Question

Multicollinearity cannot occur in a simple linear regression model.

True |

False |

Answer #1

Simple linear regression can be used to determine the fitted
model for an exponential regression function if we make the correct
logarithmic transformation on the exponential formula.
True or False

True or False: 1) A crucial assumption of the linear model is
that the sum of the residuals is 0
2)In the simple linear regression model if the R^2 is equal to
one, then the linear relationship between the variable is exact and
residuals are all zero

Which of the following are true in simple linear regression?
True or False for each
There is only one independent variable (X).
Y is the dependent variable.
The relationship between X and Y is described by a linear
function.
Changes in Y are assumed to be related to changes in X.
X is the independent variable because Y is dependent on X

What are the pitfalls of simple linear regression? True or False
for each
Lacking an awareness of the assumptions of least squares
regression.
Not knowing how to evaluate the assumptions of least squares
regressions.
Not knowing the alternatives to least squares regression if a
particular assumption is violated.
Using a regression model without knowledge of the subject
matter.
Extrapolating outside the relevant range of the X and Y
variables.
Concluding that a significant relationship identified always
reflects a cause-and-effect relationship.

In any regression model, p denotes the number of explanatory
variables in the model. In simple linear regression (SLR),
p=1. True/False?
When testing whether the slope of a explanatory variable is 0 or
not in context of multiple regression, what distribution is used to
determine the p-value? standard normal distribution / t
distribution with n−1 degrees of freedom / t distribution with n−2
degrees of freedom / t distribution with n−p−1 degrees of freedom
?
In multiple regression, there is...

An application of the simple linear regression model generated
the following results involving the F test of the overall
regression model: p = 0.0012, R2 = 0.67. The null hypothesis, which
states that none of the predictor variables are statistically
significantly related to the outcome variable, should be
rejected.
True
False

Linear Regression conception
the true regression model is Y=B0+B1X+esilon
However, the sample simple regression line is
Y_head=B0_head+B1_headX, and it doesn't have the error esilon, but
why .Please explain it step by step and with some proof to
support
***follow the comment***

True or False: In the simple regression model, both ordinary
least squares (OLS) and Method of Moments estimators produce
identical estimates. Explain.

Correlation and Regression True or False 3 Questions:
K). In simple linear regression predicting Y from X, the
unstandardized coefficient of the X variable will always equal the
Pearson r between X and Y. (Assume X and Y are not measured as z
scores).
L). In simple linear regression predicting Y from X, the
standardized coefficient of the X variable will always equal the
Pearson r between X and Y.
M). In multiple regression predicting Y from X, the standardized...

How is the slope calculated in a simple linear regression
model?

ADVERTISEMENT

Get Answers For Free

Most questions answered within 1 hours.

ADVERTISEMENT

asked 16 minutes ago

asked 16 minutes ago

asked 34 minutes ago

asked 36 minutes ago

asked 39 minutes ago

asked 51 minutes ago

asked 52 minutes ago

asked 53 minutes ago

asked 53 minutes ago

asked 1 hour ago

asked 1 hour ago

asked 1 hour ago