This study investigated coffeemaker search interest in the United States using the monthlytime series data from Google Trends. The forecasting model developed can be utilized as a part of the coffeemaker market research since accurately forecasting user interest would enable whoever is intrigued to anticipate future developments and make informed decisions. To analyze the underlying pattern, the data was decomposed with STL into seasonal, trend, and residual components. We observed a consistent annual seasonality with a surge in interest every November and December. This pattern was attributed to the increase in user interest during the end-of-the-year holiday season sales. Anomaly detection using the STL residuals found two anomalies. The anomaly witnessed in December 2020 is best understood as the result of the demand surge during the holiday season compounded by the adoption of online shopping imposed by the COVID-19 lockdown. For the model selection process, ACF and PACF plots were used to make the initial judgments on the parameters of the time series model. The first round of model selection tested potential AR and MA orders. The second round of model selection tested potential seasonal AR and MA orders. SARIMA(0, 1, 2)×(1, 0, 1)12 is the final model, chosen based on AIC and BIC scores. This model was able to capture the annual seasonal pattern and meet the stationary assumption with first-order differencing. The model has a MAPE of 4.3% and a RMSE of 3.841 with the rolling forecast origin prediction on the out-sample set. The residuals were confirmed to be white noise, which indicates the SARIMA model is a good fit for predicting the monthly coffeemaker search interest in the United States.

This thesis primarily employs text mining techniques to analyze the novel series ”Remem- brance of Earth’s Past” by Liu Cixin, with the aim of extracting information and develop- ing a comprehensive understanding through this process. It involves extracting the most frequently occurring words, calculating tf-idf (term frequency-inverse document frequency) scores, validating Zipf’s Law, generating bi-grams, conducting sentiment analysis and apply- ing topic modeling. The findings reveal significant keywords from the novel series, allowing readers to infer the main elements. While this analysis can help the audience achieve an overall understanding of the plots, reading the original work is encouraged to have a detailed sense of the entire series.

This thesis examines the roles of luck and skill in Texas Hold’em poker through theoretical analysis, statistical modeling, and empirical data from millions of hands. The study evaluates their influence across game stages—pre-flop, flop, turn, river, and show- down—highlighting how skill mitigates randomness as the game progresses.Findings reveal that while luck dominates early stages, skill increasingly impacts out- comes by enabling strategic and psychological decision-making. Quantitative models measure stage-specific contributions of luck and skill, showing that skill drives consistent long-term performance, whereas luck introduces short-term variability. Visual analyses further emphasize their complementary roles. The research provides actionable insights for optimizing poker strategies, managing risks, and improving long-term performance, while contributing to the debate on whether poker is a game of luck or skill. Future work may explore advanced modeling techniques and broader applications in decision-making under uncertainty.

This paper investigates whether the distribution of SARS-CoV-2 (COVID-19) transmission times can be reliably estimated using only case count data, employing the Hawkes model as the analytical framework. Hawkes point processes, widely recognized for modeling and analyzing time-to-event data, offer a robust approach to understanding transmission dynamics. This study fits the Hawkes model with varying productivity levels to case count data from all 50 U.S. states. Transmission time density is estimated using nonparametric methods and normal approximations. The findings indicate that, for most states, the mean transmission time is approximately 7 days, with a standard deviation of about 1 day (Science, 2020). These estimates are compared across states and with prior reports, revealing slightly shorter average transmission durations and reduced variability in this study. Furthermore, the results highlight that the virus can be transmitted as early as the first day of contact, emphasizing its potential for rapid spread (World Health Organization, 2020). As derived from this analysis, a deeper understanding of SARS-CoV-2 transmission dynamics carries significant implications for public health modeling and policy-making (Pan et al., 2020).