Advanced Statistics and Analysis using R Blog

 4/24/25 Problem Description: Florida is one of the world’s premier tourism destinations, attracting well over 100 million visitors annually. Yet visitor counts vary dramatically by season—peaking in winter months when travelers escape colder climates, and again in summer when families take vacations between school sessions. This volatility poses operational and strategic challenges for a wide range of stakeholders, from hotel and theme-park operators to transportation agencies and local governments. In an increasingly digital world, consumers signal their travel intentions earlier—often via online search engines. Google Trends data therefore offers a real-time, high-frequency proxy for consumer interest in “Florida vacation” and “beach vacation.” This project seeks to harness those digital search signals to build a timelier, more responsive forecasting model of weekly visitor volumes in Florida. By demonstrating that search interest—especially in “beach” vacations—correlates stron...

4/10/25  The table below represents charges for a student credit card.   a. Construct a time series plot using R. b. Employ E xponential Smoothing Model  as outlined in  Avril Voghlan Links to an external site. 's notes and report the statistical outcome c.  Provide a discussion on time series and Exponential Smoothing Model results that you obtained .   Month 2012 2013 Jan 31.9 39.4 Feb 27 36.2 March 31.3 40.5 Apr 31 44.6 May 39.4 46.8 Jun 40.7 44.7 Jul 42.3 52.2 Aug 49.5 54 Sep 45 48.8 Oct 50 55.8 Nov 50.9 58.7 Dec 58.5 63.4 A.  The time series plot illustrates a steady increase in student credit card charges from January to December for both 2012 and 2013, with 2013 consistently showing higher monthly charges than 2012. This suggests a year-over-year rise in credit card usage among students. Notably, the most significant increases occurred in February and July, while December recorded the highest charges in both years—$58.5 in 2012 and $63.4 in...

4/3/25  As we can see with the results above, the additive linear model demonstrated a strong overall fit, with an R² value of 0.7566, indicating that approximately 75.7% of the variance in VAS scores is explained by the model. The adjusted R², which accounts for the number of predictors, was lower at 0.4969, reflecting the complexity of the model and its many subject-level terms. The F-statistic yielded a p-value of 0.022, confirming that the model is statistically significant overall. Notably, the treatment effect was substantial and negative, suggesting that the active drug significantly reduced pain levels. There was also considerable variation across subjects, which is expected in a crossover design. Although the period effect was dropped due to multicollinearity, the model still effectively captured the key treatment effect and subject differences. This data contains additive effects on subjects, period and treatment. Compare the results with those with those obtained from t ...