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Non-response is the term generally used for the failure to give the relevant information being collected in the survey. Poor response can be because of the variety of causes, for instance, if the topic of the survey is of the intimate nature, respondents might not care to answer certain questions.
Since it is quite possible that the respondents in the survey differ in some of their characteristics from those which do not respond, a large number of non-respondents might introduce bias into the final results.
Glim is the software package specifically suited for fitting the generalized linear models (the acronym stands for the Generalized Linear Interactive Modelling), including the log
Assume that a population is normally distributed with a mean of 100 and a standard deviation of 15. Would it be unusual for the mean of a sample of 20 to be 115 or more?
Item-total correlation is an extensively used method for checking the homogeneity of the scale made up of number of items. It is simply the Pearson's product moment correlation c
This term is sometimes used for the data collected in those longitudinal studies in which more than the single response variable is recorded for each subject on each occasion. For
The distribution free or technique which is the analogue of the analysis of variance for the design with two factors. It can be applied to data sets which do not meet the assumptio
Outliers - Reasons for Screening Data Outliers are due to data entry errors, subject is not a member of the population that the sample is trying to represent, or the subject i
Hurdle Model: The model for count data which postulates two processes, one generating the zeros in the data and one generating positive values. The binomial model decides the bina
This term sometimes is applied to the model for explaining the differences found between naturally happening groups which are greater than those observed on some previous occasion;
i will like to submit my project for you to do on chi-square, ANOVA, and correlation and simple regression. how can we do this?
The Null Hypothesis - H0: There is no heteroscedasticity i.e. β 1 = 0 The Alternative Hypothesis - H1: There is heteroscedasticity i.e. β 1 0 Reject H0 if Q = ESS/2 >
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