Answer:
The correct answer that fills the gap is: Cartels generate the highest joint profit, they want to avoid a price war that leads to profit erosion and P=MC, a cournot oligopoly will generate more profit than a bertrand oligopoly
Explanation:
In Bertrand's model, consumers will buy the goods of the company that offers the lowest price. From this it can be intuited that the Nash equilibrium will be the one in which both companies set the same price. For this reason it is not attractive, since they are competition and for some of the two it may not be profitable to decrease the sale price of their products.
Answer:
A. ($1,000).
Explanation:
In the question, it is given that the fair market value of the land is $49,000 and the selling value is $48,000. So, in the given situation the selling value of the land is less than the fair market value which reflect the loss of $1,000
The $1,000 is come by subtracting the selling value and the fair market value
All other information is not relevant for the computation part. Hence, ignored it
Answer:
Let Sanguine Wines Ltd. refer to a hypothetical company for the purpose. Following would constitute Sanguine Wines Ltd's variable costs:
- Raw Material or input prices: The raw material or inputs of sanguine wines limited purchases from suppliers such as dried grapes, sugar and the likes. The price of such inputs is prone to seasonal fluctuation and thus variable
- The performance related incentive for employees for number of bottles of wine created, would be variable cost as it would vary with the no of bottles produced.
- Discount allowed to distributors which varies based upon the number of bottles purchased by them.
- Commission paid to wine salesperson which varies with respect to bottles sold.
a) - money issued by the financial intermediaries such as banks but not the central bank
Answer:
Supervised and Unsupervised Learning:
a. Unsupervised learning
b. Supervised learning
3. Supervised learning
4. Unsupervised learning
Explanation:
The key difference between supervised machine learning and unsupervised machine learning is that with supervised machine learning there is a training dataset (labeled data) on which the algorithm is trained to predict patterns. With unsupervised machine learning on the other hand, there is no training data. So, the algorithm discovers patterns on itself without reference to another labeled data or training dataset.
