Suppose you plotted a scatter plot between the residuals and predicted values in linear regression and you found that there is a relationship between them. Which of the following conclusion do you make about this situation?

Related MCQs

• Suppose you plotted a scatter plot between the residuals and predicted values in linear regression and you found that there is a relationship between them. Which of the following conclusion do you make about this situation?
• Suppose you plotted a scatter plot between the residuals and predicted values in linear regression and you found that there is a relationship between them. Which of the following conclusion do you make about this situation?
• Suppose, you got a situation where you find that your linear regression model is under fitting the data. In such situation which of the following options would you consider?
• Suppose, you got a situation where you find that your linear regression model is under fitting the data. In such situation which of the following options would you consider?
• Suppose, you got a situation where you find that your linear regression model is under fitting the data. In such situation which of the following options would you consider? 1. I will add more variables 2. I will start introducing polynomial degree variables 3. I will remove some variables
• Suppose, you got a situation where you find that your linear regression model is under fitting the data. In such situation which of the following options would you consider?1. I will add more variables2. I will start introducing polynomial degree variables3. I will remove some variables
• Suppose, you got a situation where you find that your linear regression model is under fitting the data. In such situation which of the following options would you consider?1. I will add more variables2. I will start introducing polynomial degree variables3. I will remove some variables
• What is/are true about ridge regression? 1. When lambda is 0, model works like linear regression model 2. When lambda is 0, model doesn't work like linear regression model 3. When lambda goes to infinity, we get very, very small coefficients approaching 0 4. When lambda goes to infinity, we get very, very large coefficients approaching infinity
• What is/are true about ridge regression? 1. When lambda is 0, model works like linear regression model 2. When lambda is 0, model doesn’t work like linear regression model 3. When lambda goes to infinity, we get very, very small coefficients approaching 0 4. When lambda goes to infinity, we get very, very large coefficients approaching infinity
• Which of the following assumptions do we make while deriving linear regression parameters? 1. The true relationship between dependent y and predictor x is linear 2. The model errors are statistically independent 3. The errors are normally distributed with a 0 mean and constant standard deviation 4. The predictor x is non-stochastic and is measured error-free