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              <text>This study examines the impact of the Pradhan Mantri MUDRA Yojana (PMMY) on enterprise creation in India, addressing a gap in the literature that emphasises loan trends rather than causal outcomes. Using panel data from 33 states and union territories between 2011–2021, fixed-effects regressions estimate the relationship between per-capita PMMY disbursements and MSME registrations. The results show a marked rise in MSME formation after the scheme’s launch in 2015, with per-capita lending associated with nearly one additional MSME per 1,000 people in the following year. This relationship remains robust after controlling for income, banking infrastructure and national shocks, though significant variation exists across states. Wealthier states display stronger impacts, while poorer or infrastructure-constrained regions benefit less. Interviews with beneficiaries support these patterns, indicating that although PMMY improves credit access, sustained enterprise growth also depends on market access, financial literacy and broader institutional support. </text>
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              <text>This project aims to explore the relation between horror and humour, and how parody functions within the Scary Movie films through analysing the used "methods of parodic coding" (Harries 39).</text>
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              <text>Air pollution is a growing concern in urban environments as it can heavily impact public health, especially in relation to respiratory diseases. This has led to a need for real-time monitoring of pollution levels to quickly identify any significant deviations and inform authorities, allowing for necessary interventions. Here, we propose a dynamic monitoring system that uses Kalman filters and multivariate statistical methods to track pollution trends and seasonality. By analysing the residuals of the model with non- parametric methods, we can detect changes in pollution levels and predict future readings. This gives rise to the anticipation of dangerous pollutant levels, enabling us to forewarn authorities. The approach is demonstrated using data from Athens, focusing on key pollutants such as ozone (O3), nitrogen dioxide (NO2), nitrogen oxide (NO), carbon monoxide (CO) and sulphur dioxide (SO2).</text>
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              <text>Bayesian Statistics dynamic linear models </text>
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