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In the context of learning analytics, machine learning techniques have been commonly used in order to shed a light on solving educational problems. The studies can be associated with the curriculum design, mostly at course level, which target to improve the learning and teaching processes. In this study, the relationship between the courses was analyzed via artificial neural network (ANN) to provide a support for curriculum development process at a program level. Extracting the dependence among courses within a program plays a key role in placing them coherently to ensure the success of curriculum. For this purpose, it was investigated if the performance of students in a subsequent course is influenced from the performance in some previous basic courses. The results demonstrated that the performance relations could be used to describe information for prioritization and sequencing of courses within a program. In addition, ANN can successfully predict the student performances leading to find out the relationships between these courses. The application of the multilayer feedforward neural network resulted in an achievement of a prospering prediction performance based on the grades of prerequisite courses with 87% accuracy rate without sacrificing a significant sensitivity.

In recent years, software development has become more large-scaled, complicated, and diversified. At the same time, customer requirement of high quality and shortened delivery has increased. Therefore, we have to manage process quality and control product quality in the early-stage of software development in order to produce highly quality software products during the limited period. In this paper, we conduct multivariate linear analyses by using process monitoring data, derive effective process factors affecting the final product quality, and discuss the significant process factors with respect to software management measures of quality, cost, and delivery (QCD). Then, we discuss project management on the significant process factors affecting QCD and show its effect on QCD.

We extend the traditional RBF network to be a more powerful tool in terms of considering dependence among explanatory variables. For this purpose, we propose two kernel functions of RBF network, i.e., FGM-Gauss kernel and ρ-Gauss kernel based on a copula. A copula is another expression of a joint probability distribution function. After proposing the new models, we compare the regression performances between RBF network with the traditional Gauss kernel, FGM-Gauss kernel, ρ-Gauss kernel, and the multiple linear regression analysis by numerical experimentations. We show that new models have better regression performances than RBF network with Gauss kernel and multiple regression analysis if the explanatory variables depend on each other.

Prevention and control of influenza epidemics are major challenges for public health care services. Early identification of flu outbreak is an important step towards implementing effective disease interventions for reducing mortality and morbidity in human populations. Indeed, health officials need a real geo-making tool for monitoring and prediction. The aim of the current study is to discuss a novel spatiotemporal tool for monitoring and predicting the phenomenon of the seasonal influenza epidemic spread in the human population using multiple regression analysis. The suggested tool is mainly based on three sub-systems. It allows generating simulation data by the use of a simulation system, integrating data sources in a data warehouse (DW) system and performing a specific online analysis Spatial On-Line Analytical Processing (SOLAP). Our proposal enables also to illustrate evolution of disease through visualizations in time and space. It can examine social network effects to better understand the topological structure of social contact and the impact of its properties. A regression analysis is performed on the influenza epidemic to examine the main factors influencing flu incidence number and therefore to predict and track influenza epidemic.

For an organisation, it is noteworthy to engage employees, where the organisations look to their workers’ inventiveness, activities and are proactive with the solutions for the current requirements. Achieving employees’ engagement is similar to the new Blue Ocean leadership approach (BOL) that gives an entirely new system as worker’s points of view are considered in building a new leadership profile. The key objective of this research paper is to analyse the impact of BOL on professional-level employee engagement through Kim and Mauborgne’s BOL grid four-action framework variables, particularly in the private sector. To test the developed hypothesis, the researcher applied simple linear regression and multiple regression analysis methods. The result of this research study showed that all the four-action BOL grid variables are significant in employee engagement. Further, to develop the new leadership profile, the researcher also used multiple regression analysis to conduct a detailed analysis on the impact of the BOL approach through BOL grid four-action framework variables separately. From the outcomes of the evaluation, it is accomplished that the new leadership blue ocean methodology creates a higher influence on employee engagement via the detected acts along with activities in the new BOL grid since the Create variables have extremely higher significance with 87% ($R^2=0.87$ and $p<0.001$), the Eliminate variables have next-level higher significance with 84% (($R^2=0.84$ and $p<0.001$), followed by the Raise variables that have the next higher importance with 72% ($R^2=0.72$ and $p<0.001$) which displays the consequence of BOL grid (all four-action) variables on employee engagement. In view of impending private sectors in Oman Vision 2040, the results of this research could be an important pointer to be considered in Oman’s private sector development.

The following sections are included:

• INTRODUCTION

• POPULATION PARAMETERS AND THE REGRESSION MODELS
  • Data Description
  • Building the Population Regression Model
  • Sample Versus Population Regression Model

• THE LEAST-SQUARES ESTIMATION OF α AND β
  • Scatter Diagram
  • The Method of Least Squares
  • Estimation of Intercept and Slope

• STANDARD ASSUMPTIONS FOR LINEAR REGRESSION

• THE STANDARD ERROR OF ESTIMATE AND THE COEFFICIENT OF DETERMINATION
  • Variance Decomposition
  • Standard Error of Residuals (Estimate)
  • The Coefficient of Determination

• THE BIVARIATE NORMAL DISTRIBUTION AND CORRELATION ANALYSIS
  • The Sample Correlation Coefficient
  • The Relationship Between r and b
  • The Relationship Between r and R²

• Summary

• Appendix 13A Derivation of Normal Equations and Optimal Portfolio Weights

• Appendix 13B The Derivation of Equation 13.16

• Appendix 13C The Bivariate Normal Density Function
  • Using a Mathematics Aptitude Test to Predict Grade in Statistics

• Appendix 13D American Call Option and the Bivariate Normal CDF
  • Valuating American Option

• Questions and Problems

The following sections are included:

• INTRODUCTION

• THE MODEL AND ITS ASSUMPTIONS
  • The Multiple Regression Model
  • The Regression Plane for Two Explanatory Variables
  • Assumptions for the Multiple Regression Model

• ESTIMATING MULTIPLE REGRESSION PARAMETERS
  • Annual Salary, Years of Education, and Years of Work Experience

• THE RESIDUAL STANDARD ERROR AND THE COEFFICIENT OF DETERMINATION
  • The Residual Standard Error
  • The Coefficient of Determination

• TESTS ON SETS AND INDIVIDUAL REGRESSION COEFFICIENTS
  • Test on Sets of Regression Coefficients
  • Hypothesis Tests for Individual Regression Coefficients

• CONFIDENCE INTERVAL FOR THE MEAN RESPONSE AND PREDICTION INTERVAL FOR THE INDIVIDUAL RESPONSE
  • Point Estimates of the Mean and the Individual Responses
  • Interval Estimates of Forecasts

• BUSINESS AND ECONOMIC APPLICATIONS
  • Overall Job-Worth of Performance for Certain Army Jobs
  • The Relationship Between Individual Stock Rates of Return, Payout Ratio, and Market Rates of Return
  • Analyzing the Determination of Price per Share
  • Multiple Regression Approach to Evaluating Real Estate Property
  • Multiple Regression Approach to Doing Cost Analysis

• USING COMPUTER PROGRAMS TO DO MULTIPLE REGRESSION ANALYSES
  • SAS Program for Multiple Regression Analysis
  • MINITAB Program for Multiple Regression Prediction
  • Stepwise Regression Analysis

• Summary

• Appendix 15A Derivation of the Sampling Variance of the Least-Squares Slope Estimations

• Appendix 15B Derivation of Equation 15.30

• Questions and Problems