Question

True or False:

-Presence of correlation in error terms is known as Autocorrelation.

-Normality Q-Q Plot is used to determine the normal distribution of errors.

-Normality Q-Q Plot uses standardized values of residuals.

-The problem with r-squared is that it keeps on increasing as you increase the number of variables, regardless of the fact that the new variable is actually adding new information to the model.

-F Statistics value can range between zero and any arbitrary large number. Naturally, lower the F statistics, better the model.

-Std. Error - This determines the level of variability associated with the estimates. Smaller the standard error of an estimate is, more accurate will be the predictions.

-The adjusted r-squared implies that our model explains one hundred percentage of the total variance in the data.

-If a variable shows p value > 0.05, we can remove that variable from model since at p> 0.05, we'll always fail to reject null hypothesis.

Answer 1

-Presence of correlation in error terms is known as Autocorrelation.

FALSE-

Explanation: It is the same as calculating the correlation between two different time series, except autocorrelation uses the same time series twice: once in its original form and once lagged one or more time periods.

-Normality Q-Q Plot is used to determine the normal distribution of errors.

FALSE

Expalnation! : Quantile-quantile plots (also called q-q plots) are used to determine if two data sets come from populations with a common distribution.

-Normality Q-Q Plot uses standardized values of residuals.

TRUE-

Explanation: Normal Q-Q Plot is used to assess if your residuals are normally distributted.

-The problem with r-squared is that it keeps on increasing as you increase the number of variables, regardless of the fact that the new variable is actually adding new information to the model.

TRUE:

Explanation: When more variables are added, r-squared values typically increase. They can never decrease when adding a variable; and if the fit is not 100% perfect, then adding a variable that represents random data will increase the r-squared value with probability

F Statistics value can range between zero and any arbitrary large number. Naturally, lower the F statistics, better the model.

FALSE.

Explanation:

Its value can range between zero and any arbitrary large number. Naturally, higher the F statistics, better the model.

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