Page 111 - Demo
P. 111
%u062c%u0645%u064a%u0639 %u0627%u0644%u062d%u0642%u0648%u0642 %u0645%u062d%u0641%u0648%u0638%u0629 %u0640 %u0627%u0625%u0644%u0639%u062a%u062f%u0627%u0621 %u0639%u0649%u0644 %u062d%u0642 %u0627%u0645%u0644%u0624%u0644%u0641 %u0628%u0627%u0644%u0646%u0633%u062e %u0623%u0648 %u0627%u0644%u0637%u0628%u0627%u0639%u0629 %u064a%u0639%u0631%u0636 %u0641%u0627%u0639%u0644%u0647 %u0644%u0644%u0645%u0633%u0627%u0626%u0644%u0629 %u0627%u0644%u0642%u0627%u0646%u0648%u0646%u064a%u0629111Symmetric, Significance level, e.g., sig = 0.05 or 5% means M1 & M2 are significantly different for 95% of population. Confidence limit, z = sig/2. Estimating Confidence Intervals: Statistical Significance: 1-Compute t. Select significance level (e.g. sig = 5%), 2-Consult table for tdistribution: Find t value corresponding to k-1 degrees of freedom (here, 9) , 3-tdistribution is symmetric: typically upper percentage points of distribution shown %u2192 look up value for confidence limit z=sig/2 (here, 0.025), 4- If t > z or t < -z, then t value lies in rejection region: Reject null hypothesis that mean error rates of M1 & M2 are same, 5- Conclude:
