Answer:
The gain of $8,000 is recognized and the bonds have a basis of $35,000
Explanation:
Please see attachment
Answer:
$707,000
Explanation:
Calculation for Sam's appraisal cost for quality last year
Using this formula
Appraisal cost = Annual inspection costs + Annual testing cost
Where,
Annual inspection costs =$172,000
Annual testing cost=$535,000
Let plug in the formula
Appraisal cost = $172,000 + $535,000
Appraisal cost = $707,000
Therefore Sam's appraisal cost for quality last year will be $707,000.
Answer:
c. $125.00
Explanation:
Let us assume the x for invested in portfolio
Invested proportion × expected return of the optimal portfolio + (1 - invested proportion) × risk free rate = expected return
x × 7% + (1 - x) × 3% = 8%
7% x + 3% - 3% x = 8%
4% x = 5%
X = 1.25
Now the invested amount would be
= 1.25 × $500
= $625
So, the borrowed amount would be
= $625 - $500
= $125
Answer:
d. It is best measured using the statistic variance inflation factor (VIF).
Explanation:
Multicollinearity is an important issue in multiple regression model, having many independent/ explanatory variables. Multicollinearity is the situation in which two or more independent variables are highly correlated. It is problematic because it increases the standard error of independent variable coefficient & undermines its statistical significance
Variance Inflation Factor [VIF] is a check & corrective measure of multicollinearity.
- VIF as a multicollinearity check : It quantifies the correlation between one explanatory variable with other explanatory variables.VIF = 1 implies there is no multicollinearity (correlation between independent variables); VIF upto 5 implies there is moderate multicollinearity (correlation between independent variables). VIF > 5 implies high multicollinearity (correlation between independent variables)
- VIF as a multicollinearity correction : Calculating
= σ^2 /
; where TSS = total sum of square of variable j , σ^2 = j variance, R^2 j = R^2 from regressing all other independent variable on variable j