Question A major court case on the health effects of drinking contaminated water took place in the town of Woburn, Massachusetts. A town well in Woburn was contaminated by industrial chemicals. During the period that residents drank water from this well, there were 16 birth defects among 414 births. In years when the contaminated well was shut off and water was supplied from other wells, there were 3 birth defects among 228 births. The plaintiffs suing the firm responsible for the contamination claimed that these data show that the rate of birth defects was higher when the contaminated well was in use. How statistically significant is the evidence? What assumptions does your analysis require? Do these assumptions seem reasonable in this case?

A market research firm conducts telephone surveys with a 45% historical response rate. What is the probability that in a new sample of 400 telephone numbers, at least 150 individuals will cooperate and respond to the questions? In other words, what is the probability that the sample proportion will be at least 150 400  = 0.375? (Round your answer to four decimal places.)

Select [a]: True or [b]: False for the following statements. It has been found that cities with more coffee shops have more car accidents; therefore, coffee shops cause poor driving When ice cream sales go up, so do crime rates. A reasonable conclusion is that the increase in ice cream sales causes an increase in the crime rate. Studies have shown that people who eat more fast food are generally more obese than those who do not; therefore, a conclusion is that eating fast food is related to obesity Association does not imply causation. Studies have show that obese people tend to eat more fast food; therefore, a conclusion is that obesity causes a person to eat more fast food a. True b. False

4 Insert the rubber stoppers. Mix cach solution thoroughly 5. Transfer a solution into a Spec 20 cell (fill up to 2/3 of the length) and measure the absorbance of the solution at 450 nm. Record and plot the data 6. Calculate the k value by linear regression - use provided space in the results section. 5. Using the zero knob, set the transmittance to "0" Now, Spec 20 will consider this situation as the complete darkness. 6. Insert a Spec 20 cell (looks like a small test tube) filled with distilled water (reference) in the sample chamber 7. Using 100 % knob, set the transmittance to 100. The concentration of the substance in the solution at this point is O and the light is 100 % transmitted. 8. Remove the reference. 9. Set Mode to Absorbance, insert your sample and read the number displayed. This is your absorbance for the sample. 10. You have to perform steps 2-8 whenever the wavelength setting is changed. Otherwise, keep the Mode at Absorbance and just replace your sample to read off the absorbance value. Determining the equilibrium constant NOTE: This part of experiment requires, 1. 0.0025 M Fe(NO3)3 solution, NOT the diluted solution you prepared. 2. 0.0025 M KSCN solution, NOT 1 M. 1. Wash and dry pipets, test tubes and rubber stoppers. Renumber the test tubes (6 to 10). 2. Prepare the five solutions shown in Table 6.2. Use a properly rinsed Mohr pipet for each solution. Table 6.2 Composition of solutions for determining the equilibrium constant Test Tube 0.0025 M 0.0025 M 0.1 M number Fe(NO3)3 KSCN HNO3 6 1.0 1.0 5.0 7 1.0 2.0 4.0 8 1.0 3.0 3.0 9 2.0 2.0 3.0 Determining k in Beer's Law CAUTION: DO NOT put the pipet directly into the stock bottle. Such practice will change the concentration of the stock solution and make it unusable. Instead, obtain a small beaker to take some stock solution to your bench. 1. Rinse a pipet with a 0.0025 M solution of Fe(NO3)3 (in 0.1 M HNO3). Use this Mohr pipet to transfer exactly 4 mL of the Fe(NO3)3 solution to a 100-ml volumetric flask. Add distilled water till the bottom of the meniscus coincides with the marking on the flask. Use a disposable dropper if needed. Mix the solution thoroughly 2. Label 3 pipets with diluted Fe(NO3)3, 1 M KSCN and 0.1 M HNO3 and rinse with corresponding solutions. 3. Using the pipets, prepare following 5 solutions (Table 6.1) in the 5 cleaned and dried large test tubes. All amounts are given in mL. Table 6.1 Composition of solutions for Beer's Law. Test Tube Diluted 0.1 M number 1 M KSCN Fe(NO3)3 HNO3 10 2.0 3.0 2.0 3. Stopper the tube and mix each solution thoroughly. 4. Measure the absorbance of each solution at 450 nm. Record your data. 5. Calculate the concentration of Fe(SCN)+2 in each solution using the absorbance and the Beer's Law. 6. Calculate the initial concentration of Fe(NO3)3 and KSCN using the table given above. 7. Calculate the final concentration of Fe(NO3)3 and KSCN using the data from step 6 and stoichiometry of the reaction. 8 Calculate the equilibrium constant for each solution. Calculate the mean value of Keq with its standard deviation. 1 1.0 5.0 4.0 2 2.0 5.0 3.0 3 3.0 5.0 2.0 4 4.0 5.0 1.0 9. 5 5.0 5.0 0 Discard all solutions into the proper waste container -40 1. Determining k in Beer's Law 2. Determining the equilibrium constant Test Tube No. Absorbance Test Tube No. Absorbance 1 6 146 291 2. 7 230 3 8 426 945 4 9 -492 1.560 5 10 Post Laboratory Questions 1. Complete the following table and calculate k-value for the Beer's Law. All concentrations should be in M. [Fe(SCN)*2) CA Test Tube No. Absorbance A c? Diluted Fe(NO3)3 (mL) С 0 0 Reference 0 0 0 1 1.0 0.0000140 LxD 2 2.0 6 0000400 3 3.0 0.0001378 4 4.0 .402 (2000 1968 leylo 2000293 2,5 x 10 7 5 5.0 . 5860 Co consume a CoomL) 40.ooorm Fescarta --43 2. Determining the equilibrium constant by filling the following table. Starting [Fe) Starting [SCN) Test Tube No. [Fe(SCN)*2) from A Eq. [Fe+3] Eq. [SCN1 Kea 6 7 6.005 8 0.0035 0.00 B 9 10 6.olas Sample calculation for obtaining initial [Fe+") and (SCN): Fe + SCN Fe (SCN) Sample calculation for obtaining [Fe(SCN)*2; from absorbance: Sample calculation for the equilibrium [Fe3+]: Sample calculation for the equilibrium (SCN): Sample calculation for Ke [Fe (sco) Keq [Fe +3 ] [sc]

Accrued Product Warranty Harbour Company disclosed estimated product warranty payable for comparative years as follows: (in millions) Current Year Prior Year Current estimated product warranty payable $13,350 $12,800 Noncurrent estimated product warranty payable 8,120 7,157 Total $21,470 $19,957 Assume that Harbour's sales were $157,449 million in current Year and that the total paid on warranty claims during the current year was $13,763 million a. The distinction between short- and long-term liabilities is important to creditors in order to accurately evaluate the near-term cash the business relative to the quick current assets and other longer-term b. Provide the journal entry for the current Year product warranty expense. Enter your answers in millions. If an amount box does not require an entry, leave it blank. c. What two conditions must be met in order for a product warranty liability to be reported in the financial statements?

7. = = Fig. Q5 shows a convolutional neural network architecture. We have in the first layer a one-dimensional convolution with a single filter of size 3 such that hi s({}=1 V; Xi+j-1). The second layer is fully connected, such that z = {i=1 W;hi. The hidden units' activation function s(x) is the sigmoid function. The output unit is linear with no activation function. We perform gradient descent on the loss function L = (y – z)2 where y is the training label for x. X2 W h2 W3 h3 W4 ht Fig. Q7 (a) What is the total number of parameters in this neural network? Note that convolutional layers share weights and there are no bias terms. (b) Backpropagation is a widely used algorithm for training feedforward neural networks. Briefly describe how backpropagation works. (c) Compute awi al

Payroll System Using Inheritance and PolymorphismYou need to implement the following harder file and classes. 1. Define the following constants in a header file called Employee.hFACULTY_MONTHLY_SALARY = 5000.00STAFF_MONTHLY_HOURS_WORKED = 1602. Implement an abstract class Employee with the following requirements:Attributeslast name (String)first name (String)ID number (String)Default argument constructor and argument constructors.Public methodsputData that displays the following information:ID Employee number :_________Employee name: __________pure virtual method monthlyEarning that returns the monthly earning. 3. Implement a class called Staff extending from the class Employee with the following requirements:AttributeHourly rateDefault argument and argument contructorsPublic methodsget and setThe method monthlyEarning returns monthly salary (hourly rate times 160)putData that displays the following information:ID Employee number :_________Employee name: __________Full TimeMonthly Salary: _________4. Implement a class Education with the following requirements:AttributesDegree (MS or PhD ) String typeMajor (Engineering, Chemistry, English, etc ... ) String typeResearch (number of researches)Default argument and argument constructors.Public methodsget and setImplement a class Faculty extending from the class Employee with the following requirements:AttributesLevel (use enum statement)"AS": assistant professor"AO": associate professor"FU": professorEducation objectDefault argument and argument constructorPublic methodsget and setThe method monthlyEarning returns monthly salary based on the faculty's level.AS - faculty monthly salaryAO - 1.2 times faculty monthly salaryFU - 1.4 times faculty monthly salaryputData that displays the following information:ID Employee number :_________Employee name: __________XXXXX Professor where XXXXX can be Assistant, Associate or FullMonthly Salary: _________Implement a class called Partime extending from the class Staff with the following requirements:AttributesHours worked per weekDefault argument and argument constructorsPublic methodsset and getThe method monthlyEarning returns monthly salary which hourly rate multiplied hours worked per week multiplied four.putData that displays the following information:ID Employee number :_________Employee name: __________Hours works per month: ______Monthly Salary: _________Implement a test driver program that creates an array or a vector of class Employees to store the objects Staff, Faculty, and Partime.Using polymorphism, display the following outputs:a. Employee information using the method putData. All employees StaffFacultyPart-timeb. Total monthly salary for all employees.Use the following data to create Employee objects in your program. StaffLast name: AllenFirst name: PaitaID: 123Hourly rate: $50.00Last name: ZapataFirst Name: StevenID: 456Hourly rate: $35.00Last name:RiosFirst name:EnriqueID: 789Hourly rate: $40.00FacultyLast name: JohnsonFirst name: AnneID: 243Level: FullDegree: Ph.DMajor: EngineeringReseach: 3Last name: BourisFirst name: WilliamID: 791Level: AssociateDegree: Ph.DMajor: EnglishReseach: 1Last name: AndradeFirst name: ChristopherID: 623Level: AssistantDegree: MSMajor: Physical EducationResearch: 0Part-timeLast name: GuzmanFirst name: AugustoID: 455Hourly rate: $35.00Hours worked per week: 30Last name: DepirroFirst name: MartinID: 678Hourly rate: $30.00Hours worked per week:15Last name: AldacoFirst name: MarqueID: 945Hours worked per week: 35Runtime output ID Employee number: 123Employee name: Paita AllenFull TimeMonthly Salary: 8000ID Employee number: 456Employee name: Steven ZapataFull TimeMonthly Salary: 5600ID Employee number: 789Employee name: Enrique RiosFull TimeMonthly Salary: 6400ID Employee number: 243Employee name: Anne JohnsonFull ProfessorMonthly Salary: 7000ID Employee number: 791Employee name: William BourisAssociate ProfessorMonthly Salary: 6000ID Employee number: 623Employee name: Christopher AndradeAssistant ProfessorMonthly Salary: 5000ID Employee number: 455Employee name: Augusto GuzmanHours work per month: 120Monthly Salary: 4200ID Employee number: 678Employee name: Martin DepirroHours work per month: 60Monthly Salary: 1800ID Employee number: 945Employee name: Marque AldacoHours work per month: 140Monthly Salary: 2800Total monthly salary for all employees: $46800