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Articles 1 - 4 of 4
Full-Text Articles in Statistical Models
Modeling Growth And Stress Factors For Converted Silvopasture Systems In The Missouri Ozarks, Bailee N. Suedmeyer
Modeling Growth And Stress Factors For Converted Silvopasture Systems In The Missouri Ozarks, Bailee N. Suedmeyer
MSU Graduate Theses
Silvopasture systems are becoming increasingly popular among sustainable agriculture ranchers, due to the increase in knowledge of benefits to the cattle and ability to grow cool season grasses beneath the canopy. This project focuses on the forest crop aspect of silvopasture systems from monitoring of the health of the trees over time to recommendations for thinning management to keep it functioning as viable silvopasture. The study site consists of five acres of upland hardwood forest area in Southern Missouri with 18 monumented fixed area plots. Arial and ground data was collected at each plot throughout the growing season, along with …
A Geochemical And Statistical Investigation Of The Big Four Springs Region In Southern Missouri, Jordan Jasso Vega
A Geochemical And Statistical Investigation Of The Big Four Springs Region In Southern Missouri, Jordan Jasso Vega
MSU Graduate Theses
The Big Four Springs region hosts four major first-order magnitude springs in southern Missouri and northern Arkansas. These springs are Big Spring (Carter County, MO), Greer Spring (Oregon County, MO), Mammoth Spring (Fulton County, AR), and Hodgson Mill Spring (Ozark County, MO). Based on historic dye traces and hydrogeological investigations, these springs drain an area of approximately 1500 square miles and collectively discharge an average of 780 million gallons of water per day. The rocks from youngest to oldest that are found in Big Four Springs region are the Cotter and Jefferson City Dolomite (Ordovician), Roubidoux Formation (Ordovician), Gasconade Dolomite …
Seasonal Time Series Models With Application To Weather And Lake Level Data, Mengqing Qin
Seasonal Time Series Models With Application To Weather And Lake Level Data, Mengqing Qin
MSU Graduate Theses
This work studies seasonal time series models with application to lake level and weather data. The thesis includes related time series concepts, integrated autoregressive moving average models (abbreviated as ARIMA), parameter estimation, model diagnostics, and forecasting. The studied time series models are applied to the data of daily lake level in Beaver Lake (1988-2017) and the data of daily maximum temperature in New York Central Park (1870-2017). Due to seasonality of the data, three different approaches are proposed to the modeling: regression method, functional ARIMA method and multiplicative seasonal ARIMA method. The forecasted values of the year 2018 are compared …
Decision Trees And Their Application For Classification And Regression Problems, Obinna Chilezie Njoku
Decision Trees And Their Application For Classification And Regression Problems, Obinna Chilezie Njoku
MSU Graduate Theses
Tree methods are some of the best and most commonly used methods in the field of statistical learning. They are widely used in classification and regression modeling. This thesis introduces the concept and focuses more on decision trees such as Classification and Regression Trees (CART) used for classification and regression predictive modeling problems. We also introduced some ensemble methods such as bagging, random forest and boosting. These methods were introduced to improve the performance and accuracy of the models constructed by classification and regression tree models. This work also provides an in-depth understanding of how the CART models are constructed, …