In this paper, we are interested in comparing the conventional t –test with the proposed t – test for testing equality of means with unequal and equal variances. Here, we proposed harmonic mean of variances as an alternative to the pooled sample variance when there is heterogeneity of variances. Two sets of secondary data were obtained from Agricultural Development Project (KWADP) and the Ministry of Agriculture in Ilorin, Kwara State to demonstrate the two test statistics used and the results show that the proposed t – test statistic is found to be appropriate than the conventional t – test statistic when we have unequal variances but the conventional t – test perform better when we have equal variances.
The study was carried out on hypotheses testing of variables of basic science teachers’ on academic performance students’ in Upper Basic Schools Kwara State, Nigeria. This target population for the study was all Basic Science Teachers in Kwara State, Nigeria, four hundred and sixty-nine (469) Public Upper Basic Schools. The researcher designed teachers' questionnaire and was administered to one hundred and thirty five (135) Basic science teachers that were selected from forty-five Upper Basic Schools in Kwara State. Researcher-designed validated questionnaire was used to extract data from the respondents on the teachers 'influence on the performance of students in Upper Basic Schools. Three research questions were raised with two hypotheses which were tested. Percentage, t-test statistics and ANOVA were used to analyze the facts collected. The finding showed that influence of Basic science teachers on the performance of students in Upper Basic Schools in Kwara State, Nigeria was significantly. It was also significant based on year of teaching experience and academic qualification of Basic science teachers. According to the findings, it is suggested that; the educational authorities and the school system should encourage the use of available resources by providing for them, the necessary materials that will influence Basic Science performance and enhance students learning. State government and proprietors of private secondary schools should organize seminars and workshops for science teachers on regular basis especially for the public schools’ science teachers. Professionally qualified Basic Science teachers should be allowed to teach Basic Science
It is a common practice in statistical analysis to draw conclusions based on significance. P-values often reflect the probability of incorrectly concluding that a null hypothesized model is true; they do not provide information about other types of error that are also important for interpreting statistical results. Standard model selection criteria and test procedures are often inappropriate for comparing models with different numbers of random effects, due to constraints on the parameter space of the variance components. In this paper, we focused on a minimum Bayes factor proposed by Held and Ott (2018) and applied it to a balanced two way analysis of variance (ANOVA) with random effects under three cases namely: Case 1: both factors are fixed; Case 2: both factors are random; Case 3: factor A is fixed and factor B is random. We realized that in all the three cases, considered the Bayes factor indicates weak evidence against the null hypothesis of zero variability in the effects of the levels of the factors as well as the interactions. This result is due to the conservative nature of the minimum Bayes factor.
This study centers on estimating parameters in a linear regression model in the presence of multicollinearity. Multicollinearity poses a threat to the efficiency of the Ordinary Least Squares (OLS) estimator. Some alternative estimators have been developed as remedial measures to the earlier mentioned problem. This study introduces a new unbiased modified two-parameter estimator based on prior information. Its properties are also considered; the new estimator was compared with other estimators’ Mean Square Error (MSE). A numerical example and Monte Carlo simulation were used to illustrate the performance of the new estimator.
This paper presents the simplified version of the Freeman-Tukey test statistic for testing hypothesis about multinomial probabilities in one, two and multidimensional contingency tables that does not require calculating the expected cell frequencies before test of significance. The simplified method established new criteria of collapsing cells whose frequency are less than 5. Illustrated examples compared favourably the new method with Pearson, Neyman and Likelihood ratio chi-squared statistics. Apart from being faster, the simplified version provides more accurate result since the problem of figure approximation is reduced.
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