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5.0
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5.0
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1,000
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99%
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GISC 7310 Michael Tiefelsdorf | |||||
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GISC 7310 Michael Tiefelsdorf | |||||
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Grades: 368
Median GPA: B+
Mean GPA: 3.317
2.2
Professor rating
4.2
Difficulty
10
Ratings given
17%
Would take again
Advanced GIS Data Analysis
GISC 7310
School of Economic, Political and Policy Sciences
The specification, interpretation, and properties of the multiple linear regression model, including spatial and aspatial regression diagnostics, are examined. A detailed review of the key concepts of matrix algebra, optimization techniques, and simulation experiments is given. GIS and GPS data handling procedures are discussed from a regression and linear transformation perspective. Extensions to principal component analysis, ridge regression, weighted regression, logistic, and Poisson regression are provided. Practical data analysis for large Geo-referenced data sets are exercised. 3 credit hours.
Prerequisite: GISC 6301 or equivalent.
Offering Frequency: Spring
Grades: 10
Median GPA: B+
Mean GPA: 3.000
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Grades: 368
Median GPA: B+
Mean GPA: 3.317
2.2
Professor rating
4.2
Difficulty
10
Ratings given
17%
Would take again
Advanced GIS Data Analysis
GISC 7310
School of Economic, Political and Policy Sciences
The specification, interpretation, and properties of the multiple linear regression model, including spatial and aspatial regression diagnostics, are examined. A detailed review of the key concepts of matrix algebra, optimization techniques, and simulation experiments is given. GIS and GPS data handling procedures are discussed from a regression and linear transformation perspective. Extensions to principal component analysis, ridge regression, weighted regression, logistic, and Poisson regression are provided. Practical data analysis for large Geo-referenced data sets are exercised. 3 credit hours.
Prerequisite: GISC 6301 or equivalent.
Offering Frequency: Spring
Grades: 10
Median GPA: B+
Mean GPA: 3.000
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