Question: Estimating change-point dosage. A standard method for studying toxic substances and their effects on humans is to observe the responses of rodents exposed to various doses of the substance over time. In the Journal of Agricultural, Biological, and Environmental Statistics (June 2005), researchers used least squares regression to estimate the change-point dosage—defined as the largest dose level that has no adverse effects. Data were obtained from a dose-response study of rats exposed to the toxic substance aconiazide. A sample of 50 rats was evenly divided into five dosage groups: 0, 100, 200, 500, and 750 milligrams per kilogram of body weight. The dependent variable y measured was the weight change (in grams) after a 2-week exposure. The researchers fit the quadratic model E(y)=β0+β1x+β2x2, where x = dosage level, with the following results:

y^==10.25+0.0053x-0.0000266x2

  1. Construct a rough sketch of the least square’s prediction equation. Describe the nature of the curvature in the estimated model.
  2. Estimate the weight change (y) for a rat given a dosage of 500 mg/kg of aconiazide.
  3. Estimate the weight change (y) for a rat given a dosage of 0 mg/kg of aconiazide. (This dosage is called the control dosage level.)
  4. Of the five dosage groups in the study, find the largest dosage level x that yields an estimated weight change that is closest to but below the estimated weight change for the control group. This value is the change-point dosage.

Short Answer

Expert verified

Answer

  1. Graph
  2. The change in weight is 6.25 gm when the rat is given a dosage of 500 gm.
  3. The change in weight is 10.25 gm when the rat is given a dosage of 0 gm.
  4. x = 99 is the change point level of dosage since the weight change (10.513) is also closest but less than the estimated weight change for the control group.

Step by step solution

01

Given Information

A sample of 50 rats was drawn which was evenly divided into five dosage groups: 0, 100, 200, 500, and 750 milligrams per kilogram of body weight. The quadratic model is given asE(y)=β0+β1x+β2x2 where y is the dependent variable (here, weight change after two-week exposure) and x is theindependent variable (here, dosage level) and the estimated model is given asy^=10.25+0.0053x-0.0000266x2

02

Graph for least square prediction equation 

a.

The given equation:
y^=10.25+0.0053x-0.0000266x2

Putting the value of x (dosage level) in the above equation we will get 5 different values of y (the weight change).Using these values the graph below is drawn.

Here the graph for the x2 prediction equation will be a curvature to account for the term and since the sign of coefficient of x2 term is negative the curvature will be downward sloping curve.

03

Estimation for y

b.

The weight change for a rat given a dosage of x = 500 gm is:

y^=10.25+0.0053x-0.0000266x2y^=10.25+0.0053(500)-0.0000266(500)2 y^=6.25

So, the change in weight is 6.25 gm.

04

Control dosage level

The weight change for a rat given a dosage of x = 0 gm is

y^=10.25+0.0053x-0.0000266x2y^=10.25+0.0053(0)-0.0000266(0)2 y^=10.25

Hence, the change in weight is 10.25 gm.

05

Change-point dosage level

d.

For the dosage level, x = 99 , the weight change for a rat is:

y^=10.25+0.0053x-0.0000266x2y^=10.25+0.0053(99)-0.0000266(99)2 y^=10.51399

For x = 100 (control group) the weight change is 10.514.

Therefore, x = 99 is the change point level of dosage since the weight change (10.513) is also closest but less than the estimated weight change for the control group.

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Most popular questions from this chapter

Question: Revenues of popular movies. The Internet Movie Database (www.imdb.com) monitors the gross revenues for all major motion pictures. The table on the next page gives both the domestic (United States and Canada) and international gross revenues for a sample of 25 popular movies.

  1. Write a first-order model for foreign gross revenues (y) as a function of domestic gross revenues (x).
  2. Write a second-order model for international gross revenues y as a function of domestic gross revenues x.
  3. Construct a scatterplot for these data. Which of the models from parts a and b appears to be the better choice for explaining the variation in foreign gross revenues?
  4. Fit the model of part b to the data and investigate its usefulness. Is there evidence of a curvilinear relationship between international and domestic gross revenues? Try usingα=0.05.
  5. Based on your analysis in part d, which of the models from parts a and b better explains the variation in international gross revenues? Compare your answer with your preliminary conclusion from part c.

The first-order model E(y)=β0+β1x1was fit to n = 19 data points. A residual plot for the model is provided below. Is the need for a quadratic term in the model evident from the residual plot? Explain.


Role of retailer interest on shopping behavior. Retail interest is defined by marketers as the level of interest a consumer has in a given retail store. Marketing professors investigated the role of retailer interest in consumers’ shopping behavior (Journal of Retailing, Summer 2006). Using survey data collected for n = 375 consumers, the professors developed an interaction model for y = willingness of the consumer to shop at a retailer’s store in the future (called repatronage intentions) as a function of = consumer satisfaction and = retailer interest. The regression results are shown below.

(a) Is the overall model statistically useful for predicting y? Test using a=0.05

(b )Conduct a test for interaction at a= 0.05.

(c) Use the estimates to sketch the estimated relationship between repatronage intentions (y) and satisfaction when retailer interest is x2=1 (a low value).

(d)Repeat part c when retailer interest is x2= 7(a high value).

(e) Sketch the two lines, parts c and d, on the same graph to illustrate the nature of the interaction.

Consider relating E(y) to two quantitative independent variables x1 and x2.

  1. Write a first-order model for E(y).

  2. Write a complete second-order model for E(y).

Suppose the mean value E(y) of a response y is related to the quantitative independent variables x1and x2

E(y)=2+x1-3x2-x1x2

a) Identify and interpret the slope forx2

b) Plot the linear relationship between E(y) andx2for role="math" localid="1649796003444" x1=0,1,2, whererole="math" localid="1649796025582" 1x23

c) How would you interpret the estimated slopes?

d) Use the lines you plotted in part b to determine the changes in E(y) for eachrole="math" localid="1649796051071" x1=0,1,2.

e) Use your graph from part b to determine how much E(y) changes whenrole="math" localid="1649796075921" 3x15androle="math" localid="1649796084395" 1x23.

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