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When you use linear regression to model the data, there will typically be some amount of error between the predicted value as calculated from your model, and each data point. These differences are called "residuals". If those residuals appear to be essentially random noise (i.e. they resemble a normal (a.k.a. "Gaussian") distribution), then that offers support that your linear model is a good one for the data. However, if your errors are not normally distributed, then they are likely correlated in some way which indicates that your model is not adequately taking into consideration some factor in your data. It could mean that your data is non-linear and that linear regression is not the appropriate modeling technique.
What else can I help you with?
If a data batch is approximately normally distributed would it's normal probability plot would be S-Shaped?
yes
What is the probability of rolling a dice 7?
With a single throw of a normal die, the probability is 0.With a single throw of a normal die, the probability is 0.With a single throw of a normal die, the probability is 0.With a single throw of a normal die, the probability is 0.
What is the probability of drawing a seven of hearts?
The probability on a single random draw, from a normal deck of cards, is 1/52.The probability on a single random draw, from a normal deck of cards, is 1/52.The probability on a single random draw, from a normal deck of cards, is 1/52.The probability on a single random draw, from a normal deck of cards, is 1/52.
What requirements are necessary for a normal probability distibution to be a standard normal probability distribution?
The mean must be 0 and the variance must be 1.
Why if a probability distribution curve is bell shaped why is this a normal distribution?
A bell shaped probability distribution curve is NOT necessarily a normal distribution.
