Answer:
1) 19 days
2) 109 days
Explanation:
1. Days of personal use = days in which he stayed in the house = 19
2. Days of rental use = actual days which was played for out days for elligible rent.
Where Fbr = favorite brother rent days = 11
Fbl = least favorite brother rent days = 12 days
Rfb = days of rent to friend = 14
Tpr = third party rent days = 72
Days of rental use = Fbr + Fbl + Rfb + Tpr = 11 + 12 + 14 +72 = 109days
Answer:B
Explanation:
In terms of stable supply curve and increasing demand. Looking at the law of demand that state the lower the price, the higher the quantity demanded. The law of supply also state that keeping other factors constant, an increase in price results in an increase in quantity supplied.
Answer: question not complete
c. Suppose Beth’s kindly (but still greedy) father offers to eliminate the uncertainty in Beth’s profits by agreeing to trade her the weekly profits based on a stable price of $20 per acre in exchange for the profits Beth actually makes. Should she take the deal?
d. Graph your results and explain them intuitively. Solution to this is in the picture attached
Explanation:
During the period of drought, the rate of mowing is $15 per acre, while, in the period of monsoons, the rate is $25 per acre.
a). Beth's supply function will be
q = 5P - 50
If P =15
q = 5 x 15 - 50
= 75-50
= 25
If P = 25
q = 5 x 25 - 50
= 125 - 50
= 75
b). The periods of both drought and monsoon will last for equal number of weeks in the summer. The average weekly profit will be:
If P = 15
π = P x q - C
= 15 x 25 - 362.5
= 375 - 362.5
= 12.5
If P =25
π = P x q - C
= 25 x 75 - 1362.5
= 1875 - 1362.5
= 512.5
The average weekly profit will be
=12.5 + 512.5 / 2
=525 / 2
= 262.5
c). Father offer a price of $20 irrespective of the season and the quantity will be:
q = 5 x 20 - 50
= 50
The profits in this case will be
π = P x q - C
= 20 x 50 - 800
= 200
This is less than the average profits in the other cases, hence, this offer will make her worse off.
K-means is an algorithm for unsupervised clustering that divides unlabeled data into a predetermined number (the "K") of unique groupings. To put it another way, K-means identifies observations that have similar crucial properties and groups them into clusters.
What are the various types of clusters and why is the distinction important?
The several types of clustering include:
- Connectivity-based Clustering (Hierarchical Clustering): According to the idea that every object is connected to its neighbors based on their closeness distance, hierarchical clustering, also known as connectivity-based clustering (degree of relationship).
- Centroid-based or partition clustering- Of all the clustering types used in data mining, centroid-based clustering is the simplest. It bases its operation on how closely the data points resemble the selected center value.
- Density-based Clustering (Model-based Methods): In this method, density is taken into account before distance.
- Distribution-Based Clustering- In this method, data points are created and grouped according to how likely it is that they would belong to the same probability distribution (such as a Gaussian, binomial, or other) in the data.
- Fuzzy Clustering: Fuzzy clustering broadens the use of partition-based clustering by allowing a data object to be a part of more than one cluster.
- Constraint-based (Supervised Clustering) - A constraint is characterized as the desirable characteristics of the clustering outcomes or a user's expectation of the resulting clusters. A predetermined number of clusters, the size of the clusters, or important dimensions (variables) required for the clustering process can all be used to convey this.
What are the strengths and weaknesses of k-means?
- One of the most popular and widely used clustering techniques is simple k-means. K-means has a lot of benefits, one of which is that it is quite simple to use and, more importantly, that most of the time you don't even have to use it yourself!
- We can never be certain of the true cluster because the same data inputted in a different order could result in a different cluster if there are little data. sensitive to the original state. Different starting circumstances could lead to different cluster results.
What is a cluster evaluation?
Sharing accomplishments and cooperative problem solving among the projects in the cluster form the basis for cluster evaluation (often projects funded from a basket fund).
Learn more about K-means clustering: brainly.com/question/15016224
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Answer:
The description of the given question is explained throughout the section below.
Explanation:
- The value of the property seems to be under competition from increasing numbers of marketers who'd like to acquire at around that price, the property's amount is increasing, which is defined as resistance.
- Its positions can sometimes be short-lasting if the fresh result of developments that influence the perceptions of such mainstream economy forward towards the commodity.