6 Q1. In class I demonstrated a regression of vehicle fatality rates on the percentage of the population living in a rural area on the state level using data from 2000. See the .dole Stata_class_demovehicledeaths.do to review what I did in class. Use the data in the le statedata_hw3.dta to complete the following exercises. 1. Repeat the regression separately using data from 1990 and from 1980. Compare your results. i.After opening the dataset you will rst have to create the rural percentage and vehicle fatality rate variables just as we did in class. ii.Use the summarize command (abbreviated sum) to look at summary statistics for the newly created variables. This helps to check that your variables were generated properly and remind you of what scale/units these variables are measured in. iii.Now you can drop all the observations not from 1980 (using the drop or keep commands as I showed in class), then after doing the regression, clear the data, reload the original dataset, and then restrict it to 1990 data. Alternatively you can use an if statement as part of the regression command: regress vehicledeathrate ruralpercentage if year==1980, robust 2. Now, using only data from 2000 include the state's per capita income as an addi- tional regressor. To estimate a regression with multiple regressors use the com- mand regress vehicledeathrate ruralpercentage income_percap, robust if the data only includes 2000 observations or regress vehicledeathrate ruralpercentage income_percap if year==2000, robust to make sure you restrict the estimation to the 2000 observations. Compare your results to the regression that does not include the income vari- able (the regression we did in class) and explain any di erences. 3. Now again run a regression with the rural variable and the income variable both as regressors but this time use all 153 observations across the three years. Report your results. Explain why the standard errors are smaller than in the previous regressions. Q2. For this question use the data mcas.dta. This dataset was modi ed slightly from the data provided by Stock and Watson on the textbook's website. See the descrip- tion on the website, http://wps.aw.com/aw_stock_ie_3/178/45691/11696965. cw/index.html . Use the bar on the left of that webpage to go to \\Datasets for Replicating Empirical Results" and then the data is under the heading \\California Test Score Data". 1.Create a scatter plot with district average 4th grade test scores (this variable is named totsc4 in the dataset) as the dependent variable and the percentage of students in the district qualifying for a reduced price lunch (this variable is named lnch pct in the dataset) as the independent variable. This is done simply by typing the command scatter totsc4 lnch_pct . Does it appear from the scatter plot that there is a relationship between these two variables? 2.Do you think the relationship shown in the scatter plot you just created might be confounded in some way because school districts with a high percentage of students qualifying for a reduced price lunch tend to also have a low per capita income? That is, is there an omitted variable bias? 3.Use a regression analysis to separate the e ect of per capita income from the e ect of the percentage of students qualifying for a reduced price lunch on test scores. That is, estimate the regression model with two regressors using the command regress totsc4 lnch_pct percap, robust and compare this to the simple regression that excludes percap. Explain the results carefully. 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See the hlp manual for the many available options for customizing titles, axes, colors, etc. */ graph export figure2.png, replace extension the graph can only be viewed in Stata. */ /* saves the graph. without a specified will be saved in a format that graph twoway (scatter vehicledeathrate ruralpercentage, mlabel(statename)) /// (line vdrHat ruralpercentage) /* mlabel creates a label for each observation on the scatter plot */ graph export figure2.png, replace log close if you fail to */ /* this stops the recording of your Stata session. type this your log file will not be created. s####################################statedata_hw3############################ ############21 Oct 2014 10:22##stateid##########################year########################## ###vehicledeaths####################statename########################totalpop### ######################poprural#########################income_percap############ ########################%8.0g############################################ %8.0g############################################ %8.0g############################################ %20s#f########################################### %12.0g########################################### %12.0g########################################### %8.0g#############################################X########################### ################################################################################ ################################################################################ ##########################################N` ####################T@############hN` ######################################N` ####################T@############hN` ######################################N` ####################T@############hN` ######################################N` ####################T@############hN` ######################################N` ####################T@############hN` ######################################N` ####################T@############hN` ######################################N` ####################T@############hN` ######################################J###_dta##### ### ##0#U#####iis#####T@####B############Hstateid##G###_dta# #### ### ##0#U#####tis#####T@####B############Hyear##D###_dta# #### ### ##0#U#####_TSitrvl#T@####B############H1##X###_dta##### ### ##0#U#####_TSdelta#T@####B############H+1.0000000000000X+0 00##J###_dta##### ### ##0#U#####_TSpanel#T@####B############Hstateid##G###_dta# #### ### ##0#U#####_TStvar#T@####B############Hyear########O#Di strict Of Columbia} ##########Illinois#Of Columbia#####"#j#New Jersey#re#lumbiaRl##R #####Massachusetts#lumbiaQ#+##"###a#Kentucky#Of Columbia@.#q##Z# %#4#North Carolina#umbiaK9M#.##-##South Carolina#umbia"#(####2##Vermont###ta#a#umbia#V##! +##"#####Michigan#etts#lumbia#[)###5###Washington#a#a#umbia8##### ##Louisiana#f Columbia5######(##Oklahoma#ota#a#umbiap"######7##Wisconsin#nia##umb ia]=#+##K# #H#Connecticut##rented#[-# #?! ###Arizona#plus#rented######/###Tennessee#ta#a#umbia);###E#### #Colorado#a#s#rented##S'###>###Y#Idaho### Of Columbia #9##h###6#Maryland##f Columbia<# #e *#r#Pennsylvania#a#umbia#7#####Iowa#####Of ColumbiaX #He#####i#New Mexico#re#lumbia ######S#Montana##pi#s#lumbia[##p#####[#Alaska##plus#rented###/ ##'####Maine###a#f ColumbiaM ## ######Mississippi#s#lumbia>##B##? 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###Z7#i#Wisconsin#nia##umbia>PE####S,#Z#Rhode Island#a#umbia<##s##T###Kansas###Of Columbia# %######P/###Tennessee#ta#a#umbia#L####K$#_#New York#o#re#lumbia##8$#][-#-#South Carolina#umbia4#.##kI(##Oklahoma#ota#a#umbia(#;@##D##! #Idaho####Of Columbia#####E*##Pennsylvania#a#umbiaE#*#Q###Hawaii###Of ColumbiaT#####T###Iowa#####Of Columbiarc"#`##L###Missouri#pi#s#lumbiamGI####M##| #California#ia##umbia###X###Georgia##Of Columbia7o#ro##R'##Ohio#1#akota#a#umbia#;:'##R0#G#Texas###e#ta#a#umbi al##37#L6##West Virginia##umbiaw####]@ #g#Nevada###pi#s#lumbia*####U###Maine###a#f Columbia#k####ML###Mississippi#s#lumbia #<##=###Montana##pi#s#lumbia@ #mR##B #$ Florida# Of Columbia~# ##5T##6#Alabama#n#nia##umbia;#;##G##| #Indiana##Of ColumbiaN'O#Z###O###Colorado#a#ia##umbia7;#1 #])##Oregon###ota#a#umbia#@)####Q #t#Delaware#ut#a##umbia#{ #`## [ Question 1 // clear all and load data clear all use statedata_hw6 // generate new variables for rural percentage and fatality rate gen ruralpercentage=100*poprural/totalpop gen vehicledeathrate=100000*vehicledeaths/totalpop // summary stats for two new variables sum ruralpercentage vehicledeathrate // OLS regression for 1980 reg vehicledeathrate ruralpercentage if year==1980, robust // OLS regression for 1990 reg vehicledeathrate ruralpercentage if year==1990, robust // OLS regression for 2000 with income per capita as extra regressor regress vehicledeathrate ruralpercentage income_percap if year==2000, robust // and without, form class regress vehicledeathrate ruralpercentage if year==2000, robust // OLS for all years with income per capita regress vehicledeathrate ruralpercentage income_percap, robust Question 2 // clear all and load data clear all use mcas // scatter plot scatter totsc4 lnch_pct // simple regresison regress totsc4 lnch_pct, robust // regresison with control for per capita income regress totsc4 lnch_pct percap, robust Question 1 // clear all and load data clear all use statedata_hw6 // generate new variables for rural percentage and fatality rate gen ruralpercentage=100*poprural/totalpop gen vehicledeathrate=100000*vehicledeaths/totalpop // summary stats for two new variables sum ruralpercentage vehicledeathrate // OLS regression for 1980 reg vehicledeathrate ruralpercentage if year==1980, robust // OLS regression for 1990 reg vehicledeathrate ruralpercentage if year==1990, robust // OLS regression for 2000 with income per capita as extra regressor regress vehicledeathrate ruralpercentage income_percap if year==2000, robust // and without, form class regress vehicledeathrate ruralpercentage if year==2000, robust // OLS for all years with income per capita regress vehicledeathrate ruralpercentage income_percap, robust Question 2 // clear all and load data clear all use mcas // scatter plot scatter totsc4 lnch_pct // simple regresison regress totsc4 lnch_pct, robust // regresison with control for per capita income regress totsc4 lnch_pct percap, robust