Bond Yield One measure of a bond's performance is its Yield To Maturity (YTM). YTM values for government bonds are complex to calculate and are published in tables. However, they can be approximated with the simple formula
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- A three stage amplifier is to have an overall noise temperature no greater than 7O K. The overall gain of the amplifier is to be at least 45 dB. The amplifier is to be built by adding a low-noise first stage to an existing two-stage amplifier that has the gains and noise figures shown below. The stage number refer to locations in the new amplifier. Stage Power Gain Noise Figure 20 dB 3 dB 13 15 dB 6 dB a. What is the minimum gain in decibels that the first stage can have? b. Using the gain you calculated in part (a), calculate the maximum noise figure in decibels that the first stage can have. Express answer with two decimal places. c. Suppose the gain of the first stage could be increased by 3 dB without affecting its noise figure. What would be the effect on the noise temperature of the complete amplifier?arrow_forwardQuestion 1: a)Population Growth The world population data spans from 1960 to 2017. We'd like to build a predictive model that can give us the best guess at what the population growth rate in a given year might be. We will calculate the population growth rate as follows:- Growth_rate=current_year_population−previous_year_populationprevious_year_population As such, we can only calculate the growth rate for the year 1961 onwards. Write a function that takes the population_df and a country_code as input and computes the population growth rate for a given country starting from the year 1961. This function must return a return a 2-d numpy array that contains the year and corresponding growth rate for the country. Function Specifications: Should take a population_df and country_code string as input and return a numpy array as output. The array should only have two columns containing the year and the population growth rate, in other words, it should have a shape (?, 2) where ? is…arrow_forwardProject Optimization and regression: Exact and approximate methods to solve 0-1 Knapsack problem Description The 0/1 Knapsack Problem and Logistics Transportation companies such as TNT and Royal Mail face daily problems in logistics. Consider the following simple logistics problem, which you will solve: An airline cargo company has 1 aeroplane which it flies from the UK to the US on a daily basis to transport some cargo. In advance of a flight, it receives bids for deliveries from (many) customers. Customers state the weight of the cargo item they would like delivered, and the amount they are prepared to pay. The airline is constrained by the total amount of weight the plane is allowed to carry. The company must choose a subset of the packages (bids) to carry in order to make the maximum possible profit, given the weight limit that they must respect. In mathematical form the problem is: Given a set of N items each with weight wi and value vi, for i=1 to N, choose a subset of items…arrow_forward
- QUESTION 1 An admission charge for Bandar Orchid Wetlands varies according to the age of the customer as in Figure 1. The system will receive the input for the number of persons for each category as in Figure 2. Then, it will calculate and display the total charge (include SST 8%) and the category Figure 3. [Assume all items are required. The process is not involving any decision]. Category (Age) 50 and above 13- 49 7-12 4-0 Charge (RM) 30.00 35.00 25.00 15.00 Below 4 Figure 1: Charges for Visitors Free The Bold items are the input from the user. WELCOME TO BANDAR ORCHID WETLANDS Enter the number of persons for each category Age Over 50 Age 13 - 50 Age 7 - 12 Age 4 - 6 Age Below 4 1 2 Figure 2: The Input Total for Over 50 (1 Fersons): BM 30.00 Total for 13 - 50 (2 Persons): RM 70.00 Total for 7 - 12 (4 Persons): RM 100.00 Total for 4 - 6_(O Persons): RM 0.00 Total for Below 4 (FREE) : RM 0.00 ITEMS RM 200.00 12.00 TOTAL 3ST (6R) SUB-TOTAL = 212.00 THANK YOU.ENJOY YOUR HOLIDAY!! Figure…arrow_forwardPredictive modeling: Task: Ambulance Demand Data Generating Process: Using the service data: The data is collected by the " NYC Traffic data” to answer the questions: Frequency of data collection {how often is the data collected? After every incident? Daily? Yearly?} Agency / Organization collecting the data {who specifically is collecting the data? Please avoid using general references like “government” or “police} Original Unit of Analysis {What is the original unit of analysis for the data as provided? Calls for service? Census tracts? Cities?} Transformed Unit of Analysis{i.e. are you modifying the call data to support your model? Hint: if you are doing “demand” model you will be aggregating the data.} Data Generation Description{here, I want you in your own words to describe how you think the data was generated. Think 2-3 sentences.} Data Collector{Who collects the data? A dispatcher? A Census taker?} Triggering Process{What triggers the data to be collected?…arrow_forwardQuestion 31 A survey on pollution has been conducted on four cities. The degree of pollution is classified as not polluted, moderately polluted, and heavily polluted. The results of the survey are illustrated in the table below: City city1 city2 city3 city4 Xi1: not polluted 0.3 0.2 0.1 0.7 X12: moderately polluted 0.4 0.2 0.2 0.4 Xi3: heavily polluted 0.3 0.6 0.5 0.1 Use the cosine amplitude method to develop the fuzzy relation R that relates the pollution degree to the city.arrow_forward
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