At any given combination of values , the assumptions for the multiple regression model require that the population of potential error term values hasa. a constant variance.b. a normal distribution.c. a mean equal to zero.d. All three of the assumptions are required. At any given combination of values , the assumptions for the multiple regression model require that the population of potential error term values hasO a constant variance.O a normal distribution,O a mean equal to zero.O All three of the assumptions are required.

Name: At any given combination of values , the assumptions for the multiple
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Description: At any given combination of values , the assumptions for the multiple regression model require that the population of potential error term values hasa. a constant variance.b. a normal distribution.c. a mean equal to zero.d. All three of the assumptio

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MATLAB: An Introduction with Applications

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ISBN:9781119256830

Author:Amos Gilat

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At any given combination of values , the assumptions for the multiple regression model require that the population of potential error term values has

a. a constant variance.

b. a normal distribution.

c. a mean equal to zero.

d. All three of the assumptions are required.

At any given combination of values , the assumptions for the multiple regression model require that the population of potential error term values has
O a constant variance.
O a normal distribution,
O a mean equal to zero.
O All three of the assumptions are required.

Features Features Normal distribution is characterized by two parameters, mean (µ) and standard deviation (σ). When graphed, the mean represents the center of the bell curve and the graph is perfectly symmetric about the center. The mean, median, and mode are all equal for a normal distribution. The standard deviation measures the data's spread from the center. The higher the standard deviation, the more the data is spread out and the flatter the bell curve looks. Variance is another commonly used measure of the spread of the distribution and is equal to the square of the standard deviation.

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