Nov 28, 2022  
OHIO University Undergraduate Catalog 2021-22 
    
OHIO University Undergraduate Catalog 2021-22 [Archived Catalog]

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ECON 2890 - Economic Data Analysis with Excel and SAS


The course teaches students how to use Excel and SAS software packages to analyze economic data. Students learn how to locate real-world data from various online sources. They also learn how to use statistical software to analyze the data using basic statistical and econometric methods. Students also work on empirical projects in Excel and SAS to investigate important policy issues that face societies.

Requisites: No credit if taken after ECON 4890
Credit Hours: 3
OHIO BRICKS Arch: Constructed World
Repeat/Retake Information: May be retaken two times excluding withdrawals, but only last course taken counts.
Lecture/Lab Hours: 3.0 lecture
Grades: Eligible Grades: A-F,WP,WF,WN,FN,AU,I
Course Transferability: OTM course: TMMSL Transfer Module Mathematics, Statistics and Logic
College Credit Plus: Level 1
Learning Outcomes:
  • Students will be able to describe economic issues that face societies.
  • Students will be able to locate economic data from sources on the internet and from online databases provided by the OU Alden library.
  • Students will be able to use two statistical software packages Excel and SAS to analyze the data using both informal and formal methods.
  • Students will be able to explain economic data presented in graphs and tables that they obtain from online sources such as the Bureau of Economic Analysis and Federal Reserve Board.
  • Students will be able to use statistical software to convert quantitative data into graphs and tables.
  • Students will be able to calculate new data variables such as real variables, per capita variables and growth rate variables using mathematical formulas.
  • Students will be able to use statistical software to obtain descriptive statistics and develop basic statistical inference of the data.
  • Students will be able to use statistical software to conduct basic regression estimation analysis of the data.
  • Students will be able to make and evaluate important assumptions that underlie estimation, modeling, and data analysis.
  • Students will be able to discuss the limitations of their assumptions and recognize the limits of their data analysis.
  • Students will be able to formulate meaningful hypotheses and test these hypotheses using empirical data.
  • Students will be able to interpret results and state conclusions for their hypotheses based on the significance of results.
  • Students will be able to express empirical evidence in support of their hypotheses using written reports that include graphs, tables, and words.
  • Students will be able to discuss policy implications of their findings, if relevant.



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