which also contains the variable ghindxx, a general health index. Is the difference in the average health between Plan Type 1 and 4 signicant at the 5% level? [Again, you should be able to replicate MM's means but not standard errors] How do your results from parts (e) and (f) relate to the HIE ndings discussed in MM Chapter 1