The market risk premium calculates the slope of the security market line.
What is market risk premium?
A risk premium is the higher rate of returns you can expect from riskier investments like stocks compared to risk-free investments like government bonds. The pricing model for capital assets is graphically represented by the security market line (SML), a line drawn on a graph. The SML can be used to assist in trying to compare an investment product's rate of return towards its level of risk.
Numerous exogenous factors can have an impact on the slope of a security market line. For instance, the economy's real interest rate could change, inflation might rise or fall, there could be a recession, and investors could start to take less risk generally.
In other words, higher risk also comes with higher rewards because more systematic risk is linked to higher expected returns for securities. The linear relationship also explains why security market line is indeed a straight line.
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The correct answer would be harder.
Answer:
- No, the points are evenly distributed about the x-axis.
Explanation:
<u>1. Write the table with the data:</u>
x given predicted residual
1 - 3.5 - 1.1
2 - 2.9 2
3 - 1.1 5.1
4 2.2 8.2
5 3.4 1.3
<u>2. Complete the column of residuals</u>
The residual is the observed (given) value - the predicted value.
- residual = given - predicted.
Thus, the complete table, with the residual values are:
x given predicted residual
1 - 3.5 - 1.1 - 2.4
2 - 2.9 2 - 4.9
3 - 1.1 5.1 - 6.2
4 2.2 8.2 - 6.0
5 3.4 1.3 2.1
<u>3. Residual plot</u>
You must plot the last column:
x residual
1 - 2.4
2 - 4.9
3 - 6.2
4 - 6.0
5 2.1
See the plot attached.
<em>Does the residual plot show that the line of best fit is appropriate for the data?</em>
Ideally, a residual plot for a line of best fit that is appropiate for the data must not show any pattern; the points should be randomly distributed about the x-axis.
But the points of the plot are not randomly distributed about the x-axis: there are 4 points below the x-axis and 1 point over the x-axis: there are more negative residuals than positive residuals. This is a pattern. Also, you could say that they show a curve pattern, which drives to the same conclusion: the residual plot shows that the line of best fit is not appropiate for the data.
Thus, the conclusion should be: No, the points have a pattern.
- 1. "<em>Yes, the points have no pattern</em>": false, because as shown, the points do have a pattern, which makes the residual plots does not show that the line of best fit is appropiate for the data.
- 2. "<em>No, the points are evenly distributed about the x-axis</em>": true. As already said the points have a pattern. It is a curved pattern, and this <em>shows the line of best fit is not appropiate for the data.</em>
- 3. "<em>No, the points are in a linear pattern</em>": false. The points are not in a linear pattern.
- 4. "<em>Yes, the points are in a curved pattern</em>": false. Because the points are in a curved pattern, the residual plot shows that the line of best fit is not appropiate for the data.
The complete function definition to output the hours given minutes is as follows:
def output_minutes_as_hours(orig_minutes):
return f"{orig_minutes / 60 } hours"
print(output_minutes_as_hours(120))
<h3>How to complete programming in Python?</h3>
The code is written in python.
Firstly, we define a function named "output_minutes_as_hours" and the function accept an argument "orig_minutes". The argument is the time in minutes.
Then, we return answer in hours when we divide the orig_minutes by 60.
Finally, we call the function along with it parameter.
The complete question is;
Complete the function definition to output the hours given minutes.
Sample output with input: 210.0
3.5
using variables def output_minutes_as_hours(orig_minutes):
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