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Statistical exploration of dataset examining key indicators influencing housing and urban infrastructure investments in megacities

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      Abstract

      Lagos, by the UN standards, has attained the megacity status, with the attendant challenges of living up to that titanic position; regrettably it struggles with its present stock of housing and infrastructural facilities to match its new status. Based on a survey of construction professionals’ perception residing within the state, a questionnaire instrument was used to gather the dataset. The statistical exploration contains dataset on the state of housing and urban infrastructural deficit, key indicators spurring the investment by government to upturn the deficit and improvement mechanisms to tackle the infrastructural dearth. Descriptive statistics and inferential statistics were used to present the dataset. The dataset when analyzed can be useful for policy makers, local and international governments, world funding bodies, researchers and infrastructural investors.

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      Random number datasets generated from statistical analysis of randomly sampled GSM recharge cards

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        Survey datasets on the externalizing behaviors of primary school pupils and secondary school students in some selected schools in Ogun State, Nigeria

        This data article contains the partial analysis (descriptive statistics) of data obtained from 1770 primary school pupils and secondary school students in three Local Government Areas of Ogun State, Nigeria. The schools are either privately owned or public (government owned) schools. The aim of the field survey is to measure the level and patterns of externalizing behavior of the respondents. The data was collected using a standardized questionnaire. The questionnaire is a modification of Achenbach manual for Child behavior checklist (Achenbach, 2001) [1] and manual for Youth self-report (Achenbach and Rescorla, 2001) [2]. The questionnaire was designed to suit the demographic and socio-cultural nature of the target population. Analysis of the data can provide useful insights to the patterns of externalizing behavior of primary school pupils and secondary school students.
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          Learning analytics for smart campus: Data on academic performances of engineering undergraduates in Nigerian private university

          Empirical measurement, monitoring, analysis, and reporting of learning outcomes in higher institutions of developing countries may lead to sustainable education in the region. In this data article, data about the academic performances of undergraduates that studied engineering programs at Covenant University, Nigeria are presented and analyzed. A total population sample of 1841 undergraduates that studied Chemical Engineering (CHE), Civil Engineering (CVE), Computer Engineering (CEN), Electrical and Electronics Engineering (EEE), Information and Communication Engineering (ICE), Mechanical Engineering (MEE), and Petroleum Engineering (PET) within the year range of 2002–2014 are randomly selected. For the five-year study period of engineering program, Grade Point Average (GPA) and its cumulative value of each of the sample were obtained from the Department of Student Records and Academic Affairs. In order to encourage evidence-based research in learning analytics, detailed datasets are made publicly available in a Microsoft Excel spreadsheet file attached to this article. Descriptive statistics and frequency distributions of the academic performance data are presented in tables and graphs for easy data interpretations. In addition, one-way Analysis of Variance (ANOVA) and multiple comparison post-hoc tests are performed to determine whether the variations in the academic performances are significant across the seven engineering programs. The data provided in this article will assist the global educational research community and regional policy makers to understand and optimize the learning environment towards the realization of smart campuses and sustainable education.
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            Author and article information

            Affiliations
            [a ]Department of Building Technology, Covenant University, Nigeria
            [b ]Department of Architecture, Covenant University, Nigeria
            [c ]Department of Economics and Development Studies, Covenant University, Nigeria
            Author notes
            Contributors
            Journal
            Data Brief
            Data Brief
            Data in Brief
            Elsevier
            2352-3409
            02 May 2018
            June 2018
            02 May 2018
            : 18
            : 1725-1733
            5998746
            S2352-3409(18)30442-6
            10.1016/j.dib.2018.04.089
            © 2018 The Authors

            This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

            Categories
            Construction and Architecture

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