Courses

  • Summer 2018

    All Terms Offered

    • Fall 2018
    • Fall 2019

    Official Description

    The application of regression methods to problems in agriculture and natural resources. Topics include simple linear, multiple linear, and nonlinear regression analysis and correlation analysis. Emphasis is placed on predictor variable selection, diagnostics and remedial measures and validation. Both quantitative and qualitative predictor variables are examined. The SAS system is used for all analyses. Course Information: Same as ANSC 541. Prerequisite: CPSC 440 or equivalent.

    TitleSectionCRNTypeHoursTimesDaysLocationInstructor
    This course if not available during the Summer 2018 semster
  • Fall 2018

    All Terms Offered

    • Fall 2018
    • Fall 2019

    Official Description

    The application of regression methods to problems in agriculture and natural resources. Topics include simple linear, multiple linear, and nonlinear regression analysis and correlation analysis. Emphasis is placed on predictor variable selection, diagnostics and remedial measures and validation. Both quantitative and qualitative predictor variables are examined. The SAS system is used for all analyses. Course Information: Same as ANSC 541. Prerequisite: CPSC 440 or equivalent.

    TitleSectionCRNTypeHoursTimesDaysLocationInstructor
    Regression AnalysisAB146816LAB003:00 PM - 04:50 PM F  M205 Turner Hall Carolyn J Butts-Wilmsmeyer
    Regression AnalysisAB246817LAB001:00 PM - 02:50 PM F  M205 Turner Hall Carolyn J Butts-Wilmsmeyer
    Regression AnalysisAL46809LEC502:00 PM - 03:50 PM M W  313 Mumford Hall Carolyn J Butts-Wilmsmeyer
  • Spring 2019

    All Terms Offered

    • Fall 2018
    • Fall 2019

    Official Description

    The application of regression methods to problems in agriculture and natural resources. Topics include simple linear, multiple linear, and nonlinear regression analysis and correlation analysis. Emphasis is placed on predictor variable selection, diagnostics and remedial measures and validation. Both quantitative and qualitative predictor variables are examined. The SAS system is used for all analyses. Course Information: Same as ANSC 541. Prerequisite: CPSC 440 or equivalent.

    TitleSectionCRNTypeHoursTimesDaysLocationInstructor
    This course if not available during the Spring 2019 semster