48%00 Sat 4:56 PM a learn.unm.edu fraction you must convert it to a decimal. Mak

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48%00 Sat 4:56 PM a learn.unm.edu fraction you must convert it to a decimal. Make sure to give at least as many decimal places as the question asks for f you have any questions about the quiz, post to the Help Discussion. You can also send a course message to Brian. Make sure to describe your attemptís) to answor the question and the issue you are having Multiple This test allows multiple attempts. Attempts Force This test can be saved and resumed later. Completion Question Completion Status: > Moving to another question will save this response. Question 7 of 10 Question 7 1 points Save Answer regression? ( Which of the following are required assumptions for calculating inferential statistics of a that apply) The errors of prediction are distributed normally. Homogeneity of variances: the variance of X and Y must be the same Linearity. The relationship between the two variables is linear. Heteroscedasticity: The variance around the regression line must increase as X increases. All the points must be on the regression line. Homoscedasticity: The variance around the regression line is the same for all values of X Moving to another question wil save this response. Question 7 of 10 Notes Comments

Explanation / Answer

Ans:

These three are valid assumptions:

1)The errors of perdiction are normally distributed.(multivariate normality)

2)Linearity

3)Homoscedasticity:The variance around the regression is same for all values of X

Explanations:

Assumptions for Regression:

Multivariate Normality—Multiple regression assumes that the residuals are normally distributed.

Homoscedasticity—This assumption states that the variance of error terms are similar across the values of the independent variables. A plot of standardized residuals versus predicted values can show whether points are equally distributed across all values of the independent variables.

linear regression needs the relationship between the independent and dependent variables to be linear.

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