{"id":{"repo_id":"regina","oai_identifier":"oai:uregina.scholaris.ca:10294/9344"},"canonical_url":"https://search.dev.ndltd.org/etd/regina/oai:uregina.scholaris.ca:10294/9344","repository":{"repo_id":"regina","name":"University of Regina","base_url":"https://uregina.scholaris.ca/server/oai/request"},"display":{"title":"Multiple Linear Model Selection: A Data Study on the Ecological Controls on Methylmercury Production","abstract":"Data on methylmercury (MeHg) concentrations and other environment parameters was collected between 2006 to 2012 from wetlands in the prairie pothole region in Saskatchewan by members of the Department of Biology, University of Regina. My research goal was to use the Backward Elimination model selection to develop the best fitted linear regression model to test the impact of seven environment variables on methylmercury concentration. Before analysis, I cleaned the whole data set and removed all missing values and transformed some variables such as methylmercury, sulfate, and conductivity. I compared linear regression model with two-way interactions and no interaction linear model. After applying the linear model selection, I used several methods to check the model including Residual vs. Fitted Values, Normal Q-Q plots, compare AIC; after I checked for outliers in the combined data set with final model. The results of this study indicate that temperature (Temp), dissolved organic carbon (DOC), specific ultra-violet absorbance (SUVA), logarithm transformation of conductivity (Cond) and sulfate (SO_4) impacted methylmercury concentrations in our model. The best fitted model I selected was log (MeHg) = β_0+ β_1Temp + β_2log (Cond) + β_3log (SO_4) + β_4DOC + β_5SUVA + ε","abstract_html":"Data on methylmercury (MeHg) concentrations and other environment parameters was collected between 2006 to 2012 from wetlands in the prairie pothole region in Saskatchewan by members of the Department of Biology, University of Regina. My research goal was to use the Backward Elimination model selection to develop the best fitted linear regression model to test the impact of seven environment variables on methylmercury concentration. Before analysis, I cleaned the whole data set and removed all missing values and transformed some variables such as methylmercury, sulfate, and conductivity. I compared linear regression model with two-way interactions and no interaction linear model. After applying the linear model selection, I used several methods to check the model including Residual vs. Fitted Values, Normal Q-Q plots, compare AIC; after I checked for outliers in the combined data set with final model. The results of this study indicate that temperature (Temp), dissolved organic carbon (DOC), specific ultra-violet absorbance (SUVA), logarithm transformation of conductivity (Cond) and sulfate (SO_4) impacted methylmercury concentrations in our model. The best fitted model I selected was log (MeHg) = β_0+ β_1Temp + β_2log (Cond) + β_3log (SO_4) + β_4DOC + β_5SUVA + ε","abstract_has_math":false,"creators":["Liu, Sichen"],"institution":"Faculty of Graduate Studies and Research, University of Regina","degree_name":"Master of Science (MSc)","degree_level":"Master's","degree_discipline":"Statistics","degree_department":null,"school":null,"contributors":[],"advisors":["Volodin, Andrei","Hall, Britt"],"committee_chairs":[],"committee_members":["Deng, DianLiang"],"year":2020,"date_issued":"2020-04","date_published":"2020-04","updated_at":"2026-07-24T04:03:34Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.82465/4232"],"render_values":[{"text":"https://doi.org/10.82465/4232","href":"https://doi.org/10.82465/4232","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10294/9344","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Volodin, Andrei","Hall, Britt"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Deng, DianLiang"]},{"key":"dc:creator","label":"Author","values":["Liu, Sichen"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2020-12-13T16:42:25Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2020-12-13T16:42:25Z"]},{"key":"dc:date.issued","label":"Date","values":["2020-04"]},{"key":"dc:publisher","label":"Institution","values":["Faculty of Graduate Studies and Research, University of Regina"]},{"key":"dc:type","label":"Dc Type","values":["master thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Statistics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Master's"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MSc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Faculty of Graduate Studies and Research, University of Regina"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.82465/4232"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10294/9344"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["A Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Science in Statistics, University of Regina. x, 94 p."]},{"key":"dc:description.abstract","label":"Abstract","values":["Data on methylmercury (MeHg) concentrations and other environment parameters was collected between 2006 to 2012 from wetlands in the prairie pothole region in Saskatchewan by members of the Department of Biology, University of Regina. My research goal was to use the Backward Elimination model selection to develop the best fitted linear regression model to test the impact of seven environment variables on methylmercury concentration. Before analysis, I cleaned the whole data set and removed all missing values and transformed some variables such as methylmercury, sulfate, and conductivity. I compared linear regression model with two-way interactions and no interaction linear model. After applying the linear model selection, I used several methods to check the model including Residual vs. Fitted Values, Normal Q-Q plots, compare AIC; after I checked for outliers in the combined data set with final model. The results of this study indicate that temperature (Temp), dissolved organic carbon (DOC), specific ultra-violet absorbance (SUVA), logarithm transformation of conductivity (Cond) and sulfate (SO_4) impacted methylmercury concentrations in our model. The best fitted model I selected was log (MeHg) = β_0+ β_1Temp + β_2log (Cond) + β_3log (SO_4) + β_4DOC + β_5SUVA + ε"]},{"key":"dc:title","label":"Title","values":["Multiple Linear Model Selection: A Data Study on the Ecological Controls on Methylmercury Production"]}]}],"canonical_facts":{"dc:contributor.advisor":["Volodin, Andrei","Hall, Britt"],"dc:contributor.committeemember":["Deng, DianLiang"],"dc:creator":["Liu, Sichen"],"dc:date.accessioned":["2020-12-13T16:42:25Z"],"dc:date.available":["2020-12-13T16:42:25Z"],"dc:date.issued":["2020-04"],"dc:description":["A Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Science in Statistics, University of Regina. x, 94 p."],"dc:description.abstract":["Data on methylmercury (MeHg) concentrations and other environment parameters was collected between 2006 to 2012 from wetlands in the prairie pothole region in Saskatchewan by members of the Department of Biology, University of Regina. My research goal was to use the Backward Elimination model selection to develop the best fitted linear regression model to test the impact of seven environment variables on methylmercury concentration. Before analysis, I cleaned the whole data set and removed all missing values and transformed some variables such as methylmercury, sulfate, and conductivity. I compared linear regression model with two-way interactions and no interaction linear model. After applying the linear model selection, I used several methods to check the model including Residual vs. Fitted Values, Normal Q-Q plots, compare AIC; after I checked for outliers in the combined data set with final model. The results of this study indicate that temperature (Temp), dissolved organic carbon (DOC), specific ultra-violet absorbance (SUVA), logarithm transformation of conductivity (Cond) and sulfate (SO_4) impacted methylmercury concentrations in our model. The best fitted model I selected was log (MeHg) = β_0+ β_1Temp + β_2log (Cond) + β_3log (SO_4) + β_4DOC + β_5SUVA + ε"],"dc:identifier.doi":["https://doi.org/10.82465/4232"],"dc:identifier.uri":["https://hdl.handle.net/10294/9344"],"dc:language.iso":["en"],"dc:publisher":["Faculty of Graduate Studies and Research, University of Regina"],"dc:title":["Multiple Linear Model Selection: A Data Study on the Ecological Controls on Methylmercury Production"],"dc:type":["master thesis"],"thesis:degree_discipline":["Statistics"],"thesis:degree_level":["Master's"],"thesis:degree_name":["Master of Science (MSc)"],"thesis:institution_name":["Faculty of Graduate Studies and Research, University of Regina"]},"updated_at":"2026-07-24T04:03:34Z"}