Problem 4 Select all employee's name and bonus whose bonus is < 1000. Table:Employee | empld | name | supervisor| salary | | 3 | Dan | 1 | 2 | 3 | 1000 | | 2000 | | 4000 | | 4000 | | John | 3 | Brad I null | Thomas | 3 4 -----+ empld is the primary key column for this table. Table: Bonus || empld | bonus | | 2 | 500 I | 4 | 2000 | empld is the primary key column for this table. Example ouput | name | bonus || || John | null | | Dan | 500 I | Brad | null |
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- <ul> - `bsal` = beginning salary (annual salary at time of hire) - `sal77` = annual salary in 1977 - `sex` = MALE or FEMALE - `senior` = months since hired - `age` = age in months - `educ` = years of education - `exper` = months of prior work experience </ul> banksalary.csv bsal,sal77,sex,senior,age,educ,exper 5040,12420,MALE,96,329,15,14 6300,12060,MALE,82,357,15,72 6000,15120,MALE,67,315,15,35.5 6000,16320.00098,MALE,97,354,12,24 6000,12300,MALE,66,351,12,56 6840,10380,MALE,92,374,15,41.5 8100,13979.99902,MALE,66,369,16,54.5 6000,10140,MALE,82,363,12,32 6000,12360,MALE,88,555,12,252 6900,10920,MALE,75,416,15,132 6900,10920,MALE,89,481,12,175 5400,12660.00098,MALE,91,331,15,17.5 6000,12960,MALE,66,355,15,64 6000,12360,MALE,86,348,15,25 5100,8940,FEMALE,95,640,15,165 4800,8580,FEMALE,98,774,12,381 5280,8760,FEMALE,98,557,8,190 5280,8040,FEMALE,88,745,8,90 4800,9000,FEMALE,77,505,12,63 4800,8820,FEMALE,76,482,12,6 5400,13320,FEMALE,86,329,15,24 5520,9600,FEMALE,82,558,12,97…<ul> - `bsal` = beginning salary (annual salary at time of hire) - `sal77` = annual salary in 1977 - `sex` = MALE or FEMALE - `senior` = months since hired - `age` = age in months - `educ` = years of education - `exper` = months of prior work experience </ul> banksalary.csv bsal,sal77,sex,senior,age,educ,exper 5040,12420,MALE,96,329,15,14 6300,12060,MALE,82,357,15,72 6000,15120,MALE,67,315,15,35.5 6000,16320.00098,MALE,97,354,12,24 6000,12300,MALE,66,351,12,56 6840,10380,MALE,92,374,15,41.5 8100,13979.99902,MALE,66,369,16,54.5 6000,10140,MALE,82,363,12,32 6000,12360,MALE,88,555,12,252 6900,10920,MALE,75,416,15,132 6900,10920,MALE,89,481,12,175 5400,12660.00098,MALE,91,331,15,17.5 6000,12960,MALE,66,355,15,64 6000,12360,MALE,86,348,15,25 5100,8940,FEMALE,95,640,15,165 4800,8580,FEMALE,98,774,12,381 5280,8760,FEMALE,98,557,8,190 5280,8040,FEMALE,88,745,8,90 4800,9000,FEMALE,77,505,12,63 4800,8820,FEMALE,76,482,12,6 5400,13320,FEMALE,86,329,15,24 5520,9600,FEMALE,82,558,12,97…Figure 8.15 Sensitivity Report for the Investment Advisors Problem Variable Cells Model Variable Name Final Value Reduced Cost U U.S. Oil 800.000 0.000 H Huber 1200.000 0.000 Constraints Constraint Number Name Final Value Shadow Price 1 Funds available 80000.000 0.093 2 Risk maximum 700.000 1.333 4 U.S. Oil maximum 800.000 0.000 Refer to Figure 8.15, which shows the sensitivity report for Problem 7. How much would the return for U.S. Oil have to increase before it would be beneficial to increase the investment in this stock? How much would the return for Huber Steel have to decrease before it would be beneficial to reduce the investment in this stock? How much would the total annual return be reduced if the U.S. Oil maximum were reduced to 900 shares?
- The graph below shows different function growth complexities (Big-Oorders). Label each line with the appropriate Big-O growth function from thetable on the chart.Problem 1: Consider the following knowledge base.If a student's CGPA is < 2.00, the student is on academic probation. All students who are on academic probation must meet their academic advisors before they can register. There is only one academic advisor for MBA students, Professor. Rose is an MBA student and Rose has a CGPA < 2.00. Rose must register for the semester. Answer the following questions. Represent the above knowledge base using FOL. Use the resolution algorithm to show that Rose must meet Professor.A software company has 3 senior developers and 4 junior developers. These developers are working as 3 different teams; 2 sets of ‘1Senior+1Junior’ and 1 set of ‘1Senior+2Junior’. Productivity levels of junior developers according to their senior developer working with are given in the table. Formulate this problem as an LP model. Senior D. 1 Senior D. 2 Senior D. 3 Junior D. 1 2 10 5 Junior D. 2 6 9 8 Junior D. 3 7 5 6 Junior D. 4 10 6 10
- variable cells Cell Name Final Value Reduced Cost Objective Coefficient Allowable Increase Allowable Decrease $B$6 Activity 1 3 0 30 23 17 $C$6 Activity 2 6 0 40 50 10 $D$6 Activity 3 0 –7 20 7 1E+30 Constraints Cell Name Final Value Shadow Price Constraint R.H. Side Allowable Increase Allowable Decrease $E$2 Resource A 20 7.78 20 10 12.5 $E$3 Resource B 30 6 30 50 10 $E$4 Resource C 18 0 40 1E+30 22 If the coefficient of Activity 1 in the objective function changes to $10, then: Multiple Choice the original solution remains optimal. the problem must be resolved to find the optimal solution. the shadow price is valid. the shadow price is not valid. None of the above.Variable cells Cell Name Final Value Reduced Cost Objective Coefficient Allowable Increase Allowable Decrease $B$6 Activity 1 3 0 30 23 17 $C$6 Activity 2 6 0 40 50 10 $D$6 Activity 3 0 −7 20 7 1E+30 Constraints Cell Name Final Value Shadow Price Constraint R.H. Side Allowable Increase Allowable Decrease $E$2 Resource A 20 7.78 20 10 12.5 $E$3 Resource B 30 6 30 50 10 $E$4 Resource C 18 0 40 1E+30 22 If the right-hand side of Resource B changes to 10, then: Multiple Choice the original solution remains optimal. the problem must be resolved to find the optimal solution. the shadow price is valid. the shadow price is not valid. None of the choices is correct.(An LP model for the blending problem) The Exxoff-Still Company must decide how different components of the raw materials are blended for their gasoline production. The characteristics of their two gasoline products are listed below: Gasoline Vaper Pressure Octane Number Selling price ($/barrel) Regular ≤7 ≥80 10 Premium ≤6 ≥100 12 The characteristics of the raw materials from which the gasoline can be blended are shown below: Raw Materials Vaper Pressure Octane Number Availability (barrels) Cat Gas 8 83 2700 IsoIso Gas 20 109 1350 Brady Gas 4 74 4100 The vapor pressure and octane number of a blend is simply the weighted average of the corre- sponding characteristics of its components (weighted by their proportion in the blend). (a) Please formulate a linear programming model to determine how different components of the raw materials are blended for their gasoline production to maximize Exxoff-Still’s total rev- enue. Be sure to clearly define all decision variables and explain the…
- 1. Prove the following equivalency relation: ¬P ∨¬Q≡¬(P ∧Q) 2. Prove the following equivalency relation: ¬P →¬Q≡Q→PIt's a Machine Learning question. Suppose we have market basket data consisting of 100 transactions and 20 items. Compute the total number of possible association rules. If the support for item A is 25%, the support for item B is 90% and the support for itemset {A,B} is 20%. Let the support and confidence thresholds be 10% and 60%, respectively. Compute the confidence of the association rule {A} → {B} & {B} → {A}.The Jones family wants to buy a new car and uses AHP to decide systematically which best car they should buy.When deciding which car to buy, the family considers the following criteria: cost, safety, style, and capacity. Additionally, some of the criteria have sub-criteria.The Jones’ have a shortlist of 6 cars.the hierarchy picture is given. Use AHP techniques to:Construct the Pair-wise matrix and the normalized matrixCalculate the consistency and check your pair-wise matrixGive the Criteria weights used by the decision maker for further calculation