What is regression analysis and its types?
Regression is a technique used to model and analyze the relationships between variables and often times how they contribute and are related to producing a particular outcome together. A linear regression refers to a regression model that is completely made up of linear variables.
What is the difference between stochastic and Nonstochastic?
Stochastic effects have been defined as those for which the probability increases with dose, without a threshold. Nonstochastic effects are those for which incidence and severity depends on dose, but for which there is a threshold dose. These definitions suggest that the two types of effects are not related.
Can you infer parameters from a fixed regressor?
If we have fixed regressors, theoretically speaking, we can only infer certain parameters about k conditional distributions, y ∣ xi for i = 1, 2, …, k where each xi is not a random variable, or is fixed.
How is operant conditioning different from classical conditioning?
Sometimes, operant conditioning involves punishment. In all examples of operant conditioning, a target behavior is reinforced using consequences. The main difference between classical and operant conditioning is the way the behavior is conditioned.
Can a fixed regressor be generalized to a whole distribution?
The consequence is that fixed regressors cannot be generalized to the whole distribution. For example, if we only had x = 1, 2, 3, …, 99 in the sample as fixed regressors, we can not infer anything about 100 or 99.9, but stochastic regressors can.
Which is the best example of classical conditioning?
Classical conditioning is when a conditioned response is paired with a neutral stimulus. The most famous example of this is Pavlov’s dogs, where Ivan Pavlov trained dogs to salivate at the sound of a metronome. The metronome was a neutral stimulus, since the dogs previously had no reaction to it.