Question 3: a) Based on the following two charts: Do you think the data violate

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Question 3: a) Based on the following two charts: Do you think the data violate the assumption of Variance Constancy? Yes No Explain why. Do you think the data violate the assumption of Normality? Yes No Explain why. Do you think the data violate the assumption of mean of 0? Yes No Explain why. Do you think the data violate the assumption of independence? Yes No Explain why. Ultra LTE 23:15 1 3296 Homework Homework-2.pdf =65.56, ,-84.96, n=45 1. 2. What is the equation of the simple linear regression? Given X-58, what is the estimated Y? Question 3: Based on the following two Residual Plot Normal Probability Plot x0.hi.to oo» soo.10000 020 100 120 Sample Percent Do you think the data violate the assumption of Variance Constancy? Explain why. Do you think the data violate the assumption of Normality? Explain why Do you think the data violate the assumption of mean of O? Explain why Do you think the data violate the assumption of independence? Explain why Yes No Yes No Yes No Yes No Question 4

Explanation / Answer

Q.3

(a) Do you think the data violates the assumption of variance consistency.

Answer : homoscedasticity means that when you plot the individual error against the predicted value, the variance of the error predicted value should be constant. So by seeing residual plot we can say that variance is not consistant here. So Yes, assuptions of variance consistency is violated.

(b) Do you think the data violates the assumption of normality.

Answer: Normality assupmtion is checked by the normal probability plot The normal probability plot was designed specifically to test for the assumption of normality. If your data comes from a normal distribution, the points on the graph will form a line. Here we can see that the plot is not forming a line, it is forming a curve.

So yes, it violates the assumption of normality.

(c)  Do you think the data violates the assumption of mean of 0.

Answer: assumption of mean of 0 means that mean of all residuals is equal to 0. That we can see from the plot is that it is true.

So, No the data doesn't violates the assumption of mean of 0.

(iv)  data violates the assumption of independence.

Answer:  the assumption of independence means no correlation between consecutive errors in the case of time series data or No autocorrelation of residuals. So by looking the data we doesn't see that any pattern in between residuals. SO, resiudals are indpendent here.

So, No, the data doesn't violates the assumption of independence

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