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Predictor variables = independent variables = x-variables = input variables = Features = covariates.
- They explain changes in the response.
- You want to determine how changes in one or more predictors are associated with changes in the response.
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Response variables = dependent variables = y-variables = outcome variables.
- You want to determine whether changes in the predictors are associated with changes in the response.
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Example: A plant growth study:
- Predictors: are the amount of fertilizer applied, the soil moisture, and the amount of sunlight.
- Response variable is the amount of growth that occurs during the study.
- Goal: to determine how changes in the predictors are associated with changes in plant growth (the response).
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Variables: the variables that you have in dataset (predictor variables & Response variables)
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Observation: = data points: the values of those features (variables)
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Example:
| X | Y |
|---|---|
| 1 | 1 |
| 2 | 4 |
| 3 | 9 |
| 4 | 16 |
| 5 | 25 |
Here we have 2 features (variables): x and y , and 5 observations (data points): (1,1),(2,4),(3,9),(4,16),(5,25)
- Regression a set of statistical processes for estimating the relationships between a dependent variable (Y) and one or more independent variables (X).