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Indirect standardization is the procedure of adjusting the crude mortality or morbidity rate for one or more variables by making use of a known reference population. It may, for instance, be needed to compare cancer mortality rates of single and married women with the adjustment being made for the likely different age distributions in two groups. Age-specific mortality rates in the reference population are applied individually to the age distributions of the two groups to obtain the probable number of deaths in each. These can then be summed with the observed number of deaths in the two groups to obtain comparable mortality rates.
The procedure in which initially the sample of subjects is selected for generating the auxillary information only, and then the second sample is selected in which the variable of i
The Null Hypothesis - H0: There is autocorrelation The Alternative Hypothesis - H1: There is no autocorrelation Rejection Criteria: Reject H0 (n-s)R 2 > = (1515 - 4) x (0.
Prior distributions : The probability distributions which summarize the information about a random variable or parameter known or supposed at a given time instant, prior to attaini
Poisson regression In case of Poisson regression we use ηi = g(µi) = log(µi) and a variance V ar(Yi) = φµi. The case φ = 1 corresponds to standard Poisson model. Poisson regre
This is acronym for the Epidemiological, Graphics, Estimation and Testing of the program developed for the analysis of the data from studies in epidemiology. It can be made in use
An oil company is considering whether or not to bid for an offshore drilling contract. If they bid, the value would be $600m with a 65% chance of gaining the contract. The company
Bimodal distribution : The probability distribution, or we can simply say the frequency distribution, with two modes. Figure 15 shows the example of each of them
Greenhouse geissercorrection is the method of adjusting the degrees of freedom of the within- subject F-tests in the analysis of the variance of longitudinal data so as to allow t
This is the powerful visualization tool for studying how the response relies on an explanatory variable given the values of other explanatory variables. The plot comprises of a num
Normality - Reasons for Screening Data Prior to analyzing multivariate normality, one should consider univariate normality Histogram, Normal Q-Qplot (values on x axis
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