BIOSTATISTICS CASE STUDY: Tests of Association for Categorical Data Table 1 Characteristics of 399 patients with PTB and univariate analyses of potential predictors of successful treatment Successful Characteristics Patients treatment n (%) n(96) Pvalue* Sex 0.392 Male 293 (73.4) 106 (26.6) 198 (67.6) 77 (72.6) Female Patients with comorbidities 0.018 No comorbidity With comorbidities Unknown 143 (35.9) 255 (64.1) 109 (76.2) 165 (64.7) 1 (100) CXR 0.015 Cavitations No cavitation Unknown 127 (31.9) 271 (68.1) 98 (77.2) 176 (64.9) 1 (100) Previous TB treatment 0.815 Yes No Unknown 22 (5.8) 16 (72.7) 360 (94.2) 246 (68.3) 12 (70.6) 17 DOT 0.002 Yes No Unknown 250 (63.1) 186 (74.4 3 (100) 146 (36.9) 86 (58.9) Diagnostic and treating physicians <0.001 298 (74.7) 221 (74.2) Pulmonologist Non-pulmonologist 102 (25.6) 55 (53.9) Health-care institutions <0.001 105 (26.3) 68 (64.8) Medical centre Regional hospital District hospital and 144 (36.1) 98 (24.6) 399 (100) *15-month follow-up after start of treatment. 92 (63.9) 66 (67.3) Chest specialty hospital52 (13.0) 49 (94.2) primary practitioners Total 275 (68.9) tx2 test. PTB pulmonary tuberculosis; CXR chest X-ray, TB tuberculosis; DOT- directly observed treatment. Page i ot 4

A random sample of 100 m&m's produced 17 green m&m's.  What is the point estimate for the population proportion of green m&m's?  Construct the 95% confidence interval for the true proportion of green m&m's

Use the data on student load Student Unit Load 3, 12, 6, 9, 6, 12, 15, 12, 7, 9, 3   What is the point estimate for mean student unit load? Construct the 90% confidence interval for mean student load.  Assume all requirements have been met.

Why are confidence intervals important and what do they mean to a study?