QUESTION 4
In Multivariate Linear Regression, adding more independent variables might cause the adjusted R squared to fall in some cases
True
False
TRUE, In multivariate Linear regression , adding more independent variables might cause the adjusted R squared to fall in some cases. WE can see it by formulalae given below :
R2( adjusted) = 1 - (1-R2) *[n-1]/[n-(k+1)], n= sample size and k = no of independent variables
clearly changing no. of idependent variables (k) might cause fall in adjusted R2.
QUESTION 4 In Multivariate Linear Regression, adding more independent variables might cause the adjusted R squared...
Help with some data science questions Q.1 The linear regression model assumes multivariate normality, no or little multicollinearity, no auto-correlation, and homoscedasticity? Which assumption is missing from this list? (no more than 10 words) Q.2 The coefficient of correlation measures the percent change in the feature variables explained by the target variables. a) True b) False Q.3 In a linear regression model, the coefficient measures the change in Y explained by one unit-change in X. a) True b) False Q4....
1. In multivariate regression: a) More than one independent variable is used to predict a single dependent variable b) The value of r gives you the slope c) More than one dependent variable is predicted by a single independent variable d) More regressions are necessary
Simple Linear Regression Problem
Simple Linear Regression
Problem
QUESTION 4 SUMMARY OUTPUT Regression Statistics Multiple R Squared Adjusted Rsq Standard Error Observations 0.90 0.80 0.79 82.06 19.00 ANOVA MS 467247.5 6733.3 df Regression Residual Total 467247.5 114466.2 581713.7 17 Intercept Age Coefficients St Error 756.26 10.27 30.41 1.23 t Stat 24.87 -8.33 This output was obtained from data on the age of houses (in years) and the associated amount paid in rates (S). Predict the rates paid (in dollars correct...
2 pts Question 4 In the classical regression model we maximize the sum of the squared errors. O True False 2 pts D Question 5 The terms coefficients of determination and R-square are synonyms, measuring how well a regression model fits the data. O True False 2 pts Question 6 Student's t-statistic is calculated as the ratio of an estimated coefficient divided by its standard error. True False
Question 8 0.8 pts True or False: Adding explanatory variables that do not have a significant effect on the dependent variable to our model will lower the R-squared. O True O False
Regression and Forecastng (L) Question What does the R-squared measure for the following linear regression: Y- b0+ b2* XI + b3 * X2? A. It measures the variation around the predicted regression equation. B. It measures the proportion of variation in Y explained by XI and X2. C. It measures the proportion of variation in Y that is explained by X1 holding X2 constant. D. It will have the same sign as bl E. It measures the significance of bo...
Which statement is not correct? Multiple Choice R-squared is a measure of the degree of variability in the dependent variable about its sample mean explained by the regression line. The adjusted R-squared measure should be used in the case of more than one independent variable. The null hypothesis that R2 = 0 can be tested using the F-statistic. Forecasters should always select independent variables on the basis of R2. All of the options are correct.
Question 7 2 pts Multiple regression is the process of using several independent variables to predict a number of dependent variables. O True False
TRUE OR FALSE: We cannot avoid multicollinearity in a multiple regression as the independent variables are always correlated with each other to some extent? Perfect multicollinearity means independent variables are - perfectly correlated - positively correlated - highly correlated - not correlated Near multicollinearity means independent variables are - perfectly correlated - positively correlated - highly correlated - not correlated