2. SD 142.53162.5136 145816593 147.515167.542 1504517016 152.590172.56 1551551752 157.5194M=160 160 (M)195SD=5

3. SD

4. NORMAL DISTRIBUTION Range, mean±1SD=160±5=155 to 165cm –68.27% of the observations Range, mean±2SD=160±2x5=150 to 170cm –95.45% of the observations Range, mean±3SD=160±3x5=145 to 175cm –99.5% of the observations 3 observations +3 SD fall in 0.05% group.

5. RELATIVE VARIATE ( Z ) Deviation from the mean in a normal distribution or curve is called relative or standard normal deviate. It is measured in terms of SD & it tells us how much an observation is higher or smaller than mean in terms of SD. Z=observation-mean=X-X¯ SD SD

6. RANGE Easy to understand Easy to calculate Useful as a rough measure of variation Value may be greatly changed by an extreme value Highly unstable measure of variation.

7. MEAN DEVIATION Simple to understand and interpret. Affected by the value of every observation Less affected by absolute variation Not suited for any mathematical treatment.

8. SD Affected by value of every observation It avoids algebraic fallacy Less affected by fluctuations of sampling than other measures of dispersion Has a definite mathematical meaning Has a great practical utility in sampling and statistical inferences.

9. QUESTION Average weight of baby at birth is 3.05Kg with SD of 0.39Kg. In a normal distribution a) wt. of 4 Kg as abnormal? b)wt. of 2.5 Kg as normal?

10. SAMPLING Not possible to include each & every member Not possible to examine all people of country To test efficacy of drug to all patients Cooking of rice Costly collection & Time consuming Blood test

11. POPULATION Population Sample Parameter: a value calculated from a population –Mean (μ) –Standard Deviation(σ) Sample –Mean (X) –Standard deviation ( s)

12. SAMPLING Sample is a part of population Estimation of population parameters To test the hypothesis about the population from which the sample was drawn. Inferences are applied to the whole population but generalization are valid if sample size is sufficiently large & must be representative of the population-unbiased.

13. SAMPLING Sampling units are break down of population into smaller parts which are distinct and non overlapping so that each member / element of the population belongs to one and only one sampling unit. When a list of all individuals, households, schools and industries are drawn, it is called sampling frame.

14. Sample A representative sample is the one with which we can draw valid inference regarding the population parameters. It is representative of the population under study Is large enough but not too large The selected elements must be properly approached, included and interviewed.

15. Sample size L= 2 σ √n √n= 2 σ L n= 4 σ² L² Example: 1.mean pulse rate=70 Pop. Standard deviation(σ)=8 beats Calculate sample size? 2. Mean SBP=120,SD=10, calculate n?

16. Sample size Qualitative data N=4pq L² e.g.

17. SAMPLING TECHNIQUES SIMPLE RANDOM SAMPLING SYSTEMATIC SAMPLING STRATIFIED SAMPLING MULTISTAGE SAMPLING CLUSTER SAMPLING MULTIPHASE SAMPLING CONVENIENT SAMPLING QUOTA SAMPLING SNOW BALL SAMPLING