A hostel is a type of housing that provides shared living spaces for travelers and students, particularly offered by educational institutions where students share their living space. This study aims to evaluate the challenges faced by female students living in university hostels and the impact of these issues on their academic performance. Additionally, it identifies common problems such as satisfaction with hostel facilities, security, and how the hostel environment influences their lifestyle. The population for this study includes all female students residing in Peshawar University hostels. Information was obtained from the Provost's office of the University of Peshawar, showing a total of 1951 female students residing in various hostels during the 2015-16 session across all academic programs with an age range of twenty to twenty-five years. An open-ended questionnaire was developed for in-depth interviews, and various statistical tests were employed in this study. The chi-square test of independence was used to examine associations among different factors, revealing significant associations in most cases. The odds ratio was applied to assess the strength of these associations, uncovering a strong correlation between students' health and the type of food provided by the hostel mess. Additionally, a strong association was found between students' academic performance and group discussions. ANOVA was used to analyze the number of girls in the hostel rooms, leading to the rejection of the null hypothesis. To determine student satisfaction with hostel facilities, a logistic regression model was employed. The results highlighted significant factors such as the name of the hostel, education level, water issues, generator availability, toilet conditions, hostel environment, room accommodation, health facilities, mess condition, academic performance, TV availability, internet access, and good security. Consequently, the study's findings can help improve the quality of hostel services in Pakistan, potentially increasing students' satisfaction with hostel life.

Prediction is a complex task that is based on different statistical methods and scientific knowledge. Using statistical methods, the current study aims to forecast wheat output in the Pakistani province of Sindh. To forecast the wheat harvest, secondary data on acreage, output, and yields have been gathered over the past thirty years (1984–1985 to 2013–2014). The objectives of this work are to utilize moving averages (MA) and exponential smoothing (ES) consisting of two time series methods that indicate the time series variable (wheat) with less error and compare both methods. We apply the 3,5 and 7-year moving average techniques to check which of them is performing the best without losing the information. In the second technique, we use the exponential smoothing methods to evaluate and forecast the time series variable. The models are contrasted with one another and chosen using the lowest Key Performance indicators. The analysis is conducted on R software (3.4.1) and Minitab. Based on the results, it is found that exponential smoothing performed better than the moving average. Moreover, using these methods, it is observed that the study variable follows an upward-increasing trend.

Financial development in emerging economies, such as Pakistan, often leads to disparities in wealth, causing long-term financial and social instabilities. Dealing with these imbalances requires a holistic approach that incorporates financial expansion and equal distribution of wealth. This study assesses the relationship between earnings and expenses within multiple districts in Khyber Pakhtunkhwa, Pakistan, using a multilevel regression model to gain a localized understanding of wealth disparities and consumption patterns. The Tehsil Municipal Administration in Peshawar and the Bureau of Statistics in Khyber Pakhtunkhwa analyzed data from 24 districts throughout 2012/13, 2014/15, and 2018/19. The investigation seeks to assess the performance of multilevel modeling compared to classic regression techniques and analyze expenditure patterns among districts through multilevel models. Based on the outcomes, the multilevel model outperforms standard regression models and provides additional proof of districts' influence on expenditures and earnings interactions. The outcomes emphasize the crucial importance of incorporating hierarchical data frameworks when investigating economic interactions, as they demonstrate substantial variability within each person and district level. The multilevel analysis underscores the complexities of income-expenditure dynamics in Khyber Pakhtunkhwa and demonstrates the limitations of single-level models in adequately capturing this complexity.