Question

Suppose we have the following values for the linear function relating X and Y (where Y is the dependent variable and X is the independent variable:

What would the value of R-Square be for this straight line?

Answer #1

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The value is not mentioned in the questions. But if you have the linear regression exercise in the charts/values then you can deduce the RSquare in the following way:

1. The RSquare can taken out as a ratio of Sum of Squares of Model to the Total Sum of Square = SS model / SS total

2. or you could directly pick it up where R-Sq or Coefficient of determination or RSquare (r^2) is mentioned in the table

Suppose we have the following values for the linear function
relating X and Y (where Y is the dependent variable and X is the
independent variable:
X Y
0 45
1 25
2 5
What is the value of the slope for this straight line?

Suppose we have the
following values for the linear function relating X and Y (where Y
is the dependent variable and X is the independent variable:
X Y
0 45
1 25
2 5
What is the value of
the slope for this straight line?
Question 3 options:
25
20
-20
-10

In linear regression, if we have independent variable “drug
dosage” along the x -axis and the dependent variable “hours of
sleep” along the y-axis, then interpret regression lines with a
steep negative slope, a flat horizontal line through the data, and
a steep positive slope in terms of how the independent variable
affects the dependent variable

Suppose you are given the following x and y values. Assume x is
the independent variable and y the dependent variable.
x
y
13
10
10
11
3
4
29
23
5
8
25
21
8
10
17
15
19
17
31
29
23
24
What is the coefficient of determination?

Suppose that E[X]= E[Y] = mu, where mu is a fixed unknown
number. We have independent simple random samples of size n each
from the distribution of X and Y, respectively. Suppose that Var[X]
= 2*Var[Y]. Consider the following estimators of mu:
m1 = bar{X}
m2 = bar{Y}/2
m3 = 3*bar{X}/4 + 2*bar{Y}/8
where bar{X} and bar{Y} are the sample mean of X and Y values,
respectively. Which of the estimators are unbiased?

For independent X and Y, we have probability density function
for them where pdf of X is f(x) = ne^-nx and pdf of Y is f(y) =
me^-my. (x and y greater than 0). Let M1=max(X,Y) and M2=min(X,Y).
Find cov(M2,M1).

For independent X and Y, we have probability density function
for them where pdf of X is f(x) = ne^-nx and pdf of Y is f(y) =
me^-my. (x and y greater than 0). Let M1=max(X,Y) and M2=min(X,Y).
Find cov(M2,M1).

For independent X and Y, we have probability density function
for them where pdf of X is f(x) = ne^-nx and pdf of Y is f(y) =
me^-my. (x and y greater than 0). Let M1=max(X,Y) and M2=min(X,Y).
Find cov(M2,M1).

Suppose you are given the following x and y values. Assume x is
the independent variable and y the dependent variable.
x
y
13
10
10
11
3
4
29
23
5
8
25
21
8
10
17
15
19
17
31
29
23
24
Is there a significant relationship between x and y? Explain.
Test at a 5% level of significance

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

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