Stata versions 12 & 13 Week 4 Practice Problems

Transcription

1 Stata versions 12 & 13 Week 4 Practice Problems SOLUTIONS 1 Practice Screen Capture a Create a word document Name it using the convention lastname_lab1docx (eg bigelow_lab1docx) b Using your browser, go to the welcome page for PubHlth 640 c From there, navigate to the assignments page d Capture the picture of the ostrich e Paste the picture into lastname_lab1doc, inserting it into a table with 1 row and 1 column 2 Launch Stata and Start a Log of Your Session IMPORTANT Use extension log not scml a Launch Stata b Start a log of your session, with extension log Name it lastname_log1log (eg bigelow_log1log) c In the command window, type: set more off Sol_statlab1docx revised 3/2/2014 Page 1 of 10

2 3 Create a graph Save it Paste it into your word document a Launch Stata b In the command window, type: use clear c In the command window, type: histogram hyp, discrete d Save this as a png graph, with name hypertension_barpng, to your desktop e Paste your graph into lastname_lab1doc, again inserting it into a table with 1 row and 1 column Sol_statlab1docx revised 3/2/2014 Page 2 of 10

3 4 Create a new data set in Stata using Data Editor a Execute the following commands to create a Stata data set with the following 4 observations: id type: numeric dob type: date gender type: string/character 1 3/26/1926 male /9/1956 female /1/1954 male /4/1951 female 1240 weight type: numeric * STEP 1: Clear the current data from memory clear * STEP 2: Define variables (lower case recommended) Set type Initialize to missing generate id= generate str8 dob_string="" generate str8 gender="" generate weight= * STEP 3: Click on DATA EDITOR icon to access an initially empty spreadsheet * Enter the data Then close the data editor window * STEP 4: Create a DATE variable called dob (date of birth) Drop string variable generate dob=date(dob_string, "MDY") format dob %tdnn/dd/ccyy drop dob_string * STEP 5: Create 0/1 indicator of female gender generate female=(gender=="female") * STEP 6: Label variables label variable id "Subject id" label variable weight "weight (lbs)" label variable dob "Date of birth" label variable female "0/1 female" * STEP 7: Create discrete variable value labels (the dictionary) label define femalef 0 "male" 1 "female" * STEP 8: Attach labels to discrete variable values label values female femalef list * Produce listing of data list * Save data set using FILE > SAVE AS Sol_statlab1docx revised 3/2/2014 Page 3 of 10

4 b Paste your listing of data into lastname_lab1doc, again inserting it into a table with 1 row and 1 column id gender weight dob female male /26/1926 male 2 2 female /09/1956 female 3 3 male /01/1954 male 4 4 female /04/1951 female Numerical Descriptives a Execute the following commands to produce the numerical descriptives indicated b Paste this portion of your stata log into lastname_lab1doc, again inserting it into a 1x1 table clear use " clear (In Vitro Fertilization data) sort sex tabstat bweight, by(sex) col(stat) stat(n mean sd sem min q max) Summary for variables: bweight by categories of: sex (sex of the baby) sex N mean sd se(mean) min p25 p50 p75 max male female Total Sol_statlab1docx revised 3/2/2014 Page 4 of 10

5 6 One and Two Sample Inference a Execute the following commands to produce the standard one and two sample tests b Paste this portion of your stata log into lastname_lab1doc, again inserting it into a 1x1 table ** ONE CONTINUOUS VARIABLE 99% CI for mean using command ci ci gestwks, level(99) Variable Obs Mean Std Err [99% Conf Interval] gestwks ** ONE CONTNUOUS VARIABLE - Test of null: mean=40 using command ttest ttest gestwks=40 One-sample t test Variable Obs Mean Std Err Std Dev [95% Conf Interval] gestwks mean = mean(gestwks) t = Ho: mean = 40 degrees of freedom = 640 Ha: mean < 40 Ha: mean!= 40 Ha: mean > 40 Pr(T < t) = Pr( T > t ) = Pr(T > t) = ** ONE CONTINUOUS VARIABLE - Test of null: standard deviation = 1 using command sdtest sdtest gestwks=1 One-sample test of variance Variable Obs Mean Std Err Std Dev [95% Conf Interval] gestwks sd = sd(gestwks) c = chi2 = 35e+03 Ho: sd = 1 degrees of freedom = 640 Ha: sd < 1 Ha: sd!= 1 Ha: sd > 1 Pr(C < c) = *Pr(C > c) = Pr(C > c) = Sol_statlab1docx revised 3/2/2014 Page 5 of 10

6 ** TWO CONTINUOUS VARIABLES - Test of null: Equality of 2 INDEPENDENT means using ttest sort sex ttest gestwks, by(sex) Two-sample t test with equal variances Group Obs Mean Std Err Std Dev [95% Conf Interval] male female combined diff diff = mean(male) - mean(female) t = Ho: diff = 0 degrees of freedom = 639 Ha: diff < 0 Ha: diff!= 0 Ha: diff > 0 Pr(T < t) = Pr( T > t ) = Pr(T > t) = ttest gestwks, by(sex) unequal Two-sample t test with unequal variances Group Obs Mean Std Err Std Dev [95% Conf Interval] male female combined diff diff = mean(male) - mean(female) t = Ho: diff = 0 Satterthwaite's degrees of freedom = Ha: diff < 0 Ha: diff!= 0 Ha: diff > 0 Pr(T < t) = Pr( T > t ) = Pr(T > t) = ** TWO CONTINUOUS VARIABLES - Test of equality of 2 independent variances using sdtest sdtest gestwks, by(sex) Variance ratio test Group Obs Mean Std Err Std Dev [95% Conf Interval] male female combined ratio = sd(male) / sd(female) f = Ho: ratio = 1 degrees of freedom = 325, 314 Ha: ratio < 1 Ha: ratio!= 1 Ha: ratio > 1 Pr(F < f) = *Pr(F < f) = Pr(F > f) = Sol_statlab1docx revised 3/2/2014 Page 6 of 10

7 ** 1 DISCRETE (0/1) VARIABLE - Test of binomial proportion using bitest and prtest ** Test of null: proportion of female births = 50 generate female=(sex==2) bitest female=50 Variable N Observed k Expected k Assumed p Observed p female Pr(k >= 315) = (one-sided test) Pr(k <= 315) = (one-sided test) Pr(k <= 315 or k >= 326) = (two-sided test) prtest female=50 One-sample test of proportion female: Number of obs = 641 Variable Mean Std Err [95% Conf Interval] female p = proportion(female) z = Ho: p = 05 Ha: p < 05 Ha: p!= 05 Ha: p > 05 Pr(Z < z) = Pr( Z > z ) = Pr(Z > z) = ** 1 DISCRETE (0/1) VARIABLE - 95% CI for event probability using ci & option binomial ci female, binomial level(95) -- Binomial Exact -- Variable Obs Mean Std Err [95% Conf Interval] female ** 2 DISCRETE (0/1) VARIABLES - Test of equality of probabilities using prtest sort sex prtest hyp, by(sex) Two-sample test of proportions male: Number of obs = 325 female: Number of obs = 314 Variable Mean Std Err z P> z [95% Conf Interval] male female diff under Ho: diff = prop(male) - prop(female) z = Ho: diff = 0 Ha: diff < 0 Ha: diff!= 0 Ha: diff > 0 Pr(Z < z) = Pr( Z < z ) = Pr(Z > z) = Sol_statlab1docx revised 3/2/2014 Page 7 of 10

8 **2 DISCRETE VARIABLES - chi square test using tab2 with option chi2 tab2 sex hyp, row column chi2 -> tabulation of sex by hyp hypertension (1=yes, sex of the 0=no) baby 0 1 Total male female Total Pearson chi2(1) = Pr = 0124 ** 2 DISCRETE VARIABLES - Fisher exact test using tab2 with option exact tab2 sex hyp, row column exact -> tabulation of sex by hyp hypertension (1=yes, sex of the 0=no) baby 0 1 Total male female Total Fisher's exact = sided Fisher's exact = 0077 Sol_statlab1docx revised 3/2/2014 Page 8 of 10

9 7 Simple and Multiple Linear Regression a Execute the following commands to produce some regressions b Paste this portion of your stata log into lastname_lab1doc, again inserting it into a 1x1 table clear * Depending on your Stata purchase, import EITHER hersdatadta or hersdata100dta * Choice 1 of 2: hersdatadta for Stata/IC use " clear * Choice 2 of 2: hersdatadta for SMALL Stata use " clear ** SOLUTIONS shown here utilize the larger data set, hersdatadta ** One predictor - continuous regress glucose BMI Source SS df MS Number of obs = F( 1, 2756) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = glucose Coef Std Err t P> t [95% Conf Interval] BMI _cons ** One predictor - nominal physical activity with design variables xi: regress glucose iphysact iphysact _Iphysact_1-5 (naturally coded; _Iphysact_1 omitted) Source SS df MS Number of obs = F( 4, 2758) = 1651 Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = glucose Coef Std Err t P> t [95% Conf Interval] _Iphysact_ _Iphysact_ _Iphysact_ _Iphysact_ _cons Sol_statlab1docx revised 3/2/2014 Page 9 of 10

10 ** Multiple predictor model with both BMI and phsyact xi: regress glucose BMI iphysact iphysact _Iphysact_1-5 (naturally coded; _Iphysact_1 omitted) Source SS df MS Number of obs = F( 5, 2752) = 4925 Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = 3536 glucose Coef Std Err t P> t [95% Conf Interval] BMI _Iphysact_ _Iphysact_ _Iphysact_ _Iphysact_ _cons ** Partial F test of BMI controlling for physact: 1 df Partial F testparm BMI ( 1) BMI = 0 F( 1, 2752) = Prob > F = ** Partial F test of physical activity controlling for BMI: 4 df Partial F testparm _Iphysact* ( 1) _Iphysact_2 = 0 ( 2) _Iphysact_3 = 0 ( 3) _Iphysact_4 = 0 ( 4) _Iphysact_5 = 0 F( 4, 2752) = 584 Prob > F = Sol_statlab1docx revised 3/2/2014 Page 10 of 10