The major part of students at institutions of higher learning has shown an aversion for statistics. These attitudes impede on students’ performance. Among factors affecting students’ achievement in the subject is self-efficacy, self-concept, anxiety and low self-perception. In the main, this study sought to explore students’ perceptions and attitudes towards statistics. Data used was collected through SATS-36 and MPSP questionnaires from students who availed themselves for lectures of first year statistics and statistics related courses at a university in South Africa. The findings proved that students’ perceived academic and professional relevance of statistics relates to their statistics proficiency. Students with low statistics self-perception are bound to develop negative attitudes towards the subject. Interest, mathematics and statistics self-efficacy, enjoyment, worth, relevance and effort were identified as precursors of statistics course achievement.

In this paper, both Seasonal ARIMA and Holt-Winters models are developed to predict the monthly car sales in South Africa using data for the period of January 1994 to December 2013. The purpose of this study is to choose an optimal model suited for the sector. The three error metrics; mean absolute error, mean absolute percentage error and root mean square error were used in making such a choice. Upon realizing that the three forecast errors could not provide concrete basis to make conclusion, the power test was calculated for each model proving Holt-Winters to having about 0.3% more predictive power. Empirical results also indicate that Holt-Winters model produced more precise short-term seasonal forecasts. The findings also revealed a structural break in April 2009, implying that the car industry was significantly affected by the 2008 and 2009 US financial crisis.

The study assessed the reliability and the validity of student-lecturer

This study applies cointegration and error correction approaches to determine the effect of macroeconomic determinants on household debt in the United States of America. Cointegration analysis provides an effective framework used for estimating and modelling relationships from time series data. Short-run and long-run cointegration models explaining the relationships between the US household debt and related macroeconomic factors are estimated. The data used covers a period of 1990 Q1 to 2013 Q1 and is sourced from the electronic data delivery system of the OECD, USA Federal Housing Finance Agency and the USA Department of the Treasury among others. SAS 9.3 version was used to obtain the results. The sample and variables were meritorious according to KMO and Cronbach's alpha. Unit root test results provided enough evidence to conclude that the series were stationary after first differencing. Further data analysis was carried out with the first lag chosen by the AIC and SBC. Three cointegrating vectors were identified and were later standardised to correctly provide parameter estimates of the vector error correction model of household debts. The model revealed some short and long-run relationships. Revealed by the model is that 1.5 % of long-run equilibrium was corrected per quarter. The results of the current study are crucial to households and policy makers. Researchers may also refer to these results.

The purpose of this paper was to use the metric Multidimensional scaling (MDS) to explore the ten dire household debt determinants in the context of South Africa. Macroeconomic data used was collected from the South African reserve bank and Statistics South African websites for the first quarters of 1990 to 2013. SPSS 22 was used to execute the analysis. A Standardized Residuals Sum of Squares (STRESS 1) measure calculated as 0.00077confirmed the best fit of the MDS model and the Tucker's Coefficient of Congruence implied that 99.9% of variance in the model is accounted for by the two dimensions. This was also a confirmation that the ten selected determinants can better be represented in a two dimensional perpetual map. The findings revealed two profiles of household debts. Gross domestic product and house prices are associated with high levels of household debts. The remainder of the determinants is found to have low effects. MDS demonstrated its effectiveness in classifying household debt determinants according to their contribution. Also revealed is that an MDS is a useful tool to use in quantifying the ubiquitous, but slimy, notion of similarity.