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14 Cards in this Set
- Front
- Back
- 3rd side (hint)
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Linear regression assumptions |
•Predictors are error free •linearity of response to predictors •condtsnt variance within & for all predictors •independence of errors •lack of multi colinearity |
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Y=mx+b+e |
Y=response variable M=slope B= covariance E=error term |
Whats what |
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X, Y |
X covariate Y response variable |
What are they in models? |
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E |
Distance between model and data (synthetic or observed) |
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Approaches to modeling |
•Hypothesis testing •which is best model •data mining Last 2 require rigorous work |
3 |
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P value |
Probability that model is an accident |
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Model evaluation |
Parameter sensitivity Ground truthing Uncertainty within data and predictors Evaluate by looking at r^2 and spread |
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3 model components |
•trend (correlation) •random •auto correlated |
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1st law of geography |
Everything is related hut but nearer things are more closely related than further things |
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Statial model |
Abstraction of something special |
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Goals of models |
•robust •verifiable •simple |
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Modeling methods |
•Interpolation •Density •Correlation/regression |
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Density |
Finding an abundance of discrete occurances |
Plants, disease, crime |
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Attributes |
•continuous •dates •descriptive text |
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