Abstract
This paper proposes a non parametric method for two factor data analysis with unequal cell frequencies and interaction. Chi-square test statistic was developed for testing the null hypothesis of no treatment effect and interaction between factor A and factor B. The proposed methods are illustrated with some data and compared with the usual unweighted mean method. The result showed that the proposed method is more powerful than the method of unweighted mean.
Highlights
Analysis of variance (ANOVA) is generally regarded as the best analysis technique for balanced experiments that have equal number of subjects in each group that is cells with equal frequency [3]
Some classifications of missingness were given as missingness at random (MCAR) as a situation where the probability of missing data does not depend on observed or un observed data, missing at random (MAR) as the probability that the missing data does not depend on the observed data while missing not at random (MNAR), is the probability that the missing data depends on the unobserved data conditional on the observed data [4]
We shall use the data on final cumulative grade point average (FCGPA) of students who graduated in statistics from a certain University by State of origin for four years
Summary
Analysis of variance (ANOVA) is generally regarded as the best analysis technique for balanced experiments that have equal number of subjects in each group that is cells with equal frequency [3]. Two – way ANOVA with unequal cell frequencies without assumption of equal error variance was considered by taking generalized approach to finding p-values [5]. When the sample size per treatment combination is not the same for all treatments in a two factor ANOVA, the factor effect become more complicated and the usual calculations are no longer directly applicable [7], [9]. In this situation, the easiest and exact way to obtain the proper sum of squares for testing factor effects and interactions is through regression approach. We present an alternative nonparametric method that will take care of different factors and interaction effects
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Science Journal of Applied Mathematics and Statistics
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
