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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.
In cases of high multicollinearity it is not possible to assess the individual significance of one or more partial regression coefficient true or false or uncertain?
The given statement is true. Reason: High multicollinearity can make it difficult to determine the individual significance of predictors in a model.
A zero population correlation coefficient between a pair of random variables means that there is no linear relationship between the random variables True or false?
True , it would have been false only if it was mentioned no relationship . But as it mentions linear it is true.
If the coefficient of determination for a data set containing 12 points is 0.5 6 of the data points must lie on the regression line for the data set.?
That is not true. It is possible for a data set to have a coefficient of determination to be 0.5 and none of the points to lies on the regression line.
What is sample regression function?
To take a simple case, let's suppose you have a set of pairs (x1, y1), (x2, y2), ... (xn, yn). You have obtained these by choosing the x values and then observing the corresponding y values experimentally. This set of pairs would be called a sample.For whatever reason, you assume that the y's and related to the x's by some function f(.), whose parameters are, say, a1, a2, ... . In far the most frequent case, the y's will be assumed to be a simple linear function of the x's: y = f(x) = a + bx.Since you have observed the y's experimentally they will almost always be subject to some error. Therefore, you apply some statistical method for obtaining an estimate of f(.) using the sample of pairs that you have.This estimate can be called the sample regression function. (The theoretical or 'true' function f(.) would simply be called the regression function, because it does not depend on the sample.)
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.
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
Is it true if you log the all values and make regression linear?
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.
What do you mean if a linear model underestimates?
In graph form, the linear equation lies below the true line or curve.
In software testing regression testing must consist of fixed set of test to create a base line is it true or false?
Regression are classified as - Full / Complete Regression -- Entire application is regressed - Regional regression -- Tests performed around defect fixes or code changes
In cases of high multicollinearity it is not possible to assess the individual significance of one or more partial regression coefficient true or false or uncertain?
The given statement is true. Reason: High multicollinearity can make it difficult to determine the individual significance of predictors in a model.
Multiple regression analysis examines the relationship of several dependent variables on the independent variable?
True.
What is true about a point?
It has no linear dimensions.
When formulating a linear programming model on a spreadsheet the measure of performance is located in the target cell?
Yes, in a linear programming model on a spreadsheet, the measure of performance is typically located in the target cell, which is often the cell that you are trying to either maximize or minimize by changing the decision variables. The goal is to optimize the measure of performance by finding the best values for the decision variables based on the constraints of the model.
