0
Is it true if you log the all values and make regression linear?
Wiki User
∙ 14y ago
Your question is a bit hard to understand, but I'll do my best. Sometimes taking the log of your independent variable will improve a linear fit. If you have two sets of data, X and Y, and they don't seem to fit a linear relationship, you may take the log of X, and the log of X may fit a linear relationship. Example: Suppose your data correctly fits the model y = a Xm. So plotting Y and X*, where X* is the log of X, and performing a linear regression, you obtain a slope and intercept. Your intercept is log(a). If you are using log base 10, then a (in the model) = 10intercept value and m is the slope of the semi-log line.
Wiki User
∙ 14y ago
Add your answer:
Earn +20 pts
True of false linear regression question. The need for possible transformation in regressor variables can be aided by observing the leverage, or partial regression plots?
true
Is it true to do linear regression there must be paired scores on two variables?
Yes.
True or false a linear regression is useful for modeling the position of an object in free fall?
true, liner regression is useful for modeling the position of an object in free fall
What is true about the y-intercept in the linear regression model?
The value depends on the slope of the line.
Is it true that the y-intercept in the linear regression model is always 0?
It could be any value
What is the purpose of a residual analysis in simple linear regression?
One of the main reasons for doing so is to check that the assumptions of the errors being independent and identically distributed is true. If that is not the case then the simple linear regression is not an appropriate model.
What is the solution of a system of linear equations in two variables?
The solution of a system of linear equations is a pair of values that make both of the equations true.
What does it mean for a system of linear equations to have no solutions?
It means that there is no set of values for the variables such that all the linear equations are simultaneously true.
When you use your regression line to predict values that are not in the sample and discovered large residual values in each prediction what can you say about your regression line?
There are several possible explanations: Leaving aside the two most obvious reasons: calculation error and attempted extrapolation, there are the following possibilities: The true relationship is non-linear. A relevant variable has been missed omitted. The observations are very variable: leading to a very large residual error. There is not enough variation in the independent (or predictive) variable so that Sxx is very small.
What are you trying to find when solving a system of linear equations?
You are trying to find a set of values such that, if those values are substituted for the variables, every equation in the system is true.
A value or values that make an equation true?
a solution
What are the values of a varible that make an equation true?
Solution set
