There lies different opportunities for acquiring profit as well as loss in the concerned business by Ilya and Gregor, which in turn depend on the set of three probable strategies which yield different results in the favourable market and in the unfavourable market. Strategy 1 involves Gregor and Ilya renting a considerably costly office in the location near their probable customers. On the other hand, under Strategy 2, they can rent a comparatively cheaper office in the neighbouring suburb region and the last strategy is the strategy of not at all opening any business venture.
As can be seen from the above, Ilya being a risk loving and optimist personnel is eager to opt for maximum risk to get greater benefits. Keeping this into account the optimum strategy in the perception of Ilya is that of Strategy 1, as here, both the risks as well as level of expected profits are maximum.
Gregor being more conservative and risk averse than that of Ilya, the optimum strategy of the same is the one with maximum profit along with minimum risk. Thus, Gregor will choose the third strategy as the loss is minimum in the concerned strategy.
As is evident from the above numbers, the expected profit is comparatively highest in case of the second strategy, which in turn indicates to the fact that the second strategy of getting an office in the cheaper suburbs will be chosen.
iii. The probability range in which the third strategy can be chosen is 0≤P≤0.28
From the concerned problem, the linear programming model can be constructed as below:
Min (Z) = 960(TV) + 480 (Radio) + 600(Billboards) + 120 (Newspaper)
Subject to:
TV≤10
Radio≤10
Billboards≤10
Newspaper≤10
TV≥6
Radio≥6
TV + Radio≥6
960(TV) – 600(Billboards) – 120(Newspaper)≥0
960(TV) + 480(Radios) + 600(Billboards) + 120(Newspapers)≤14000
Non-negativity constraints: TV≥0, Radios≥0, Newspaper≥0, Billboards≥0
The solution of the above problem can be seen as follows:
= (6*36000) + (6*26500) +(8*30000) = 615000.
By taking area as the independent variable, the predicted selling prices for the house can be seen as follows:
Predicted Price = -34301.5987 + [62.96*(Area)]
For the concerned model, the coefficient of determination = (0.7952^2) = 0.6323. This in turn shows that around 62.23% of the dynamics or variability of the price of the house can be explained by the independent variable (Area) of the same.
Therefore, when Area is equal to 2000 square ft, the selling price of the same is as follows:
Price = -34301.5987 + (62.96*2000) = 91618.4
Now, considering bedrooms as the independent variable, the predicted selling price of the concerned house is as follows:
Predicted Price = 648.6487 + [35168.9189*(Bedroom numbers)]
For this model, the determination coefficient = (0.5047^2) = 0.2547, which in turn implies that nearly 25.47% of the variations in the price of the house can be explained by the bedroom numbers present in the house.
Thus, when the number of bedrooms in the house is 3,
Price = 648.6487 + [35168.9189*3] = 106155
If age is considered to be the independent variable in the concerned problem, then,
Expected Price = 141448.2518 + [-2256.7296*(Age)]
The coefficient of determination being (0.8629^2) = 0.7446, nearly 74.46% of the selling price variability of the house can be explained by the age of the same. Thus, when the age is 24 years,
Price = 141448.2518 + [-2256.7296*24] = 87286.7.
From the above solutions, it can be asserted that the parameter area, can explain the variability of the price of the house the most, which in turn implies that using the area of the house to predict its selling price is the best possible model.
Considering the bedroom numbers as well as the area of the house as the independent variables, the predicted selling price of the same can be seen as follows:
Price = -26129.5 + [76.1268*(Area)] + [-12403.1*(Bedroom numbers)]
For this model, the determination coefficient is (0.8616^2) = 0.7423, which implies that the bedroom numbers of the house and the area of the same can explain 74.23% of all the variabilities in its selling price.
Again, considering area and age as the independent variables the predicted price for the house can be found as follows:
Price = 69793.9387 + [27.0743 * (Area)] + [-1554.9387 *(Age of the house)]
Determination coefficient in this case, is (0.8978^2) = 0.774, which in turn indicates towards the fact that 77.4% of the total variations in the selling price of the house can be explained by age and area of the same.
Predicted price of house, when bedrooms and age of the house are the independent variables is:
Price = 99495.77 + [12389 * (Number of Bedrooms)] + [-1985.53 * (Age)]
Here, the coefficient of determination is (0.9309^2) = 0.8665. Thus, 86.65% of the selling price variability can be explained by bedroom numbers and the age of the house.
On the other hand, by taking area, age and bedrooms as the independent variables, the predicted price of the concerned house can be seen as follows:
Price = 70181.01 + [25.1505 * (House Area)] + [-1574.39 * (Age)] + [1389.257 * (Bedroom Numbers)]
In this case, the determination coefficient is (0.9407^2) = 0.8848, which indicates that 88.48% of the house price variability can be explained by age, area and number of bedrooms in the concerned house.
Thus, from the above discussion and calculations it can be asserted that the model considering all the variables (age, area and number of bedrooms) explain the variability of the house selling prices the most and is thus the best model.
When the Multilayer Perception Model is run with the three concerned independent variables (age of the house, area of the house and number of bedrooms in the house) for predicting its selling price with 1 hidden layer, it shows that the correlation coefficient is of the value 0.9399. This in turn implies that there is 93.96% accuracy in the concerned model, which in turn implies that the MLP Model is much more accurate than that of the regression model in the aspect of prediction of house prices.
However, when the number of hidden layers increase, the model becomes less accurate which in turn implies that MLP Model is a more efficient one with one hidden layer. However, when there are more hidden layers, the regression model can be considered to be better than the MLP as its precision does not change. This can be shown with the help of the following tables:
Conclusion
As can be seen from the above section, for Logistic Regression, the area under the ROC Curve is 0.888 and for Naïve Bayes it is 0.845, which implies that the former is higher than the latter and thus, the former is a better classifier than the latter.
From the lift charts also, it can be seen that in case of the Regression the lift is higher than that of the Naïve Bayes classifier, which in turn also indicates that the former or the Logistic Regression is a better classifier than the Naïve Bayes classifier.
Essay Writing Service Features
Our Experience
No matter how complex your assignment is, we can find the right professional for your specific task. Contact Essay is an essay writing company that hires only the smartest minds to help you with your projects. Our expertise allows us to provide students with high-quality academic writing, editing & proofreading services.
Free Features
Free revision policy
$10Free bibliography & reference
$8Free title page
$8Free formatting
$8How Our Essay Writing Service Works
First, you will need to complete an order form. It's not difficult but, in case there is anything you find not to be clear, you may always call us so that we can guide you through it. On the order form, you will need to include some basic information concerning your order: subject, topic, number of pages, etc. We also encourage our clients to upload any relevant information or sources that will help.
Complete the order form
Once we have all the information and instructions that we need, we select the most suitable writer for your assignment. While everything seems to be clear, the writer, who has complete knowledge of the subject, may need clarification from you. It is at that point that you would receive a call or email from us.
Writer’s assignment
As soon as the writer has finished, it will be delivered both to the website and to your email address so that you will not miss it. If your deadline is close at hand, we will place a call to you to make sure that you receive the paper on time.
Completing the order and download