Normal deviate

Normal deviate

Normal Deviate

In statistics, the distance of a data point from the average value of the data set divided by standard deviation.
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The sample size was 100 cases and was calculated using WHO calculator9 (n= Z2 PQ/ d2, where n = desired sample size, Z = standard normal deviate, corresponding to 95% confidence level, P = proportion in the target population estimated to have a particular characteristic, Q =1-P = proportion in the target population not having the particular characteristics) and d= degree of accuracy required, Z2 =3.8416, P=7% (0.07)9, Q=1-P, d2=0.0025).
(Sample size was calculated using the formula n=Z2pq/d2 where n=sample size, Z=standard normal deviate at 95% confidence level=1.96, p=prevalence of the factor under study, 84% (0.84) from a previous study, q= complementary factor for q=1- p, N=average number of targeted population (i.e.
The division of distance of one data point from its mean to the standard deviation of the distribution is known as normal deviate or the standardized value.
This will generate a standard normal deviate centered on 0 with a range of -6 to 6.
We calculated the GG by converting fructosamine into a standardized normal deviate by subtracting the mean fructosamine from each value and dividing the result by the SD of fructosamine (4).
Statistical analysis was done according to method described by Snedecor and Cochran (1994) by using normal deviate test.
Then, we can use the normal deviate Z calculated as the difference of z and to, the transform of the hypothesized coefficient, divided by the standard error of z to test the null hypothesis.
where n = desired sample size, z = standard normal deviate corresponding to 95% confidence level, p = vertical transmission rate of intraocular pressure (IOP) i.e 3%, q = 1 - p and d = degree of accuracy (0.05 or 95%).
To generate the data, the structural equations (2.1) were transformed to the reduced form, error terms for sample sizes of fifteen, twenty-five and forty were produced by a random normal deviate generator and values for the endogenous variables were calculated.
The large sample normal deviate transformation of chi-square proposed by Fisher and Yates (1963) formed the basis of the Z-variance test.
culicifacies was significantly higher (P<0.05) during rainy season (when it was compared by normal deviate 'Z' test) than those in winter (Z=4.33) and summer (Z=7,08) probably due to increasing number of temporary breeding sites and it was also higher (P<0.05) in winter than that in summer (Z=3.4) (Table I) because winter is the post-monsoon season and retains some of the temporary habitats.
[z.sub.1 - [Alpha]/2] = Normal Deviate for Significance Level ([Alpha] = 0.05, 2-sided)