The purpose of this study is to determine students' academic difficulties in learning a statistics and probability course. The study sample consisted of 24 instructors (male and female) in the Humanities track in the Department of Basic Sciences in the Common First Year (CFY) at King Saud University (KSU).The results showed that students in the Humanities track faced several academic difficulties in learning statistics and probability and these difficulties were concentrated in the lessons of permutation, combination, probability and random variables. The results indicated that all the difficulties that instructors surveyed occurred at a high or very high level and that no single difficulty was located at the medium or lower levels. The results indicated that, from the point of view of the instructors, there were no statistically significant differences in the level of students' academic difficulties in learning statistics and probability related to the type of instructor (male or female), the instructor's experience in the CFY (less than five years, over or equal to five years), and specialization (statistics, mathematics). Keywords : Students' Difficulties, Mathematics' Difficulties, Statistics and Probability. DOI : 10.7176/JEP/10-9-05 Publication date :March 31 st 2019

The absence of geostatistical modeling of volumetric parameters of the long-discovered Nigerian heavy oil and bitumen deposits is responsible for the inconsistencies surrounding estimates of hydrocarbon-in-place contained therein. An exploratory data analysis (EDA) is a pre-cursor to such modeling. As part of EDA, this work presents the descriptive statistics and probability distributions of the volumetric parameters of a Nigerian heavy oil and bitumen deposit. Raw data from the existing works have been assembled into a database. Using basic principles, porosity have been computed, from the raw data, for several core samples retrieved from the two bituminous horizons in the deposit. The computed database has been partitioned into the two horizons, using depth-to-top and thickness data. Furthermore, this work has conducted detailed analyses and offers robust discussions on the descriptive statistics and probability distributions of the porosity, depth-to-top, and thickness databases. The statistics and distribution curves obtained are observed to exhibit good correlations with existing geologic, stratigraphic, and textural data. An hypothesis suggesting the two horizons belong to same geological population has been formulated and tested at field and well levels; with results affirming the hypothesis. The descriptive statistics and probability distributions obtained offer a significant understanding of the characteristics and features of the available data. In addition, the distributions now become prior information to which reservoir descriptions would be constrained, in the future conditional simulation stage of this work. The correlation of core data obtained here with the existing geologic, stratigraphic, and textural data would promote data integration in the characterization of this deposit.

In the first chapter the authors put into practice the following educational principles, reflecting their views of how these subjects should be taught: a focus on the most relevant ideas and postpone extensions to later stages; illustrating the complementary/dual nature of statistical and probabilistic reasoning; utilising the potential of technology and show its limits; and reflecting on the different levels of formalisation to meet the wide variety of students’ previous knowledge, abilities, and learning types.