Let us consider the data of marks scored in unit tests held in school. For each unit test, the marks scored by all students of the class is recorded. Maximum marks are 25 in each subject. The subjects are Maths, Science. Social Studies (S.St.), Hindi, and English. For simplicity, we assume there are 4 students in the class and the table below shows their marks in Unit Test 1, Unit Test 2 and Unit Test 3. Table 3.1 shows this data. Table 3.1 Case Study Result Maths Science S.St. Hindi Name/ Unit Subjects Test Raman 1 Raman 2 Raman 3 Zuhaire 1 Zuhaire 2 Zuhaire 3 Aashravy 1 Aashravy 2 Aashravy 3 Mishti 1 Mishti 2 Mishti 3 22 21 14 20 23 22 23 24 12 15 587 18 17 21 20 19 17 15 22222 18 19 25 21 18 18 17 15 22 21 19 20 24 19 25 25 20 20 22 24 24 25 23 15 17 21 22 24 25 Eng 21 24 23 19 15 13 22 21 23 22 23 20 Let us store the data in a DataFrame, as shown in

Let us consider the data of marks scored in unit tests held in school. For each unit test, the marks scored by all students of the class is recorded. Maximum marks are 25 in each subject. The subjects are Maths, Science. Social Studies (S.St.), Hindi, and English. For simplicity, we assume there are 4 students in the class and the table below shows their marks in Unit Test 1, Unit Test 2 and Unit Test 3. Table 3.1 shows this data. Table 3.1 Case Study Result Maths Science S.St. Hindi Name/ Unit Subjects Test Raman 1 Raman 2 Raman 3 Zuhaire 1 Zuhaire 2 Zuhaire 3 Aashravy 1 Aashravy 2 Aashravy 3 Mishti 1 Mishti 2 Mishti 3 22 21 14 20 23 22 23 24 12 15 587 18 17 21 20 19 17 15 22222 18 19 25 21 18 18 17 15 22 21 19 20 24 19 25 25 20 20 22 24 24 25 23 15 17 21 22 24 25 Eng 21 24 23 19 15 13 22 21 23 22 23 20 Let us store the data in a DataFrame, as shown in

Operations Research : Applications and Algorithms

4th Edition

ISBN:9780534380588

Author:Wayne L. Winston

Publisher:Wayne L. Winston

Chapter20: Queuing Theory

Section20.2: Modeling Arrival And Service Processes

Problem 3P

See similar textbooks

Similar questions

Assuming a total sample of 1079 persons, among which 520 persons are having autism and 559 are healthy persons. When we pass the data of 520 autism patients into the KNN classifier, it correctly predicted “220” patients as autism category and the remaining patients into healthy category. Similarly, from 559 healthy persons, the KNN categorize “100” as autism patients and the remaining as healthy persons. In the above scenario, if “autism” is considered as “positive class” and “healthy person” is considered as negative class then find the: True Positive True Negative False Positive False Negative Sensitivity (True positive rate) Specificity (True negative rate) Accuracy Precision
Assuming a total sample of 1079 persons, among which 520 persons are having autism and 559 are healthy persons. When we pass the data of 520 autism patients into the KNN classifier, it correctly predicted “220” patients as autism category and the remaining patients into healthy category. Similarly, from 559 healthy persons, the KNN categorize “100” as autism patients and the remaining as healthy persons. In the above scenario, if “autism” is considered as “positive class” and “healthy person” is considered as negative class then find the: True Positive True Negative False Positive False Negative Sensitivity (True positive rate) Specificity (True negative rate) Accuracy Precision Draw the confusion matrix Assuming a total sample of 1079 persons, among which 520 persons are having autism and 559 are healthy persons. When we pass the data of 520 autism patients into the KNN classifier, it correctly predicted “220” patients as autism category and the…
Bluereef real estate agent wants to form a relationship between the prices of houses, how many bedrooms, House size in sq ft and Lot Size in sq ft.The data pertaining to 100 houses were processed using MINITAB and the following is an extract of the output obtained:The regression equation is Price = B + Bredroom + House Size + Alot SizePredictor Coef SE Coef T PConstant 37718 14177 2.66 **Bedrooms 2306 6994 0.33 0.742House Size 74.3 52.98 * 0.164Lot Size -4.36 17.02 -0.26 0.798 S= 25023 R-Sq=56.0% R-Sq(adj)=54.6% Source DF SS MS F PRegression 3 76501718347 25500572782 *** ****Residual 96 60109046053…
Ma1. 1) On a Bank Reconciliation, if our check was written for $492.83 and was processed as such by the bank, but had been shown in our company's accounting records as a check for $498.23, we would code this as a C+ item.T rue or False 2) In the Bottom-Up method of calculating required revenue, we treat the amount of desired net income (once we have calculated how much it should be) as:a. a variable cost. b. a step cost. c. unnecessary for the calculation. d. a fixed item. e. none of the above. 3) A large F variance from budget in a revenue item should be investigated. True or False 4) If a Bank Reconciliation cannot be made to balance, then something unusual has occurred and must be investigated. True or False 5) In preparing a bank reconciliation, we will code an NSF check (using the fabulous Bessner system) as: a. a C+ item. b. a C- item. c. a B+ item. d. a B- item. e. none of the above. 6) If a company wants to end up with an AFTER-TAX profit of $25,000, and its tax rate is 38%,…
In the refrigerator example, suppose the warranty worksas follows. If a refrigerator fails at any time within 5 yearsof purchase, we give the consumer a prorated refund on the$500 purchase price. For example, if the refrigerator failsafter 4 years, we pay the customer $100. If the refrigeratorfails after 3 years, we pay the customer $200. Estimate ourexpected warranty expense per refrigerator sold.
The following data give the starting salary for students who recently graduated from a local university and accepted jobs soon after graduation. The starting salary, grade-point average (GPA), and major (business or other) are provided. SALARY $29,500 $46,000 $39,800 $36,500 GPA 3.1 3.5 3.8 2.9 Major Other Business Business Other SALARY $42,000 $31,500 $36,200 GPA 3.4 2.1 2.5 Major Business Other Business Using a computer, develop a regression model that could be used to predict a starting salary based on GPA and major. Use this model to predict the starting salary for a business major with a GPA of 3.0. What does the model say about the starting salary for a business major compared to a non-business major? Do you believe this model is useful in predicting the starting salary? Justify your answer, using the information provided in the computer output.
(Code in R language) Consider the data presented in the Trades.csv file (table given below). This file represents 35 days worth of data from a brokerage house that is trying to predict the number of trade executions per day as a function of the number of incoming phone calls to the Set up a scatterplot of the Determine the fitted regression equation for this data, and use it to predict the number of trade executions that will occur if there are 2300 incoming calls to the If the firm receives 100 more calls on Tuesday than they did on Monday, how many more executions should they expect? Suppose the CFO asks you to predict the number of executions when there are 3500 incoming calls. What should you say? (Trades.csv file) Day Calls Executions 1 2591 417 2 2146 321 3 2185 362 4 2245 364 5 2600 442 6 2510 386 7 2394 370 8 2486 376 9 2483 463 10 2297 389 11 2106 302 12 2035 266 13 1936 339 14 1951 369 15 2292 403 16 2094 319 17 1897 306 18 2237 397…
This question involves data from which nothing interesting can be learned about frequent itemsets, because there are no sets of items that are correlated. Suppose the items are numbered 1 to 10, and each basket is constructed by including item i with probability 1/i, each decision being made independently of all other decisions. That is, all the baskets contain item 1, half contain item 2, a third contain item 3, and so on. Assume the number of baskets is sufficiently large that the baskets collectively behave as one would expect statistically. Let the support threshold be 1% of the baskets.(a) Find the frequent itemsets.(b) Prove that in this data there are no interesting association rules, i.e., the interest of every association rule is 0.
Answer the following: This problem exercises the basic concepts of game playing, using tic-tac-toe (noughts and crosses) as an example. We define Xn as the number of rows, columns, or diagonals with exactly n X’s and no O’s. Similarly, On is the number of rows, columns, or diagonals with just n O’s. The utility function assigns +1 to any position with X3=1 and −1 to any position with O3=1. All other terminal positions have utility 0. For nonterminal positions, we use a linear evaluation function defined as Eval(s)=3X2(s)+X1(s)−(3O2(s)+O1(s)). a. Show the whole game tree starting from an empty board down to depth 2 (i.e., one X and one O on the board), taking symmetry into account. b. Mark on your tree the evaluations of all the positions at depth 2. c .Using the minimax algorithm, mark on your tree the backed-up values for the positions at depths 1 and 0, and use those values to choose the best starting move. Provide original solutions including original diagram for part a!
Answer the following: This problem exercises the basic concepts of game playing, using tic-tac-toe (noughts and crosses) as an example. We define Xn as the number of rows, columns, or diagonals with exactly n X’s and no O’s. Similarly, On is the number of rows, columns, or diagonals with just n O’s. The utility function assigns +1 to any position with X3=1 and −1 to any position with O3=1. All other terminal positions have utility 0. For nonterminal positions, we use a linear evaluation function defined as Eval(s)=3X2(s)+X1(s)−(3O2(s)+O1(s)). a. Show the whole game tree starting from an empty board down to depth 2 (i.e., one X and one O on the board), taking symmetry into account. b. Mark on your tree the evaluations of all the positions at depth 2. c .Using the minimax algorithm, mark on your tree the backed-up values for the positions at depths 1 and 0, and use those values to choose the best starting move. Provide original solution!
What is the code in Stata for this question? Run the linear regression given by wages = β0 +β1education+β2workexp+β3unionmember +β4south+ β5−9occupation + β10female + u where wages=hourly wage in US dollars; education=years of schooling; workexp=years of work experience; unionmember=a dummy variable equal to ”1” if a person is a union member, and ”0” otherwise; south=a dummy variable equal to ”1” if a person lives in the south, and ”0” otherwise; occupation=a categorical variable equal to ”1” if a person’s occupation is ”management”; ”2” if it is ”sales”; ”3” if it is ”clerical”, ”4” if it is ”service”, ”5” if it is ”professional” and ”6” if it is ”other”. Please make sure to use ”other” as the base category; female =a dummy variable equal to ”1” if a person is female, and ”0” if a person is male.
Correct answer will be upvoted else downvoted. Let C={c1,c2,… ,cm} (c1<c2<… <cm) be the arrangement of individuals who hold cardboards of 'C'. Let P={p1,p2,… ,pk} (p1<p2<… <pk) be the arrangement of individuals who hold cardboards of 'P'. The photograph is acceptable if and provided that it fulfills the accompanying requirements: C∪P={1,2,… ,n} C∩P=∅. ci−ci−1≤ci+1−ci(1<i<m). pi−pi−1≥pi+1−pi(1<i<k). Given a cluster a1,… ,an, kindly track down the number of good photographs fulfilling the accompanying condition: ∑x∈Cax<∑y∈Pay. The appropriate response can be huge, so output it modulo 998244353. Two photographs are unique if and provided that there exists no less than one individual who holds a cardboard of 'C' in one photograph yet holds a cardboard of 'P' in the other. Input Each test contains numerous experiments. The main line contains the number of experiments t (1≤t≤200000). Depiction of the experiments follows. The…