{smcl} {com}{sf}{ul off}{txt}{.-} name: {res} {txt}log: {res}/Users/stephenchang/Desktop/TICR Program/Spring 2017/EPI 265 Epidemiologic Methods III Research Methods in Chronic Disease Epidemiology/Lecture 7/HW 7/assign7_stephenchang.smcl {txt}log type: {res}smcl {txt}opened on: {res}20 May 2017, 14:38:58 {txt} {com}. . /* Assignment Week 7 > Specify a hypothesis regarding a particular exposure and outcome and a > binary effect modifier including specific measures of association > (specify the magnitudes of that association you anticipate: I suggest making > everything cross-sectional). > > Using the software of your choice, generate a population with 1000 people under > a causal structure consistent with this hypothesis. Draw a simple random sample > 100 individuals from this population and estimate the population average > exposure-outcome association and the association stratified by your modifier of > interest within this subset. Repeat this 10 times and write the parameter > estimates and CI each time. > > Repeat the data set construction, setting the causal effect to the null. Again > repeat this 10 times and write the parameter estimate and CI each time (if you > figure out how to automate it, run it 1000 times and post the histogram of the > parameter estimates and p-values). > > Use your code above and also a canned software command to estimate statistical > power to detect the difference in means under the settings below: > *n=100, μ0=.02, μ1=.12, SD=1, α=.05 > *n=100,μ0=.02, μ1=.12, SD=2, α=.05 > *n=500, μ0=.3, μ1=.3, SD=1, α=.05 > For each of the 3 settings above, what is the power to detect whether the ratio > of the means=1? > */ . . clear {txt} {com}. . set obs 1000 {txt}{p} number of observations (_N) was 0, now 1,000 {p_end} {com}. . set seed 98770 {txt} {com}. . /* Model: BMD = beta0 + beta1A*CalciumIntake[Smoking] + beta1B*CalciumIntake[NoSmoking] + beta2*Smoking + ErrorTerm > BMD and CalcumIntake are continuous, > Smoking is a dichotomous effect modifier > > if smoking calcium intake less effecive in increasing BMD > => beta1A < beta1B > > pick: > beta0 = 1.0 g/cm^2 > beta1A = 0.01 g/cm^2 per 1000 mg Calcium / day > beta1B = 0.03 g/cm^2 per 1000 mg Calcium / day > beta2 = -0.1 g/cm^2 (smokers w/o Calcium intake should have somewhat smaller > BMD values than non-smokers w/o Calcium intake) > > */ . . * ---- 1. Data Generation . gen Smoking = runiform()>=0.5 /* effect modifier; total sample has ~half people that are smoking */ {txt} {com}. . gen CalciumIntake=rnormal(1000,300) /* Calcium Intake, mean 1000 mg/day, std-dev 300 mg/day */ {txt} {com}. replace CalciumIntake = 0 if CalciumIntake < 0 /* don't allow negative Intake */ {txt}(0 real changes made) {com}. . gen CalciumIntakeSmoking = CalciumIntake if Smoking == 1 {txt}(507 missing values generated) {com}. replace CalciumIntakeSmoking = 0 if Smoking == 0 {txt}(507 real changes made) {com}. . gen CalciumIntakeNotSmoking = CalciumIntake if Smoking == 0 {txt}(493 missing values generated) {com}. replace CalciumIntakeNotSmoking = 0 if Smoking == 1 {txt}(493 real changes made) {com}. . gen ErrorTerm = rnormal(0, 0.1) /* normally distributed error term */ {txt} {com}. . gen BMD = 1.0 + 0.01 * CalciumIntakeSmoking + 0.03 * CalciumIntakeNotSmoking -0.1 * Smoking + ErrorTerm {txt} {com}. . * ---- 2. run regression on 10 random samples of 100 patients each . . * the population average exposure-outcome association, i.e., don't account for eff. modifier . bootstrap, reps(10) size(100) noisily: regress BMD CalciumIntake bootstrap: First call to regress with data as is: . regress BMD CalciumIntake {txt} Source {c |} SS df MS Number of obs ={res} 1,000 {txt}{hline 13}{c +}{hline 34} F(1, 998) = {res} 292.01 {txt} Model {c |} {res} 31205.6872 1 31205.6872 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 106650.317 998 106.864045 {txt}R-squared ={res} 0.2264 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.2256 {txt} Total {c |} {res} 137856.005 999 137.993999 {txt}Root MSE = {res} 10.338 {txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} BMD{col 15}{c |} Coef.{col 27} Std. Err.{col 39} t{col 47} P>|t|{col 55} [95% Con{col 68}f. Interval] {hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} CalciumIntake {c |}{col 15}{res}{space 2} .0190545{col 27}{space 2} .0011151{col 38}{space 1} 17.09{col 47}{space 3}0.000{col 55}{space 4} .0168664{col 68}{space 3} .0212426 {txt}{space 8}_cons {c |}{col 15}{res}{space 2} 2.004213{col 27}{space 2} 1.147482{col 38}{space 1} 1.75{col 47}{space 3}0.081{col 55}{space 4}-.2475413{col 68}{space 3} 4.255967 {txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res} {txt}Bootstrap replications ({res}10{txt}) {com}. regress BMD CalciumIntake {txt} Source {c |} SS df MS Number of obs ={res} 100 {txt}{hline 13}{c +}{hline 34} F(1, 98) = {res} 39.49 {txt} Model {c |} {res} 4589.36692 1 4589.36692 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 11388.0052 98 116.204134 {txt}R-squared ={res} 0.2872 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.2800 {txt} Total {c |} {res} 15977.3721 99 161.387597 {txt}Root MSE = {res} 10.78 {txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} BMD{col 15}{c |} Coef.{col 27} Std. Err.{col 39} t{col 47} P>|t|{col 55} [95% Con{col 68}f. Interval] {hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} CalciumIntake {c |}{col 15}{res}{space 2} .019565{col 27}{space 2} .0031133{col 38}{space 1} 6.28{col 47}{space 3}0.000{col 55}{space 4} .0133868{col 68}{space 3} .0257431 {txt}{space 8}_cons {c |}{col 15}{res}{space 2} 3.000773{col 27}{space 2} 3.344565{col 38}{space 1} 0.90{col 47}{space 3}0.372{col 55}{space 4}-3.636407{col 68}{space 3} 9.637954 {txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{com}. regress BMD CalciumIntake {txt} Source {c |} SS df MS Number of obs ={res} 100 {txt}{hline 13}{c +}{hline 34} F(1, 98) = {res} 41.58 {txt} Model {c |} {res} 4557.90374 1 4557.90374 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 10741.4092 98 109.606216 {txt}R-squared ={res} 0.2979 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.2908 {txt} Total {c |} {res} 15299.3129 99 154.538514 {txt}Root MSE = {res} 10.469 {txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} BMD{col 15}{c |} Coef.{col 27} Std. Err.{col 39} t{col 47} P>|t|{col 55} [95% Con{col 68}f. Interval] {hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} CalciumIntake {c |}{col 15}{res}{space 2} .0269773{col 27}{space 2} .0041834{col 38}{space 1} 6.45{col 47}{space 3}0.000{col 55}{space 4} .0186754{col 68}{space 3} .0352793 {txt}{space 8}_cons {c |}{col 15}{res}{space 2}-5.791539{col 27}{space 2} 4.380094{col 38}{space 1} -1.32{col 47}{space 3}0.189{col 55}{space 4}-14.48369{col 68}{space 3} 2.900615 {txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{com}. regress BMD CalciumIntake {txt} Source {c |} SS df MS Number of obs ={res} 100 {txt}{hline 13}{c +}{hline 34} F(1, 98) = {res} 21.65 {txt} Model {c |} {res} 2275.9501 1 2275.9501 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 10302.9605 98 105.13225 {txt}R-squared ={res} 0.1809 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.1726 {txt} Total {c |} {res} 12578.9106 99 127.059703 {txt}Root MSE = {res} 10.253 {txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} BMD{col 15}{c |} Coef.{col 27} Std. Err.{col 39} t{col 47} P>|t|{col 55} [95% Con{col 68}f. Interval] {hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} CalciumIntake {c |}{col 15}{res}{space 2} .0166687{col 27}{space 2} .0035825{col 38}{space 1} 4.65{col 47}{space 3}0.000{col 55}{space 4} .0095593{col 68}{space 3} .0237781 {txt}{space 8}_cons {c |}{col 15}{res}{space 2} 4.586797{col 27}{space 2} 3.638756{col 38}{space 1} 1.26{col 47}{space 3}0.210{col 55}{space 4}-2.634196{col 68}{space 3} 11.80779 {txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{com}. regress BMD CalciumIntake {txt} Source {c |} SS df MS Number of obs ={res} 100 {txt}{hline 13}{c +}{hline 34} F(1, 98) = {res} 28.39 {txt} Model {c |} {res} 2879.37388 1 2879.37388 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 9940.3098 98 101.431733 {txt}R-squared ={res} 0.2246 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.2167 {txt} Total {c |} {res} 12819.6837 99 129.491754 {txt}Root MSE = {res} 10.071 {txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} BMD{col 15}{c |} Coef.{col 27} Std. Err.{col 39} t{col 47} P>|t|{col 55} [95% Con{col 68}f. Interval] {hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} CalciumIntake {c |}{col 15}{res}{space 2} .0194998{col 27}{space 2} .0036599{col 38}{space 1} 5.33{col 47}{space 3}0.000{col 55}{space 4} .0122368{col 68}{space 3} .0267627 {txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.9592{col 27}{space 2} 3.637379{col 38}{space 1} 0.54{col 47}{space 3}0.591{col 55}{space 4}-5.259059{col 68}{space 3} 9.177459 {txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{com}. regress BMD CalciumIntake {txt} Source {c |} SS df MS Number of obs ={res} 100 {txt}{hline 13}{c +}{hline 34} F(1, 98) = {res} 21.35 {txt} Model {c |} {res} 2203.85388 1 2203.85388 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 10115.1135 98 103.215444 {txt}R-squared ={res} 0.1789 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.1705 {txt} Total {c |} {res} 12318.9674 99 124.434014 {txt}Root MSE = {res} 10.16 {txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} BMD{col 15}{c |} Coef.{col 27} Std. Err.{col 39} t{col 47} P>|t|{col 55} [95% Con{col 68}f. Interval] {hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} CalciumIntake {c |}{col 15}{res}{space 2} .0197824{col 27}{space 2} .0042811{col 38}{space 1} 4.62{col 47}{space 3}0.000{col 55}{space 4} .0112866{col 68}{space 3} .0282782 {txt}{space 8}_cons {c |}{col 15}{res}{space 2} .3580954{col 27}{space 2} 4.303101{col 38}{space 1} 0.08{col 47}{space 3}0.934{col 55}{space 4}-8.181268{col 68}{space 3} 8.897459 {txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{com}. regress BMD CalciumIntake {txt} Source {c |} SS df MS Number of obs ={res} 100 {txt}{hline 13}{c +}{hline 34} F(1, 98) = {res} 14.42 {txt} Model {c |} {res} 1517.45188 1 1517.45188 {txt}Prob > F ={res} 0.0003 {txt} Residual {c |} {res} 10311.8183 98 105.222636 {txt}R-squared ={res} 0.1283 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.1194 {txt} Total {c |} {res} 11829.2702 99 119.487577 {txt}Root MSE = {res} 10.258 {txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} BMD{col 15}{c |} Coef.{col 27} Std. Err.{col 39} t{col 47} P>|t|{col 55} [95% Con{col 68}f. Interval] {hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} CalciumIntake {c |}{col 15}{res}{space 2} .0125262{col 27}{space 2} .0032985{col 38}{space 1} 3.80{col 47}{space 3}0.000{col 55}{space 4} .0059804{col 68}{space 3} .0190719 {txt}{space 8}_cons {c |}{col 15}{res}{space 2} 7.457918{col 27}{space 2} 3.441096{col 38}{space 1} 2.17{col 47}{space 3}0.033{col 55}{space 4} .6291737{col 68}{space 3} 14.28666 {txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{com}. regress BMD CalciumIntake {txt} Source {c |} SS df MS Number of obs ={res} 100 {txt}{hline 13}{c +}{hline 34} F(1, 98) = {res} 34.62 {txt} Model {c |} {res} 3683.66249 1 3683.66249 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 10426.7055 98 106.394954 {txt}R-squared ={res} 0.2611 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.2535 {txt} Total {c |} {res} 14110.3679 99 142.528969 {txt}Root MSE = {res} 10.315 {txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} BMD{col 15}{c |} Coef.{col 27} Std. Err.{col 39} t{col 47} P>|t|{col 55} [95% Con{col 68}f. Interval] {hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} CalciumIntake {c |}{col 15}{res}{space 2} .0217282{col 27}{space 2} .0036927{col 38}{space 1} 5.88{col 47}{space 3}0.000{col 55}{space 4} .0144002{col 68}{space 3} .0290563 {txt}{space 8}_cons {c |}{col 15}{res}{space 2} .2143975{col 27}{space 2} 3.778695{col 38}{space 1} 0.06{col 47}{space 3}0.955{col 55}{space 4}-7.284299{col 68}{space 3} 7.713094 {txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{com}. regress BMD CalciumIntake {txt} Source {c |} SS df MS Number of obs ={res} 100 {txt}{hline 13}{c +}{hline 34} F(1, 98) = {res} 27.40 {txt} Model {c |} {res} 2761.18361 1 2761.18361 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 9876.11352 98 100.776669 {txt}R-squared ={res} 0.2185 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.2105 {txt} Total {c |} {res} 12637.2971 99 127.649466 {txt}Root MSE = {res} 10.039 {txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} BMD{col 15}{c |} Coef.{col 27} Std. Err.{col 39} t{col 47} P>|t|{col 55} [95% Con{col 68}f. Interval] {hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} CalciumIntake {c |}{col 15}{res}{space 2} .0175534{col 27}{space 2} .0033535{col 38}{space 1} 5.23{col 47}{space 3}0.000{col 55}{space 4} .0108986{col 68}{space 3} .0242083 {txt}{space 8}_cons {c |}{col 15}{res}{space 2} 3.216072{col 27}{space 2} 3.330537{col 38}{space 1} 0.97{col 47}{space 3}0.337{col 55}{space 4} -3.39327{col 68}{space 3} 9.825415 {txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{com}. regress BMD CalciumIntake {txt} Source {c |} SS df MS Number of obs ={res} 100 {txt}{hline 13}{c +}{hline 34} F(1, 98) = {res} 16.46 {txt} Model {c |} {res} 1702.16732 1 1702.16732 {txt}Prob > F ={res} 0.0001 {txt} Residual {c |} {res} 10132.3137 98 103.390956 {txt}R-squared ={res} 0.1438 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.1351 {txt} Total {c |} {res} 11834.481 99 119.540212 {txt}Root MSE = {res} 10.168 {txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} BMD{col 15}{c |} Coef.{col 27} Std. Err.{col 39} t{col 47} P>|t|{col 55} [95% Con{col 68}f. Interval] {hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} CalciumIntake {c |}{col 15}{res}{space 2} .0143796{col 27}{space 2} .0035439{col 38}{space 1} 4.06{col 47}{space 3}0.000{col 55}{space 4} .0073468{col 68}{space 3} .0214125 {txt}{space 8}_cons {c |}{col 15}{res}{space 2} 6.755841{col 27}{space 2} 3.597928{col 38}{space 1} 1.88{col 47}{space 3}0.063{col 55}{space 4}-.3841304{col 68}{space 3} 13.89581 {txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{com}. regress BMD CalciumIntake {txt} Source {c |} SS df MS Number of obs ={res} 100 {txt}{hline 13}{c +}{hline 34} F(1, 98) = {res} 36.86 {txt} Model {c |} {res} 4093.626 1 4093.626 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 10884.7878 98 111.069263 {txt}R-squared ={res} 0.2733 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.2659 {txt} Total {c |} {res} 14978.4138 99 151.297109 {txt}Root MSE = {res} 10.539 {txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} BMD{col 15}{c |} Coef.{col 27} Std. Err.{col 39} t{col 47} P>|t|{col 55} [95% Con{col 68}f. Interval] {hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} CalciumIntake {c |}{col 15}{res}{space 2} .0212909{col 27}{space 2} .003507{col 38}{space 1} 6.07{col 47}{space 3}0.000{col 55}{space 4} .0143314{col 68}{space 3} .0282505 {txt}{space 8}_cons {c |}{col 15}{res}{space 2} .4812509{col 27}{space 2} 3.661148{col 38}{space 1} 0.13{col 47}{space 3}0.896{col 55}{space 4}-6.784178{col 68}{space 3} 7.74668 {txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res} {txt}Linear regression{col 49}Number of obs{col 67}= {res} 1,000 {txt}{col 49}Replications{col 67}= {res} 10 {txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res} 22.03 {txt}{col 49}Prob > chi2{col 67}= {res} 0.0000 {txt}{col 49}R-squared{col 67}= {res} 0.2264 {txt}{col 49}Adj R-squared{col 67}= {res} 0.2256 {txt}{col 49}Root MSE{col 67}= {res} 10.3375 {txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 15}{c |} Observed{col 27} Bootstrap{col 55} Norm{col 68}al-based {col 1} BMD{col 15}{c |} Coef.{col 27} Std. Err.{col 39} z{col 47} P>|z|{col 55} [95% Con{col 68}f. Interval] {hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} CalciumIntake {c |}{col 15}{res}{space 2} .0190545{col 27}{space 2} .0040592{col 38}{space 1} 4.69{col 47}{space 3}0.000{col 55}{space 4} .0110986{col 68}{space 3} .0270105 {txt}{space 8}_cons {c |}{col 15}{res}{space 2} 2.004213{col 27}{space 2} 3.800265{col 38}{space 1} 0.53{col 47}{space 3}0.598{col 55}{space 4} -5.44417{col 68}{space 3} 9.452595 {txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {com}. . * the association stratified by your modifier of interest . bootstrap, reps(10) size(100) noisily: regress BMD Smoking##c.CalciumIntake bootstrap: First call to regress with data as is: . regress BMD Smoking##c.CalciumIntake {txt} Source {c |} SS df MS Number of obs ={res} 1,000 {txt}{hline 13}{c +}{hline 34} F(3, 996) > {res} 99999.00 {txt} Model {c |} {res} 137846.666 3 45948.8888 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 9.33819578 996 .009375699 {txt}R-squared ={res} 0.9999 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.9999 {txt} Total {c |} {res} 137856.005 999 137.993999 {txt}Root MSE = {res} .09683 {txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} BMD{col 25}{c |} Coef.{col 37} Std. Err.{col 49} t{col 57} P>|t|{col 65} [95% Con{col 78}f. Interval] {hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 14}1.Smoking {c |}{col 25}{res}{space 2}-.1139296{col 37}{space 2} .0215362{col 48}{space 1} -5.29{col 57}{space 3}0.000{col 65}{space 4}-.1561911{col 78}{space 3}-.0716681 {txt}{space 10}CalciumIntake {c |}{col 25}{res}{space 2} .0299713{col 37}{space 2} .0000153{col 48}{space 1} 1962.23{col 57}{space 3}0.000{col 65}{space 4} .0299413{col 78}{space 3} .0300012 {txt}{space 23} {c |} Smoking#c.CalciumIntake {c |} {space 21}1 {c |}{col 25}{res}{space 2}-.0199719{col 37}{space 2} .0000209{col 48}{space 1} -954.06{col 57}{space 3}0.000{col 65}{space 4} -.020013{col 78}{space 3}-.0199308 {txt}{space 23} {c |} {space 18}_cons {c |}{col 25}{res}{space 2} 1.028911{col 37}{space 2} .0156329{col 48}{space 1} 65.82{col 57}{space 3}0.000{col 65}{space 4} .998234{col 78}{space 3} 1.059588 {txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res} {txt}Bootstrap replications ({res}10{txt}) {com}. regress BMD Smoking##c.CalciumIntake {txt} Source {c |} SS df MS Number of obs ={res} 100 {txt}{hline 13}{c +}{hline 34} F(3, 96) > {res} 99999.00 {txt} Model {c |} {res} 14500.6083 3 4833.5361 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} .903353832 96 .009409936 {txt}R-squared ={res} 0.9999 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.9999 {txt} Total {c |} {res} 14501.5116 99 146.479916 {txt}Root MSE = {res} .097 {txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} BMD{col 25}{c |} Coef.{col 37} Std. Err.{col 49} t{col 57} P>|t|{col 65} [95% Con{col 78}f. Interval] {hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 14}1.Smoking {c |}{col 25}{res}{space 2}-.1691619{col 37}{space 2} .0744032{col 48}{space 1} -2.27{col 57}{space 3}0.025{col 65}{space 4} -.316851{col 78}{space 3}-.0214727 {txt}{space 10}CalciumIntake {c |}{col 25}{res}{space 2} .0299128{col 37}{space 2} .0000582{col 48}{space 1} 513.74{col 57}{space 3}0.000{col 65}{space 4} .0297973{col 78}{space 3} .0300284 {txt}{space 23} {c |} Smoking#c.CalciumIntake {c |} {space 21}1 {c |}{col 25}{res}{space 2}-.0199481{col 37}{space 2} .0000728{col 48}{space 1} -274.05{col 57}{space 3}0.000{col 65}{space 4}-.0200926{col 78}{space 3}-.0198036 {txt}{space 23} {c |} {space 18}_cons {c |}{col 25}{res}{space 2} 1.09429{col 37}{space 2} .061001{col 48}{space 1} 17.94{col 57}{space 3}0.000{col 65}{space 4} .9732038{col 78}{space 3} 1.215376 {txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{com}. regress BMD Smoking##c.CalciumIntake {txt} Source {c |} SS df MS Number of obs ={res} 100 {txt}{hline 13}{c +}{hline 34} F(3, 96) > {res} 99999.00 {txt} Model {c |} {res} 13323.151 3 4441.05033 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} .910025887 96 .009479436 {txt}R-squared ={res} 0.9999 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.9999 {txt} Total {c |} {res} 13324.061 99 134.586475 {txt}Root MSE = {res} .09736 {txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} BMD{col 25}{c |} Coef.{col 37} Std. Err.{col 49} t{col 57} P>|t|{col 65} [95% Con{col 78}f. Interval] {hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 14}1.Smoking {c |}{col 25}{res}{space 2}-.1180278{col 37}{space 2} .0719342{col 48}{space 1} -1.64{col 57}{space 3}0.104{col 65}{space 4}-.2608161{col 78}{space 3} .0247605 {txt}{space 10}CalciumIntake {c |}{col 25}{res}{space 2} .0299256{col 37}{space 2} .0000458{col 48}{space 1} 653.16{col 57}{space 3}0.000{col 65}{space 4} .0298347{col 78}{space 3} .0300165 {txt}{space 23} {c |} Smoking#c.CalciumIntake {c |} {space 21}1 {c |}{col 25}{res}{space 2}-.0199604{col 37}{space 2} .0000713{col 48}{space 1} -280.03{col 57}{space 3}0.000{col 65}{space 4}-.0201018{col 78}{space 3}-.0198189 {txt}{space 23} {c |} {space 18}_cons {c |}{col 25}{res}{space 2} 1.081556{col 37}{space 2} .0460797{col 48}{space 1} 23.47{col 57}{space 3}0.000{col 65}{space 4} .9900883{col 78}{space 3} 1.173023 {txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{com}. regress BMD Smoking##c.CalciumIntake {txt} Source {c |} SS df MS Number of obs ={res} 100 {txt}{hline 13}{c +}{hline 34} F(3, 96) > {res} 99999.00 {txt} Model {c |} {res} 13497.7714 3 4499.25715 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} .909826543 96 .00947736 {txt}R-squared ={res} 0.9999 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.9999 {txt} Total {c |} {res} 13498.6813 99 136.350316 {txt}Root MSE = {res} .09735 {txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} BMD{col 25}{c |} Coef.{col 37} Std. Err.{col 49} t{col 57} P>|t|{col 65} [95% Con{col 78}f. Interval] {hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 14}1.Smoking {c |}{col 25}{res}{space 2}-.0554188{col 37}{space 2} .0718007{col 48}{space 1} -0.77{col 57}{space 3}0.442{col 65}{space 4} -.197942{col 78}{space 3} .0871044 {txt}{space 10}CalciumIntake {c |}{col 25}{res}{space 2} .0300026{col 37}{space 2} .0000518{col 48}{space 1} 579.19{col 57}{space 3}0.000{col 65}{space 4} .0298998{col 78}{space 3} .0301054 {txt}{space 23} {c |} Smoking#c.CalciumIntake {c |} {space 21}1 {c |}{col 25}{res}{space 2}-.0200615{col 37}{space 2} .0000704{col 48}{space 1} -285.02{col 57}{space 3}0.000{col 65}{space 4}-.0202012{col 78}{space 3}-.0199218 {txt}{space 23} {c |} {space 18}_cons {c |}{col 25}{res}{space 2} 1.009756{col 37}{space 2} .0529161{col 48}{space 1} 19.08{col 57}{space 3}0.000{col 65}{space 4} .9047188{col 78}{space 3} 1.114794 {txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{com}. regress BMD Smoking##c.CalciumIntake {txt} Source {c |} SS df MS Number of obs ={res} 100 {txt}{hline 13}{c +}{hline 34} F(3, 96) > {res} 99999.00 {txt} Model {c |} {res} 15827.1058 3 5275.70194 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} .893723634 96 .009309621 {txt}R-squared ={res} 0.9999 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.9999 {txt} Total {c |} {res} 15827.9995 99 159.878783 {txt}Root MSE = {res} .09649 {txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} BMD{col 25}{c |} Coef.{col 37} Std. Err.{col 49} t{col 57} P>|t|{col 65} [95% Con{col 78}f. Interval] {hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 14}1.Smoking {c |}{col 25}{res}{space 2}-.0652996{col 37}{space 2} .0683239{col 48}{space 1} -0.96{col 57}{space 3}0.342{col 65}{space 4}-.2009214{col 78}{space 3} .0703223 {txt}{space 10}CalciumIntake {c |}{col 25}{res}{space 2} .0299731{col 37}{space 2} .0000468{col 48}{space 1} 640.17{col 57}{space 3}0.000{col 65}{space 4} .0298802{col 78}{space 3} .030066 {txt}{space 23} {c |} Smoking#c.CalciumIntake {c |} {space 21}1 {c |}{col 25}{res}{space 2}-.0199875{col 37}{space 2} .0000647{col 48}{space 1} -309.14{col 57}{space 3}0.000{col 65}{space 4}-.0201159{col 78}{space 3}-.0198592 {txt}{space 23} {c |} {space 18}_cons {c |}{col 25}{res}{space 2} 1.017419{col 37}{space 2} .0506524{col 48}{space 1} 20.09{col 57}{space 3}0.000{col 65}{space 4} .9168746{col 78}{space 3} 1.117963 {txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{com}. regress BMD Smoking##c.CalciumIntake {txt} Source {c |} SS df MS Number of obs ={res} 100 {txt}{hline 13}{c +}{hline 34} F(3, 96) > {res} 99999.00 {txt} Model {c |} {res} 13952.977 3 4650.99232 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 1.00469747 96 .010465599 {txt}R-squared ={res} 0.9999 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.9999 {txt} Total {c |} {res} 13953.9817 99 140.94931 {txt}Root MSE = {res} .1023 {txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} BMD{col 25}{c |} Coef.{col 37} Std. Err.{col 49} t{col 57} P>|t|{col 65} [95% Con{col 78}f. Interval] {hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 14}1.Smoking {c |}{col 25}{res}{space 2}-.1900684{col 37}{space 2} .070432{col 48}{space 1} -2.70{col 57}{space 3}0.008{col 65}{space 4}-.3298748{col 78}{space 3} -.050262 {txt}{space 10}CalciumIntake {c |}{col 25}{res}{space 2} .0298533{col 37}{space 2} .0000488{col 48}{space 1} 611.76{col 57}{space 3}0.000{col 65}{space 4} .0297564{col 78}{space 3} .0299502 {txt}{space 23} {c |} Smoking#c.CalciumIntake {c |} {space 21}1 {c |}{col 25}{res}{space 2}-.0198914{col 37}{space 2} .0000677{col 48}{space 1} -293.93{col 57}{space 3}0.000{col 65}{space 4}-.0200257{col 78}{space 3} -.019757 {txt}{space 23} {c |} {space 18}_cons {c |}{col 25}{res}{space 2} 1.166516{col 37}{space 2} .0501764{col 48}{space 1} 23.25{col 57}{space 3}0.000{col 65}{space 4} 1.066917{col 78}{space 3} 1.266115 {txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{com}. regress BMD Smoking##c.CalciumIntake {txt} Source {c |} SS df MS Number of obs ={res} 100 {txt}{hline 13}{c +}{hline 34} F(3, 96) > {res} 99999.00 {txt} Model {c |} {res} 13014.0019 3 4338.00064 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} .971984598 96 .01012484 {txt}R-squared ={res} 0.9999 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.9999 {txt} Total {c |} {res} 13014.9739 99 131.464383 {txt}Root MSE = {res} .10062 {txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} BMD{col 25}{c |} Coef.{col 37} Std. Err.{col 49} t{col 57} P>|t|{col 65} [95% Con{col 78}f. Interval] {hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 14}1.Smoking {c |}{col 25}{res}{space 2}-.0520091{col 37}{space 2} .071555{col 48}{space 1} -0.73{col 57}{space 3}0.469{col 65}{space 4}-.1940447{col 78}{space 3} .0900265 {txt}{space 10}CalciumIntake {c |}{col 25}{res}{space 2} .0300435{col 37}{space 2} .0000471{col 48}{space 1} 637.36{col 57}{space 3}0.000{col 65}{space 4} .02995{col 78}{space 3} .0301371 {txt}{space 23} {c |} Smoking#c.CalciumIntake {c |} {space 21}1 {c |}{col 25}{res}{space 2}-.0200271{col 37}{space 2} .0000702{col 48}{space 1} -285.13{col 57}{space 3}0.000{col 65}{space 4}-.0201665{col 78}{space 3}-.0198877 {txt}{space 23} {c |} {space 18}_cons {c |}{col 25}{res}{space 2} .9502599{col 37}{space 2} .0467586{col 48}{space 1} 20.32{col 57}{space 3}0.000{col 65}{space 4} .8574448{col 78}{space 3} 1.043075 {txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{com}. regress BMD Smoking##c.CalciumIntake {txt} Source {c |} SS df MS Number of obs ={res} 100 {txt}{hline 13}{c +}{hline 34} F(3, 96) > {res} 99999.00 {txt} Model {c |} {res} 13475.518 3 4491.83932 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} .961490736 96 .010015528 {txt}R-squared ={res} 0.9999 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.9999 {txt} Total {c |} {res} 13476.4795 99 136.126055 {txt}Root MSE = {res} .10008 {txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} BMD{col 25}{c |} Coef.{col 37} Std. Err.{col 49} t{col 57} P>|t|{col 65} [95% Con{col 78}f. Interval] {hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 14}1.Smoking {c |}{col 25}{res}{space 2} .0028424{col 37}{space 2} .0688114{col 48}{space 1} 0.04{col 57}{space 3}0.967{col 65}{space 4}-.1337471{col 78}{space 3} .1394319 {txt}{space 10}CalciumIntake {c |}{col 25}{res}{space 2} .0300463{col 37}{space 2} .0000547{col 48}{space 1} 548.85{col 57}{space 3}0.000{col 65}{space 4} .0299376{col 78}{space 3} .030155 {txt}{space 23} {c |} Smoking#c.CalciumIntake {c |} {space 21}1 {c |}{col 25}{res}{space 2}-.0200842{col 37}{space 2} .000067{col 48}{space 1} -299.63{col 57}{space 3}0.000{col 65}{space 4}-.0202173{col 78}{space 3}-.0199511 {txt}{space 23} {c |} {space 18}_cons {c |}{col 25}{res}{space 2} .9475204{col 37}{space 2} .0556733{col 48}{space 1} 17.02{col 57}{space 3}0.000{col 65}{space 4} .8370098{col 78}{space 3} 1.058031 {txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{com}. regress BMD Smoking##c.CalciumIntake {txt} Source {c |} SS df MS Number of obs ={res} 100 {txt}{hline 13}{c +}{hline 34} F(3, 96) > {res} 99999.00 {txt} Model {c |} {res} 14457.4907 3 4819.16357 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} .832284253 96 .008669628 {txt}R-squared ={res} 0.9999 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.9999 {txt} Total {c |} {res} 14458.323 99 146.043667 {txt}Root MSE = {res} .09311 {txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} BMD{col 25}{c |} Coef.{col 37} Std. Err.{col 49} t{col 57} P>|t|{col 65} [95% Con{col 78}f. Interval] {hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 14}1.Smoking {c |}{col 25}{res}{space 2}-.1347341{col 37}{space 2} .0625966{col 48}{space 1} -2.15{col 57}{space 3}0.034{col 65}{space 4}-.2589875{col 78}{space 3}-.0104808 {txt}{space 10}CalciumIntake {c |}{col 25}{res}{space 2} .0300511{col 37}{space 2} .0000409{col 48}{space 1} 734.94{col 57}{space 3}0.000{col 65}{space 4} .0299699{col 78}{space 3} .0301323 {txt}{space 23} {c |} Smoking#c.CalciumIntake {c |} {space 21}1 {c |}{col 25}{res}{space 2}-.0199147{col 37}{space 2} .000065{col 48}{space 1} -306.59{col 57}{space 3}0.000{col 65}{space 4}-.0200436{col 78}{space 3}-.0197858 {txt}{space 23} {c |} {space 18}_cons {c |}{col 25}{res}{space 2} .939027{col 37}{space 2} .0407665{col 48}{space 1} 23.03{col 57}{space 3}0.000{col 65}{space 4} .8581061{col 78}{space 3} 1.019948 {txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{com}. regress BMD Smoking##c.CalciumIntake {txt} Source {c |} SS df MS Number of obs ={res} 100 {txt}{hline 13}{c +}{hline 34} F(3, 96) > {res} 99999.00 {txt} Model {c |} {res} 11917.4661 3 3972.48871 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} .914833735 96 .009529518 {txt}R-squared ={res} 0.9999 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.9999 {txt} Total {c |} {res} 11918.381 99 120.387687 {txt}Root MSE = {res} .09762 {txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} BMD{col 25}{c |} Coef.{col 37} Std. Err.{col 49} t{col 57} P>|t|{col 65} [95% Con{col 78}f. Interval] {hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 14}1.Smoking {c |}{col 25}{res}{space 2} -.128612{col 37}{space 2} .0677671{col 48}{space 1} -1.90{col 57}{space 3}0.061{col 65}{space 4}-.2631286{col 78}{space 3} .0059046 {txt}{space 10}CalciumIntake {c |}{col 25}{res}{space 2} .0299347{col 37}{space 2} .0000551{col 48}{space 1} 543.65{col 57}{space 3}0.000{col 65}{space 4} .0298254{col 78}{space 3} .030044 {txt}{space 23} {c |} Smoking#c.CalciumIntake {c |} {space 21}1 {c |}{col 25}{res}{space 2}-.0199444{col 37}{space 2} .0000674{col 48}{space 1} -295.95{col 57}{space 3}0.000{col 65}{space 4}-.0200782{col 78}{space 3}-.0198106 {txt}{space 23} {c |} {space 18}_cons {c |}{col 25}{res}{space 2} 1.046459{col 37}{space 2} .0540746{col 48}{space 1} 19.35{col 57}{space 3}0.000{col 65}{space 4} .9391223{col 78}{space 3} 1.153797 {txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{com}. regress BMD Smoking##c.CalciumIntake {txt} Source {c |} SS df MS Number of obs ={res} 100 {txt}{hline 13}{c +}{hline 34} F(3, 96) > {res} 99999.00 {txt} Model {c |} {res} 13123.217 3 4374.40567 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} .78823604 96 .008210792 {txt}R-squared ={res} 0.9999 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.9999 {txt} Total {c |} {res} 13124.0052 99 132.56571 {txt}Root MSE = {res} .09061 {txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} BMD{col 25}{c |} Coef.{col 37} Std. Err.{col 49} t{col 57} P>|t|{col 65} [95% Con{col 78}f. Interval] {hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 14}1.Smoking {c |}{col 25}{res}{space 2}-.1628457{col 37}{space 2} .0606693{col 48}{space 1} -2.68{col 57}{space 3}0.009{col 65}{space 4}-.2832733{col 78}{space 3}-.0424181 {txt}{space 10}CalciumIntake {c |}{col 25}{res}{space 2} .0300283{col 37}{space 2} .0000446{col 48}{space 1} 673.40{col 57}{space 3}0.000{col 65}{space 4} .0299397{col 78}{space 3} .0301168 {txt}{space 23} {c |} Smoking#c.CalciumIntake {c |} {space 21}1 {c |}{col 25}{res}{space 2}-.0199284{col 37}{space 2} .0000586{col 48}{space 1} -339.99{col 57}{space 3}0.000{col 65}{space 4}-.0200447{col 78}{space 3} -.019812 {txt}{space 23} {c |} {space 18}_cons {c |}{col 25}{res}{space 2} .962684{col 37}{space 2} .0447959{col 48}{space 1} 21.49{col 57}{space 3}0.000{col 65}{space 4} .8737648{col 78}{space 3} 1.051603 {txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res} {txt}Linear regression{col 49}Number of obs{col 67}= {res} 1,000 {txt}{col 49}Replications{col 67}= {res} 10 {txt}{col 49}Wald chi2({res}3{txt}){col 67}= {res}1341698.20 {txt}{col 49}Prob > chi2{col 67}= {res} 0.0000 {txt}{col 49}R-squared{col 67}= {res} 0.9999 {txt}{col 49}Adj R-squared{col 67}= {res} 0.9999 {txt}{col 49}Root MSE{col 67}= {res} 0.0968 {txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 25}{c |} Observed{col 37} Bootstrap{col 65} Norm{col 78}al-based {col 1} BMD{col 25}{c |} Coef.{col 37} Std. Err.{col 49} z{col 57} P>|z|{col 65} [95% Con{col 78}f. Interval] {hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {space 14}1.Smoking {c |}{col 25}{res}{space 2}-.1139296{col 37}{space 2} .0621189{col 48}{space 1} -1.83{col 57}{space 3}0.067{col 65}{space 4}-.2356805{col 78}{space 3} .0078213 {txt}{space 10}CalciumIntake {c |}{col 25}{res}{space 2} .0299713{col 37}{space 2} .0000682{col 48}{space 1} 439.47{col 57}{space 3}0.000{col 65}{space 4} .0298376{col 78}{space 3} .0301049 {txt}{space 23} {c |} Smoking#c.CalciumIntake {c |} {space 21}1 {c |}{col 25}{res}{space 2}-.0199719{col 37}{space 2} .0000641{col 48}{space 1} -311.67{col 57}{space 3}0.000{col 65}{space 4}-.0200975{col 78}{space 3}-.0198463 {txt}{space 23} {c |} {space 18}_cons {c |}{col 25}{res}{space 2} 1.028911{col 37}{space 2} .0756611{col 48}{space 1} 13.60{col 57}{space 3}0.000{col 65}{space 4} .8806181{col 78}{space 3} 1.177204 {txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {com}. . * ---- 3. Setting the causal effect to the null and repeat 1. & 2. . drop BMD {txt} {com}. gen BMD = 1.0 + 0.02 * CalciumIntake + ErrorTerm {txt} {com}. . * the population average exposure-outcome association, i.e., don't account for eff. modifier . bootstrap, reps(10) size(100) noisily: regress BMD CalciumIntake bootstrap: First call to regress with data as is: . regress BMD CalciumIntake {txt} Source {c |} SS df MS Number of obs ={res} 1,000 {txt}{hline 13}{c +}{hline 34} F(1, 998) > {res} 99999.00 {txt} Model {c |} {res} 34332.6744 1 34332.6744 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 9.40269665 998 .00942154 {txt}R-squared ={res} 0.9997 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.9997 {txt} Total {c |} {res} 34342.0771 999 34.3764535 {txt}Root MSE = {res} .09706 {txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} BMD{col 15}{c |} Coef.{col 27} Std. Err.{col 39} t{col 47} P>|t|{col 55} [95% Con{col 68}f. Interval] {hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} CalciumIntake {c |}{col 15}{res}{space 2} .0199864{col 27}{space 2} .0000105{col 38}{space 1} 1908.94{col 47}{space 3}0.000{col 55}{space 4} .0199659{col 68}{space 3} .020007 {txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.020791{col 27}{space 2} .0107743{col 38}{space 1} 94.74{col 47}{space 3}0.000{col 55}{space 4} .9996482{col 68}{space 3} 1.041934 {txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res} {txt}Bootstrap replications ({res}10{txt}) {com}. regress BMD CalciumIntake {txt} Source {c |} SS df MS Number of obs ={res} 100 {txt}{hline 13}{c +}{hline 34} F(1, 98) > {res} 99999.00 {txt} Model {c |} {res} 3310.23431 1 3310.23431 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} .816624525 98 .008332903 {txt}R-squared ={res} 0.9998 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.9998 {txt} Total {c |} {res} 3311.05093 99 33.4449589 {txt}Root MSE = {res} .09128 {txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} BMD{col 15}{c |} Coef.{col 27} Std. Err.{col 39} t{col 47} P>|t|{col 55} [95% Con{col 68}f. Interval] {hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} CalciumIntake {c |}{col 15}{res}{space 2} .0199957{col 27}{space 2} .0000317{col 38}{space 1} 630.28{col 47}{space 3}0.000{col 55}{space 4} .0199328{col 68}{space 3} .0200587 {txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.001822{col 27}{space 2} .0316862{col 38}{space 1} 31.62{col 47}{space 3}0.000{col 55}{space 4} .938942{col 68}{space 3} 1.064702 {txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{com}. regress BMD CalciumIntake {txt} Source {c |} SS df MS Number of obs ={res} 100 {txt}{hline 13}{c +}{hline 34} F(1, 98) > {res} 99999.00 {txt} Model {c |} {res} 3444.28342 1 3444.28342 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} .996514138 98 .010168512 {txt}R-squared ={res} 0.9997 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.9997 {txt} Total {c |} {res} 3445.27993 99 34.8008074 {txt}Root MSE = {res} .10084 {txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} BMD{col 15}{c |} Coef.{col 27} Std. Err.{col 39} t{col 47} P>|t|{col 55} [95% Con{col 68}f. Interval] {hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} CalciumIntake {c |}{col 15}{res}{space 2} .0200372{col 27}{space 2} .0000344{col 38}{space 1} 582.00{col 47}{space 3}0.000{col 55}{space 4} .0199688{col 68}{space 3} .0201055 {txt}{space 8}_cons {c |}{col 15}{res}{space 2} .9650738{col 27}{space 2} .0363245{col 38}{space 1} 26.57{col 47}{space 3}0.000{col 55}{space 4} .892989{col 68}{space 3} 1.037159 {txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{com}. regress BMD CalciumIntake {txt} Source {c |} SS df MS Number of obs ={res} 100 {txt}{hline 13}{c +}{hline 34} F(1, 98) > {res} 99999.00 {txt} Model {c |} {res} 2984.52678 1 2984.52678 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} .758354252 98 .007738309 {txt}R-squared ={res} 0.9997 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.9997 {txt} Total {c |} {res} 2985.28513 99 30.1543953 {txt}Root MSE = {res} .08797 {txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} BMD{col 15}{c |} Coef.{col 27} Std. Err.{col 39} t{col 47} P>|t|{col 55} [95% Con{col 68}f. Interval] {hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} CalciumIntake {c |}{col 15}{res}{space 2} .0199486{col 27}{space 2} .0000321{col 38}{space 1} 621.03{col 47}{space 3}0.000{col 55}{space 4} .0198849{col 68}{space 3} .0200124 {txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.055382{col 27}{space 2} .0332941{col 38}{space 1} 31.70{col 47}{space 3}0.000{col 55}{space 4} .9893109{col 68}{space 3} 1.121453 {txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{com}. regress BMD CalciumIntake {txt} Source {c |} SS df MS Number of obs ={res} 100 {txt}{hline 13}{c +}{hline 34} F(1, 98) > {res} 99999.00 {txt} Model {c |} {res} 3636.00343 1 3636.00343 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} .96319935 98 .009828565 {txt}R-squared ={res} 0.9997 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.9997 {txt} Total {c |} {res} 3636.96663 99 36.7370366 {txt}Root MSE = {res} .09914 {txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} BMD{col 15}{c |} Coef.{col 27} Std. Err.{col 39} t{col 47} P>|t|{col 55} [95% Con{col 68}f. Interval] {hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} CalciumIntake {c |}{col 15}{res}{space 2} .0200241{col 27}{space 2} .0000329{col 38}{space 1} 608.23{col 47}{space 3}0.000{col 55}{space 4} .0199587{col 68}{space 3} .0200894 {txt}{space 8}_cons {c |}{col 15}{res}{space 2} .9635048{col 27}{space 2} .0344384{col 38}{space 1} 27.98{col 47}{space 3}0.000{col 55}{space 4} .895163{col 68}{space 3} 1.031847 {txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{com}. regress BMD CalciumIntake {txt} Source {c |} SS df MS Number of obs ={res} 100 {txt}{hline 13}{c +}{hline 34} F(1, 98) > {res} 99999.00 {txt} Model {c |} {res} 3486.30668 1 3486.30668 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} .793091246 98 .008092768 {txt}R-squared ={res} 0.9998 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.9998 {txt} Total {c |} {res} 3487.09977 99 35.2232301 {txt}Root MSE = {res} .08996 {txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} BMD{col 15}{c |} Coef.{col 27} Std. Err.{col 39} t{col 47} P>|t|{col 55} [95% Con{col 68}f. Interval] {hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} CalciumIntake {c |}{col 15}{res}{space 2} .0199699{col 27}{space 2} .0000304{col 38}{space 1} 656.35{col 47}{space 3}0.000{col 55}{space 4} .0199095{col 68}{space 3} .0200303 {txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.051733{col 27}{space 2} .0332769{col 38}{space 1} 31.61{col 47}{space 3}0.000{col 55}{space 4} .9856965{col 68}{space 3} 1.11777 {txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{com}. regress BMD CalciumIntake {txt} Source {c |} SS df MS Number of obs ={res} 100 {txt}{hline 13}{c +}{hline 34} F(1, 98) > {res} 99999.00 {txt} Model {c |} {res} 3522.58435 1 3522.58435 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} .899390783 98 .009177457 {txt}R-squared ={res} 0.9997 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.9997 {txt} Total {c |} {res} 3523.48374 99 35.5907449 {txt}Root MSE = {res} .0958 {txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} BMD{col 15}{c |} Coef.{col 27} Std. Err.{col 39} t{col 47} P>|t|{col 55} [95% Con{col 68}f. Interval] {hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} CalciumIntake {c |}{col 15}{res}{space 2} .0199314{col 27}{space 2} .0000322{col 38}{space 1} 619.54{col 47}{space 3}0.000{col 55}{space 4} .0198675{col 68}{space 3} .0199952 {txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.07559{col 27}{space 2} .0316983{col 38}{space 1} 33.93{col 47}{space 3}0.000{col 55}{space 4} 1.012686{col 68}{space 3} 1.138494 {txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{com}. regress BMD CalciumIntake {txt} Source {c |} SS df MS Number of obs ={res} 100 {txt}{hline 13}{c +}{hline 34} F(1, 98) > {res} 99999.00 {txt} Model {c |} {res} 3471.70149 1 3471.70149 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} .871947403 98 .008897422 {txt}R-squared ={res} 0.9997 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.9997 {txt} Total {c |} {res} 3472.57344 99 35.0764994 {txt}Root MSE = {res} .09433 {txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} BMD{col 15}{c |} Coef.{col 27} Std. Err.{col 39} t{col 47} P>|t|{col 55} [95% Con{col 68}f. Interval] {hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} CalciumIntake {c |}{col 15}{res}{space 2} .0200204{col 27}{space 2} .0000321{col 38}{space 1} 624.65{col 47}{space 3}0.000{col 55}{space 4} .0199568{col 68}{space 3} .020084 {txt}{space 8}_cons {c |}{col 15}{res}{space 2} .9892422{col 27}{space 2} .0344696{col 38}{space 1} 28.70{col 47}{space 3}0.000{col 55}{space 4} .9208384{col 68}{space 3} 1.057646 {txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{com}. regress BMD CalciumIntake {txt} Source {c |} SS df MS Number of obs ={res} 100 {txt}{hline 13}{c +}{hline 34} F(1, 98) > {res} 99999.00 {txt} Model {c |} {res} 3331.0541 1 3331.0541 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} .926753539 98 .009456669 {txt}R-squared ={res} 0.9997 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.9997 {txt} Total {c |} {res} 3331.98085 99 33.6563722 {txt}Root MSE = {res} .09725 {txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} BMD{col 15}{c |} Coef.{col 27} Std. Err.{col 39} t{col 47} P>|t|{col 55} [95% Con{col 68}f. Interval] {hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} CalciumIntake {c |}{col 15}{res}{space 2} .0199847{col 27}{space 2} .0000337{col 38}{space 1} 593.50{col 47}{space 3}0.000{col 55}{space 4} .0199179{col 68}{space 3} .0200515 {txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.023243{col 27}{space 2} .0350209{col 38}{space 1} 29.22{col 47}{space 3}0.000{col 55}{space 4} .9537455{col 68}{space 3} 1.092741 {txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{com}. regress BMD CalciumIntake {txt} Source {c |} SS df MS Number of obs ={res} 100 {txt}{hline 13}{c +}{hline 34} F(1, 98) > {res} 99999.00 {txt} Model {c |} {res} 2577.3417 1 2577.3417 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} .696932195 98 .007111553 {txt}R-squared ={res} 0.9997 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.9997 {txt} Total {c |} {res} 2578.03864 99 26.0407943 {txt}Root MSE = {res} .08433 {txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} BMD{col 15}{c |} Coef.{col 27} Std. Err.{col 39} t{col 47} P>|t|{col 55} [95% Con{col 68}f. Interval] {hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} CalciumIntake {c |}{col 15}{res}{space 2} .0199321{col 27}{space 2} .0000331{col 38}{space 1} 602.01{col 47}{space 3}0.000{col 55}{space 4} .0198664{col 68}{space 3} .0199978 {txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.064855{col 27}{space 2} .0332615{col 38}{space 1} 32.01{col 47}{space 3}0.000{col 55}{space 4} .9988482{col 68}{space 3} 1.130861 {txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res}{com}. regress BMD CalciumIntake {txt} Source {c |} SS df MS Number of obs ={res} 100 {txt}{hline 13}{c +}{hline 34} F(1, 98) > {res} 99999.00 {txt} Model {c |} {res} 3394.7368 1 3394.7368 {txt}Prob > F ={res} 0.0000 {txt} Residual {c |} {res} 1.04642792 98 .010677836 {txt}R-squared ={res} 0.9997 {txt}{hline 13}{c +}{hline 34} Adj R-squared ={res} 0.9997 {txt} Total {c |} {res} 3395.78323 99 34.3008407 {txt}Root MSE = {res} .10333 {txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 1} BMD{col 15}{c |} Coef.{col 27} Std. Err.{col 39} t{col 47} P>|t|{col 55} [95% Con{col 68}f. Interval] {hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} CalciumIntake {c |}{col 15}{res}{space 2} .0199713{col 27}{space 2} .0000354{col 38}{space 1} 563.85{col 47}{space 3}0.000{col 55}{space 4} .019901{col 68}{space 3} .0200416 {txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.037706{col 27}{space 2} .0350405{col 38}{space 1} 29.61{col 47}{space 3}0.000{col 55}{space 4} .9681689{col 68}{space 3} 1.107243 {txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {res} {txt}Linear regression{col 49}Number of obs{col 67}= {res} 1,000 {txt}{col 49}Replications{col 67}= {res} 10 {txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res} 278522.51 {txt}{col 49}Prob > chi2{col 67}= {res} 0.0000 {txt}{col 49}R-squared{col 67}= {res} 0.9997 {txt}{col 49}Adj R-squared{col 67}= {res} 0.9997 {txt}{col 49}Root MSE{col 67}= {res} 0.0971 {txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 15}{c |} Observed{col 27} Bootstrap{col 55} Norm{col 68}al-based {col 1} BMD{col 15}{c |} Coef.{col 27} Std. Err.{col 39} z{col 47} P>|z|{col 55} [95% Con{col 68}f. Interval] {hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} CalciumIntake {c |}{col 15}{res}{space 2} .0199864{col 27}{space 2} .0000379{col 38}{space 1} 527.75{col 47}{space 3}0.000{col 55}{space 4} .0199122{col 68}{space 3} .0200606 {txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.020791{col 27}{space 2} .0409483{col 38}{space 1} 24.93{col 47}{space 3}0.000{col 55}{space 4} .940534{col 68}{space 3} 1.101048 {txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {com}. . * run it 1000 times . bootstrap, reps(1000) size(100): regress BMD CalciumIntake {txt}(running regress on estimation sample) Bootstrap replications ({res}1000{txt}) {hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 .................................................. 50 .................................................. 100 .................................................. 150 .................................................. 200 .................................................. 250 .................................................. 300 .................................................. 350 .................................................. 400 .................................................. 450 .................................................. 500 .................................................. 550 .................................................. 600 .................................................. 650 .................................................. 700 .................................................. 750 .................................................. 800 .................................................. 850 .................................................. 900 .................................................. 950 .................................................. 1000 {res} {txt}Linear regression{col 49}Number of obs{col 67}= {res} 1,000 {txt}{col 49}Replications{col 67}= {res} 1,000 {txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res} 344527.15 {txt}{col 49}Prob > chi2{col 67}= {res} 0.0000 {txt}{col 49}R-squared{col 67}= {res} 0.9997 {txt}{col 49}Adj R-squared{col 67}= {res} 0.9997 {txt}{col 49}Root MSE{col 67}= {res} 0.0971 {txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {col 15}{c |} Observed{col 27} Bootstrap{col 55} Norm{col 68}al-based {col 1} BMD{col 15}{c |} Coef.{col 27} Std. Err.{col 39} z{col 47} P>|z|{col 55} [95% Con{col 68}f. Interval] {hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} CalciumIntake {c |}{col 15}{res}{space 2} .0199864{col 27}{space 2} .0000341{col 38}{space 1} 586.96{col 47}{space 3}0.000{col 55}{space 4} .0199197{col 68}{space 3} .0200532 {txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.020791{col 27}{space 2} .0354324{col 38}{space 1} 28.81{col 47}{space 3}0.000{col 55}{space 4} .9513451{col 68}{space 3} 1.090237 {txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12} {com}. . * ---- 4. estimate statistical power to detect the difference in means . power onemean 0.02 0.12, sd(1) n(100) {res} {p 0 2 2}{txt}Estimated power for a one-sample mean test{p_end}{txt}t test {txt}{txt}{bind:Ho: m = m0} {txt}versus {bind:Ha: m != m0} {txt}Study parameters: {txt}{ralign 16:alpha = }{res} 0.0500 {txt}{ralign 16:N = }{res} 100 {txt}{ralign 16:delta = }{res} 0.1000 {txt}{ralign 16:m0 = }{res} 0.0200 {txt}{ralign 16:ma = }{res} 0.1200 {txt}{ralign 16:sd = }{res} 1.0000 {p}{txt}Estimated power:{p_end} {txt}{ralign 16:power = }{res} 0.1677 {txt} {com}. power onemean 0.02 0.12, sd(2) n(100) {res} {p 0 2 2}{txt}Estimated power for a one-sample mean test{p_end}{txt}t test {txt}{txt}{bind:Ho: m = m0} {txt}versus {bind:Ha: m != m0} {txt}Study parameters: {txt}{ralign 16:alpha = }{res} 0.0500 {txt}{ralign 16:N = }{res} 100 {txt}{ralign 16:delta = }{res} 0.0500 {txt}{ralign 16:m0 = }{res} 0.0200 {txt}{ralign 16:ma = }{res} 0.1200 {txt}{ralign 16:sd = }{res} 2.0000 {p}{txt}Estimated power:{p_end} {txt}{ralign 16:power = }{res} 0.0785 {txt} {com}. power onemean 0.3 0.3, sd(1) n(500) {res}{err}{p}null and alternative means are equal; this is not allowed{p_end} {txt}{search r(198), local:r(198);} end of do-file {search r(198), local:r(198);} {com}. help runiform . do "/var/folders/rm/pcbq8xt88v52870059bb8fbh0000gn/T//SD28463.000000" {txt} {com}. ***Epidemiology 265, Assignment 8 . **Stephen Chang . . clear {txt} {com}. . cd "/Users/stephenchang/Desktop/TICR Program/Spring 2017/EPI 265 Epidemiologic Methods III Research Methods in Chronic Disease Epidemiology/Lecture 8/HW 8" {err}unable to change to /Users/stephenchang/Desktop/TICR Program/Spring 2017/EPI 265 Epidemiologic Methods III Research Methods in Chronic Disease Epidemiology/Lecture 8/HW 8 {txt}{search r(170), local:r(170);} end of do-file {search r(170), local:r(170);} {com}. do "/Users/stephenchang/Desktop/TICR Program/Spring 2017/EPI 265 Epidemiologic Methods III Research Methods in Chronic Disease Epidemiology/Lecture 8/HW8/Week 8.do" {txt} {com}. ***Epidemiology 265, Assignment 8 . **Stephen Chang . . clear {txt} {com}. . cd "/Users/stephenchang/Desktop/TICR Program/Spring 2017/EPI 265 Epidemiologic Methods III Research Methods in Chronic Disease Epidemiology/Lecture 8/HW8" {res}/Users/stephenchang/Desktop/TICR Program/Spring 2017/EPI 265 Epidemiologic Methods III Research Methods in Chronic Disease Epidemiology/Lecture 8/HW8 {txt} {com}. . capture log close {smcl} {com}{sf}{ul off}