Question

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

Answer #1

Which of the following statements concerning regression and
correlation analysis is/are true?
A. If the correlation coefficient is zero, then there is no linear
relationship between the two variables.
B. A negative value for the correlation coefficient indicates that
high values of the independent variable are correlated with low
values of the dependent variable.
C. The slope coefficient for a simple linear regression model
measures the expected change in the independent variable for a unit
change in the dependent variable....

In simple linear regression analysis which of the following
assumptions must be met in order to ensure valid conclusions?
* The variances for each X are independent of each other.
* the variance of X is equal to the variance of Y
* All of these statements are true.
* The X variable and the Y variable each have histograms that
are normal
* X is linearly related to Y

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...

True or False: Assuming a linear relationship between X and Y,
if the coefficient of correlation (r) equals 0.50, this means that
50% of the variation in the dependent variable (Y) is due to
changes in the independent variable (X).

True or False: Assuming a linear relationship between X
and Y, if the coefficient of correlation (r)
equals 0.50, this means that 50% of the variation in the dependent
variable (Y) is due to changes in the independent variable (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.

Which of the following is TRUE in simple linear regression?
A.
A residual represents the difference between an observed Y value
and predicted X value for a given Y value.
B.
A residual is always greater than the observed Y value.
C.
A residual represent the difference between the average X value
and the average Y value.
D.
None of the above.

1. The dependent variable has a score in the case of logistic
regression. TRUE or FALSE and why?
2, If the probability of an even A is 0.2 and that of an event B
is 0.10 then the odds ratio is: a.1 b. 2.25 c. 2.0 d. 0.5
3. In the case of logistic regression, we estimate how much the
natural logarithm of the odds for Y =1 changes for a unit change in
X. TRUE or FALSE
4. In...

QUESTION 4
Determine if each of the following statements is True or False
with full justification explaining your reasoning.
In multiple regression, a large R square indicates all
independent variables will have a significant effect on the
dependent variable if the significance level is set to 5%.
(1 mark)
In multiple regression, if an you have an independent variable
with a large p-value (close to 1), this independent variable cannot
be used to predict the dependent variable.
(1...

Which of the the following are true regarding regression models?
Select all that apply.
Group of answer choices
Errors sum to a large number.
The relationship between the dependent variable and independent
variables must be non-linear.
It is used to determine if one or more independent variables
affect the dependent variable.
The error is found by subtracting the actual value from the
predicted value.
Observations must be independent.

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