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Missing values: The observations missing from the set of data for some of the reason. In longitudinal studies, for instance, they might occur because subjects drop out of the study completely or do not appear for one or other of scheduled visits or because of the equipment failure. The common causes of subjects prematurely ceasing to participate include the recovery, lack of improvement, the unwanted signs or symptoms that might be related to the investigational treatment, unlikeable study procedures and the intercurrent health problems. Such values greatly complicate number of methods of analysis and simply using those individuals for whom data are complete can be unsatisfactory in number of situations. A distinction can be made between the values missing completely at random (MCAR), missing at random (MAR) and the non-ignorable (or informative).
The MCAR variety arise when the individuals drop out of study in a process which is independent of the observed measurements and those that would have been available had they not been missing both; here the observed values effectively constitute the simple random sample of the values for all study subjects. Random drop-out (MAR) happens when the dropout process depends on the outcomes which have been observed in the past, but given this information is conditionally independent of all future (which is unrecorded) values of the outcome variable following the drop-out. At last, in the case of informative drop-out, the drop-out process depends upon the unobserved values of the result variable. It is the latter which cause most the problems for the analysis of data comprising missing values.
1) Let N1(t) and N2(t) be independent Poisson processes with rates, ?1 and ?2, respectively. Let N (t) = N1(t) + N2(t). a) What is the distribution of the time till the next epoch
The Null Hypothesis - H0: There is no first order autocorrelation The Alternative Hypothesis - H1: There is first order autocorrelation Durbin-Watson statistic = 1.98307
A term commonly encountered in the application of the agglomerative hierarchical clustering techniques, where it refers to the 'tree-like' diagram illustrating the series of steps
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A study not involving the passing of time. All information is collected at the same time and subjects are contacted only once. Many surveys are of this type. The temporal sequence
The GRE has a combined verbal and quantitative mean of 1000 and a standard deviation of 200.
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The equation linking the height and weight of the children between the ages of 5 and 13 and given as follows here w is the mean weight in kilograms and h the mean height in
The Null Hypothesis - H0: β 1 = 0 i.e. there is homoscedasticity errors and no heteroscedasticity exists The Alternative Hypothesis - H1: β 1 ≠ 0 i.e. there is no homoscedasti
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