Module 2 : Traffic Measurement Procedures

Lecture 06 : Measurement over a Short Section

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	Data Analysis Standard Error of the mean The means of different sample taken from the same population are distributed normally about the true mean of population with a standard deviation, is known as standard error. $$S_e = \frac{\sigma_s}{\sqrt n}$$ (1) Sample Size Generally, sample sizes of 50 to 200 vehicles are taken. In that case, standard error of mean is usually under the acceptable limit. If precision is prior then minimum no. of sample should be taken, that can be measured by using the following equation. $$n_r = \frac{Z^2 \sigma_s^2}{S_e^2}$$ (2) where, $n_r$ is the no. of sample required, $\sigma_s$ is the Standard deviation, Z is value calculated from Standard Normal distribution Table for a particular confidence level (i.e. for 95% confidence Z=1.96 and for 99.7% confidence Z=3.0) and Se is the permissible (acceptable) error in mean calculation. Precision and Confidence Intervals Confidence intervals express the range within which a result for the whole population would occur for a particular proportion of times an experiment or test was repeated among a sample of the population. Confidence interval is a standard way of articulate the statistical accuracy of an experiment based assessment. If assess has a high error level, the equivalent confidence interval will be ample, and the less confidence we can have that the experiment results depict the situation among the whole population. When quoting confidence It is common to refer to the some confidence interval around an experiment assessment or test result. So, the confidence interval for estimated true mean speed can be calculated by $$\mu = v_t \pm Z\sigma_s$$ (3) where, $\mu$ is the confidence interval, $v_t$ is mean speed, $\sigma_s$ is standard deviation and Z is constant for specified confidence.