A regression model has low bias and high variance. How can it be improved?
A regression model has low bias and high variance. How can it be improved?
1) You are reviewing a ridge regression model that your business partner is building. In the model, he has applied the shrinkage penalty to all terms (intercept included), leading to a massive reduction in variance. Is everything okay with this implementation? (Choose the MOST CORRECT answer.) a) Yes, the ridge regression model is implemented properly. b) No, there should be a massive reduction in bias (not variance). c) No, the shrinkage penalty term does not apply to the intercept. d)...
1.) What is the difference between a simple regression model and a multiple regression model? a.) There isn’t one. The two terms are equivalent b.) A simple regression model has a single predictor whereas a multiple regression model has potentially many c.) A simple regression model can handle only limited amounts of data whereas a multiple regression model can handle large data sets d.) A simple regression is appropriate for a dichotomous outcome variable, whereas a multiple regression model should...
The high−low method is theoretically better than regression analysis because the high−low method uses more data points than regression analysis. True False
Amazon’s Path to Becoming the Low-Cost Provider in E-Commerce How has Amazon improved value chain logistics to gain competitive advantage as the low-cost provider in E-Commerce?
Can the Solow model be improved by explicitly recognising a role for human and physical capital? Really struggling with how to write a conclusion for this.
Multiple Linear Regression - Omitted Variable bias. Can someone provide me with an intuitive explanation of ommitted variable bias.
If the input to our linear regression object is of 10 dimensions, including the bias, how many variables does our cost or total loss function contain?
how does projection bias violate the standard economic model?
Coin 1 has bias p1, coin 2 has bias p2, coin 3 has bias p3. All coin flips are independent. We choose one of the three coins at random (each coin equally likely). Then we toss n times. Let's say K is A RANDOM VARIABLE the indicates the number of heads. Can we approximate K as normal? If yes what is mean and variance in this case? Let's say we toss coin 1 n1 times, coin 2 n2 times and...
Suppose you estimate a multiple regression model using OLS and the coefficient of determination is very high (above 0.8), while none of the estimated coefficients are (individually) statistically different from zero at the 5-percent level of significance. The most likely reason for this result is: multicollinearity. spurious regression. omitted variable bias. serial correlation.