Question: Glass as a waste encapsulant. Because glass is not subject to radiation damage, encapsulation of waste in glass is considered to be one of the most promising solutions to the problem of low-level nuclear waste in the environment. However, chemical reactions may weaken the glass. This concern led to a study undertaken jointly by the Department of Materials Science and Engineering at the University of Florida and the U.S. Department of Energy to assess the utility of glass as a waste encapsulant. Corrosive chemical solutions (called corrosion baths) were prepared and applied directly to glass samples containing one of three types of waste (TDS-3A, FE, and AL); the chemical reactions were observed over time. A few of the key variables measured were

y = Amount of silicon (in parts per million) found in solution at end of experiment. (This is both a measure of the degree of breakdown in the glass and a proxy for the amount of radioactive species released into the environment.)

x1 = Temperature (°C) of the corrosion bath

x2 = 1 if waste type TDS-3A, 0 if not

x3 = 1 if waste type FE, 0 if not

(Waste type AL is the base level.) Suppose we want to model amount y of silicon as a function of temperature (x1) and type of waste (x2, x3).

a. Write a model that proposes parallel straight-line relationships between amount of silicon and temperature, one line for each of the three waste types.

b. Add terms for the interaction between temperature and waste type to the model of part a.

c. Refer to the model of part b. For each waste type, give the slope of the line relating amount of silicon to temperature.

e. Explain how you could test for the presence of temperature–waste type interaction.

Short Answer

Expert verified

Answer

a. A model that proposes parallel straight-line relationships between amount of silicon and temperature can be written asy=β0+β1x1+β2x2+β3x3.

b. A model that proposes relationships between amount of silicon and temperature and waste types with interaction between temperature and waste types can be written as y=β0+β1x1+β2x2+β3x3+β4x1x2+β5x1x3.

c. For AL waste type, the slope of the line will be β1. For TSA-3A waste type, the slope of the line will be localid="1651563388536" (β1+β4).For FE waste type, the slope of the line will be(β1+β5)..

d. The presence of temperature-waste type interaction can be tested by doing hypothesis testing on β parameters indicating interaction terms.

Step by step solution

01

Model 

A model that proposes parallel straight-line relationships between amount of silicon and temperature can be indicated by a model where there is no interaction amongst the variables in the model.

Mathematically, it can be written as y=β0+β1x1+β2x2+β3x3.

02

Interaction model

A model that proposes relationships between amount of silicon and temperature and waste types with interaction between temperature and waste types can be written asy=β0+β1x1+β2x2+β3x3+β4x1x2+β5x1x3.

03

Slope of the line for each waste type

For the three waste types; TDS-3A, FE, and AL, two variables are introduced in the model x2 for TSA-3A and x3 for FE. Therefore, Al indicates the base levels.

For AL waste type, the slope of the line will be

y=β0+β1x1+β2x2+β3x3+β4x1x2+β5x1x3y=β0+β1x1+β2(0)+β3(0)+β4x1(0)+β5(0)y=β0+β1x1

The slope of the line is β1.

For TSA-3A waste type, the slope of the line will be

role="math" localid="1651554382496" y=β0+β1x1+β2x2+β3x3+β4x1x2+β5x1x3y=β0+β1x1+β2(1)+β3(0)+β4x1(0)+β5x1(0)forx2=1,andx3=0y=(β0+β2)+(β1+β4)x1

Theslopeofthelineis(β1+β4).

For FE waste type, the slope of the line will be

y=β0+β1x1+β2x2+β3x3+β4x1x2+β5x1x3y=β0+β1x1+β2(1)+β3(0)+β4x1(0)+β5x1(1)forx2=0,andx3=1y=(β0+β3)+(β1+β5)x1

Theslopeofthelineis(β1+β5).

04

Hypotheses testing

The presence of temperature-waste type interaction can be tested by doing hypothesis testing on β parameters indicating interaction terms.

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Most popular questions from this chapter

Question: Orange juice demand study. A chilled orange juice warehousing operation in New York City was experiencing too many out-of-stock situations with its 96-ounce containers. To better understand current and future demand for this product, the company examined the last 40 days of sales, which are shown in the table below. One of the company’s objectives is to model demand, y, as a function of sale day, x (where x = 1, 2, 3, c, 40).

  1. Construct a scatterplot for these data.
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  3. Fit a first-order model to these data.
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Question: Write a regression model relating the mean value of y to a qualitative independent variable that can assume two levels. Interpret all the terms in the model.

Question: Chemical plant contamination. Refer to Exercise 12.18 (p. 725) and the U.S. Army Corps of Engineers study. You fit the first-order model,E(Y)=β0+β1x1+β2x2+β3x3 , to the data, where y = DDT level (parts per million),X1= number of miles upstream,X2= length (centimeters), andX3= weight (grams). Use the Excel/XLSTAT printout below to predict, with 90% confidence, the DDT level of a fish caught 300 miles upstream with a length of 40 centimeters and a weight of 1,000 grams. Interpret the result.

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E(y)=β0+β1x1+β2x2+β3x22+β4x3+β5x1x22

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x1=(1receivedtreatment0didnotreceivetreatment)

The resulting least squares prediction equation is

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State casket sales restrictions. Some states permit only licensed firms to sell funeral goods (e.g., caskets, urns) to the consumer, while other states have no restrictions. States with casket sales restrictions are being challenged in court to lift these monopolistic restrictions. A paper in the Journal of Law and Economics (February 2008) used multiple regression to investigate the impact of lifting casket sales restrictions on the cost of a funeral. Data collected for a sample of 1,437 funerals were used to fit the model. A simpler version of the model estimated by the researchers is E(y)=β0+β1x1+β2x2+β3x1x2, where y is the price (in dollars) of a direct burial, x1 = {1 if funeral home is in a restricted state, 0 if not}, and x2 = {1 if price includes a basic wooden casket, 0 if no casket}. The estimated equation (with standard errors in parentheses) is:

y^=1432 + 793x1- 252x2+ 261x1x2, R2= 0.78

(70) (134) (109)

  1. Calculate the predicted price of a direct burial with a basic wooden casket at a funeral home in a restricted state.

  2. The data include a direct burial funeral with a basic wooden casket at a funeral home in a restricted state that costs \(2,200. Assuming the standard deviation of the model is \)50, is this data value an outlier?

  3. The data also include a direct burial funeral with a basic wooden casket at a funeral home in a restricted state that costs \(2,500. Again, assume that the standard deviation of the model is \)50. Is this data value an outlier?

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