Tag Archives: statistics

Arrhenius-Type Relationship of Viscosity as a Function of Temperature for Mustard and Cotton Seed Oils (Published)

The knowledge and evaluation of transport behaviors of fluids are very important in heat and mass flow. In this study, we adopted a statistical technique for regression analysis and statistical correlation tests. An equation modeling the relationship between the two parameters of viscosity Arrhenius-type equation, such as the Arrhenius energy (Ea) or the pre-exponential factor (A) was used. In addition, we introduce two other parameters; the Arrhenius temperature (T) and Arrhenius activation temperature (T*) to enrich the discussion. The viscosity data from two vegetable oils at different temperature ranges gives excellent statistical results. In addition, the model in this case is very useful for engineering data and permits the estimation of one non-available parameter when the other is available. The Activation energy Ea, Entropic (pre-exponential) factor A, Arrhenius temperature TA and the Arrhenius activation temperature for the mustard oil were observed to be 374.37381 J/mole, 12.39260595 cP, -17.89797783 oC, 45.051 oC respectively while Activation energy Ea, Entropic (pre-exponential) factor A, Arrhenius temperature TA and the Arrhenius activation temperature for the cotton seed oil are respectively 451.90611 J/mole, 8.210386507 cP, -25.8292961 oC, 54.381 oC . The coefficients of regressions (R2) for the graph of the natural log of viscosity versus reciprocal of temperature (Figures 2 and 4) for the mustard oil and cotton seed oil are 0.9996 and 0.9996 respectively. Since the correlation coefficient is the measure of how well a collection of data points can be modeled by a line, we can hence conclude that the natural log of the viscosity of both seed oil samples versus the inverse of their respective temperatures have a very good fit.

Keywords: Correlation, Model, Temperature, arrhenius parameters, statistics, vegetable oil, viscosity

Empirical Research in Education: Do Statistical Packages for Data Analysis Matter? (Published)

This paper is based on review of literature on the relevance of statistical packages in educational research. In this paper the importance of statistical packages for data analysis in educational research examined. The collective importance of statistical software packages is to help improve research expertise, make research work robust and faster, make research work easy and efficient, and minimize human error in data analysis. The paper clarifies the terms empirical research, and statistical data analysis. The statistical packages introduced in this paper include Microsoft Excel (MS-Excel), Minitab, Matlab, Stata, Statistical Package for the Social Sciences (SPSS), R, Statistical Analysis System (SAS),and Econometric Views (Eviews). The common features of statistical software as identified in this paper are that their basic programme are evolving, and they use both menus and/or command languages. The factors limiting the use of statistical packages among educational researchers include the perceived usefulness, statistical software self-efficacy, and statistics anxiety. Ways proposed in this paper to encourage educational researchers embrace the use statistical packages are as follows: Educational researchers should be mandated by the law governing their establishment to be research analyst. Choosing statistical software packages to learn should be based on suitability of the software. Mentorship in using statistical software package is necessary for learners in educational research.


Keywords: Data analysis, Empirical Research, statistical package., statistics

Examination of Hypotheses in Marketing Research (Published)

Statistical data is derived based on the survey of respondents, in the following three areas of the Georgian consumer market: product prices, tuition fees in higher education, the number of people wishing to travel to the parts of Georgia. Using this marketing information, the task of examination hypotheses about the unknown average values ​​of populations is solved.

Keywords: Hypothesis, Respondent, data, statistics

The Relevance and Significance of Correlation in Social Science Research (Published)

As important as statistics in the social sciences are, their application to real life situation has been minimal. Many scientific discoveries of great importance would have been impossible if scientists had only conceived of the world in terms of certainty. In many situations studied by scientists, and most certainly in all situations studied in social sciences, researchers can at best identify and measure imperfect associations between variables. Drawing largely from secondary sources, this paper examined the relevance and significance of correlation in social science. Findings showed that correlation is indispensable especially in studies that require the understanding of certainty and the degree to which variables show a mutual association.

Keywords: Correlation, Research, Variable, statistics


Tertiary Students’ studying statistics usually process statistical information in different ways depending on their programme of study. Teaching methodologies for transmitting statistical information to students also vary considerably depending on the type of programme being taught, a trade-off between the two must be sought for, it is therefore necessary to determine what is most likely to trigger each student’s concentration, and how to maintain it. The study examined the distribution of learning styles of accounting, statistics and engineering students among the four learning styles and its implication to teaching in higher institutions. Data for the study was collected using Solomon and Felder’s ILS questionnaire. Purposive sampling technique was used to select the respondents; the responses from each person’s questionnaire were entered into Felder’s self-scoring web based instrument. The output was further analyzed via SPSS version 17. The results showed that there were remarkable differences in the distribution of the programme of study to the learning styles. Majority of the students belonged to the active, visual, sensing and sequential learners. There was sufficient evidence to believe that differences existed among the active-reflective learners and program of study. The multiple comparisons method gave pair-wise significance among the active-reflective group of learners. The pair business and statistics was pair-wise significant (P = 0.016 < 0.05), the pair Business and Engineering learners was not significant (p = 0.197 > 0.05) finally, the pair Engineering-Statistics Learners was highly significant (p = 0.004< 0.05). For statistics to have practical relevance and provide the various categories of students with the opportunity to understand how the concepts can be applied in the world of work. It is highly recommended to lecturers to conduct need assessments to find the learning styles of their students and structure their teaching methods to satisfy the needs of the students.

Keywords: Higher Education, Teaching Methods, learning styles, statistics