Question

An equation may be nonlinear in the variables. However, linear regression analysis (OLS) can be applied...

An equation may be nonlinear in the variables. However, linear regression analysis (OLS) can be applied to a nonlinear equation if a certain condition is met. What is this special condition?

Homework Answers

Answer #1

OLS (Ordinary Least Squares) can be applied to any model or relationship as long as the equation is LINEAR IN PARAMETERS or Can be transformed such that the transformed model is Linear in Parameters. This is the special condition which needs to be met so as to apply Linear Regression Analysis to a nonlinear relationship between dependent and independent variable. The equation can be non linear in Variables but has to be linear in parameters or there should exist a transformation such that the transformed relation becomes linear in parameters.

Know the answer?
Your Answer:

Post as a guest

Your Name:

What's your source?

Earn Coins

Coins can be redeemed for fabulous gifts.

Not the answer you're looking for?
Ask your own homework help question
Similar Questions
Regression analysis involves lurking variables, outliers, scatterplots, linear correlation coefficient, and regression equation. Answer the following...
Regression analysis involves lurking variables, outliers, scatterplots, linear correlation coefficient, and regression equation. Answer the following questions for your main post: What types of data can be used to generate different kinds of graphs?
Regression analysis involves lurking variables, outliers, scatterplots, linear correlation coefficient, and regression equation. Answer the following...
Regression analysis involves lurking variables, outliers, scatterplots, linear correlation coefficient, and regression equation. Answer the following 2 questions for your main post: How have you used data to showcase an example or solve a problem? Did you get the outcome you expected? Explain.
Consider the simple linear regression model for which the population regression equation can be written in...
Consider the simple linear regression model for which the population regression equation can be written in conventional notation as: yi= Beta1(xi)+ Beta2(xi)(zi)2+ui Derive the Ordinary Least Squares estimator (OLS) of beta i.e(BETA)
Suppose that your linear regression model includes a constant term, so that in the linear regression...
Suppose that your linear regression model includes a constant term, so that in the linear regression model Y = Xβ + ε The matrix of explanatory variables X can be partitioned as follows: X = [i X1]. The OLS estimator of β can thus be partitioned accordingly into b’ = [b0 b1’], where b0 is the OLS estimator of the constant term and b1 is the OLS estimator of the slope coefficients. a) Use partitioned regression to derive formulas for...
Maxim runs an OLS regression where age, specified as a single linear variable, is one of...
Maxim runs an OLS regression where age, specified as a single linear variable, is one of the independent variables and is measured in years. Moyosore runs a regression that is the same except that she measures age in decades. What is the difference between the estimates of the coefficient on age for these two researchers?
Concept Questions We've discussed that a linear regression assumes the relationship between variables is linear: it...
Concept Questions We've discussed that a linear regression assumes the relationship between variables is linear: it forms a constant slope. But suppose the data is U-shaped or inverted U-shaped. How would you created a linear regression so the line would follow this data? (hint: think of what the equation for a U-shaped line looks like.) Suppose you applied a scalar to a variable. Then you used both the original variable and the scaled variable as explanatory variables. What would happen...
Please provide a sample scenario that can be analyzed with a simple linear regression analysis. Make...
Please provide a sample scenario that can be analyzed with a simple linear regression analysis. Make sure to clearly state your predictor and criterion variables.
Regression analysis procedures have as their primary purpose the development of an equation that can be...
Regression analysis procedures have as their primary purpose the development of an equation that can be used for predicting values on some DV for all members of a population. T F A secondary purpose is to use regression analysis as a means of explaining causal relationships among variables. T F In order to make predictions, three important facts about the regression line must be known. One of them is: The point at which the line crosses the X-axis. T F...
We are all too familiar with linear equations in two variables. These systems may have no...
We are all too familiar with linear equations in two variables. These systems may have no solution, one solution, or infinitely many. Of course, we can interpret these solutions geometrically as two parallel lines, two intersecting lines, or two identical lines in the plane. How does this extend into linear equations in three variables? If a linear equation in two variables describes a line, what does a linear equation in three variables describe? Give a geometric interpretation for the possible...
4. What is a linear Diophantine equation of two variables? How many solutions can such an...
4. What is a linear Diophantine equation of two variables? How many solutions can such an equation have? How can the solution(s) be found?
ADVERTISEMENT
Need Online Homework Help?

Get Answers For Free
Most questions answered within 1 hours.

Ask a Question
ADVERTISEMENT