In today's world of stiff competition in the labour market, coupled with advanced technology, industries require of students to have job experience before employing them. The challenge here is that the experience being required is not taught in the lecture rooms. The reality is that, it is only gained though hands on the job, thus real world confrontation popularly called industrial attachment a platform for students arm themselves with all the skill, knowledge and demanded experience. This article examines the industrial attachment programmes of polytechnics in Ghana and pertinent issues involved. The paper tackled the problem from four thematic areas; the preparation before the training starts, the perceived challenges encountered, the benefits derived from embarking on the training and suggested strategies to be employed to enhance the programme.

This study uses Time Series models to predict the annual traffic accidents in Ghana. The traffic accidents data spanning from January 1990 to December 2019 was used. The Box-Jenkins model building strategy was used. The Augmented Dickey Fuller (ADF) test showed that the accident data was stationary. Three ARMA models were suggested based on the ACF and PACF plots of the differenced series, these were ARMA (0,0), ARIMA (1,0), and ARMA (2,0). The model with the smallest corrected Akaike Information Criteria (AICs) and Bayesian Information Criteria (BIC) was chosen as the best model. The Ljung-Box statistics among others were used in assessing the quality of the model. ARMA (1,0) was the best model for the Ghana annual Traffic Accident data. The results showed that, from January to July, it would be difficult to make accurate estimates of the number of road incidents for the years leading up to 2020. This was due to the fact that the white noise process values were statistically independent at various times.

This research article aimed at modeling the variations in the dollar/cedi exchange rate. It examines the applicability of a range of ARCH/GARCH specifications for modeling volatility of the series. The variants considered include the ARMA, GARCH, IGARCH, EGARCH and M-GARCH specifications. The results show that the series was non stationary which resulted from the presence of a unit root in it. The ARMA (1, 1) was found to be the most suitable model for the conditional mean. From the Box–Ljung test statistics x-squared of 1476.338 with p value 0.00217 for squared returns and 16.918 with 0.0153 p values for squared residuals, the null hypothesis of no ARCH effect was rejected at 5% significance level indicating the presence of an ARCH effect in the series. ARMA (1, 1) + GARCH (1, 1) which has all parameters significant was found to be the most suitable model for the conditional mean with conditional variance, thus showing adequacy in describing the conditional mean with variance of the return series at 5% significant level. A 24 months forecast for the mean actual exchange rates and mean returns from January, 2013 to December, 2014 made also showed that the fitted model is appropriate for the data and a depreciating trend of the cedi against the dollar for forecasted period respectively.

The Sarima model is used in this study to forecast the monthly temperature in Ghana's northern region. The researchers used temperature data from January 1990 to December 2020. The temperature data was found to be stationary using the Augmented Dickey Fuller (ADF) test. The ACF and PACF plots proposed six SARIMA models: SARIMA (1,0,0) (1,0,0) (12), SARIMA (2,0,0) (1,0,0) (12), SARIMA (1,0,1) (1,0,0) (12), SARIMA (0,0,1) (1,0,0) (12), SARIMA (0,0,1) (0,0,1) (12), SARIMA (0,0,1) (0,0,1) (12). The best model was chosen based on the lowest Akaike Information Criteria (AICs) and Bayesian Information Criteria (BIC) values. The Ljung-Box data, among others, were used to determine the model's quality. All diagnostic tests are passed by the SARIMA (1,0,0) (1,0,0) (12) model. As a result, the SARIMA (1,0,0) (1,0,0) (12) is the best-fitting model for predicting monthly temperatures in Ghana's northern region.

The study examines whether there is an association between brand of mobile phone used and occupational status, educational level and satisfaction level. Influence of mobile phone usage was also examined. Data were collected on 478 mobile phone users. Convenient sampling method where the respondents were interviewed as they were encountered was used. Statistical analysis methods such as descriptive statistics, chi-square test statistic, one sample t-test, were applied. The study indicated that Samsung is the most used mobile phone brand. The study also revealed that the major factors that influence the choice of a brand of mobile phone are price, the number of features, battery capacity, quality and fashionableness. The study discovered that there is no association between mobile phone brand used and educational level. The study reveals that there is an association between brand of mobile phone used and satisfaction with performance. Conclusion can be drawn that majority of the respondents often used their mobile phone application and were satisfied with the attributes of their mobile phone.