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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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| Name | Grades | Rating | |||
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Not teaching in Spring 2026 | |||||
GISC 7361 Daniel Griffith | |||||
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Not teaching in Spring 2026 | |||||
GISC 7361 Daniel Griffith | |||||
Grades: 599
Median GPA: B+
Mean GPA: 3.119
2.2
Professor rating
4.3
Difficulty
8
Ratings given
17%
Would take again
Spatial Statistics
GISC 7361
School of Economic, Political and Policy Sciences
The application of statistical techniques to the explicit treatment of space (geography) in social science models. Covers indices of spatial autocorrelation, the specification of autoregressive models (Gaussian, Poisson, binomial/logistic), geostatistical modeling, spatial filtering, Bayesian map analysis, random effects in models, and imputation of missing geocoded data. Recommended: GISC 7360. 3 credit hours.
Prerequisite: GISC 7310 or EPPS 7316 or equivalent.
Offering Frequency: Based on student interest and instructor availability
This professor/course combination hasn't been taught in the semesters you selected. To see more grade data, try changing your filters.
Grades: 0
Median GPA: None
Mean GPA: None
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Grades: 599
Median GPA: B+
Mean GPA: 3.119
2.2
Professor rating
4.3
Difficulty
8
Ratings given
17%
Would take again
Spatial Statistics
GISC 7361
School of Economic, Political and Policy Sciences
The application of statistical techniques to the explicit treatment of space (geography) in social science models. Covers indices of spatial autocorrelation, the specification of autoregressive models (Gaussian, Poisson, binomial/logistic), geostatistical modeling, spatial filtering, Bayesian map analysis, random effects in models, and imputation of missing geocoded data. Recommended: GISC 7360. 3 credit hours.
Prerequisite: GISC 7310 or EPPS 7316 or equivalent.
Offering Frequency: Based on student interest and instructor availability
This professor/course combination hasn't been taught in the semesters you selected. To see more grade data, try changing your filters.
Grades: 0
Median GPA: None
Mean GPA: None
Click a checkbox to add something to compare.