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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.

Short Answer

Expert verified

With 90% accuracy, it can be concluded that the mean DDT level of fish will be between the interval (3.8650, 33.8956)

Step by step solution

01

Step-by-Step SolutionStep 1: Confidence interval interpretation

The 90% confidence interval for DDT level of fish caught 300 miles upstream with a length of 40 cm and weight of 1000 grams is given in the image as (3.8650, 33.8956). This means that with 90% accuracy, it can be concluded that the mean DDT level of fish will be between the interval (3.8650, 33.8956)

02

Predicted value interpretation

The predicted value of the DDT level of fish calculated here is also 18.8803 which lies within the interval indicating that the predicted value is close to the actual value with 90% accuracy.

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Most popular questions from this chapter

Question: Ambiance of 5-star hotels. Although invisible and intangible, ambient conditions such as air quality , temperature , odor/aroma , music , noise level , and overall image may affect guests’ satisfaction with their stay at a hotel. A study in the Journal of Hospitality Marketing & Management (Vol. 24, 2015) was designed to assess the effect of each of these ambient factors on customer satisfaction with the hotel . Using a survey, researchers collected data for a sample of 422 guests at 5-star hotels. All variables were measured as an average of several 5-point questionnaire responses. The results of the multiple regression are summarized in the table on the next page.

  1. Write the equation of a first-order model for hotel image as a function of the six ambient conditions.
  2. Give a practical interpretation of each of the b-estimates shown.
  3. A 99% confidence interval for is (.350, .576). Give a practical interpretation of this result.
  4. Interpret the value of adjusted .
  5. Is there sufficient evidence that the overall model is statistically useful for predicting hotel image ? Test using a = .01.

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.
  2. Does it appear that a second-order model might better explain the variation in demand than a first-order model? Explain.
  3. Fit a first-order model to these data.
  4. Fit a second-order model to these data.
  5. Compare the results in parts c and d and decide which model better explains variation in demand. Justify your choice.

Question: Estimating repair and replacement costs of water pipes. Refer to the IHS Journal of Hydraulic Engineering (September, 2012) study of the repair and replacement of water pipes, Exercise 11.21 (p. 655). Recall that a team of civil engineers used regression analysis to model y = the ratio of repair to replacement cost of commercial pipe as a function of x = the diameter (in millimeters) of the pipe. Data for a sample of 13 different pipe sizes are reproduced in the accompanying table. In Exercise 11.21, you fit a straight-line model to the data. Now consider the quadratic model,E(y)=β0+β1x+β2x2. A Minitab printout of the analysis follows (next column).

  1. Give the least squares prediction equation relating ratio of repair to replacement cost (y) to pipe diameter (x).
  2. Conduct a global F-test for the model usingα=0.01. What do you conclude about overall model adequacy?
  3. Evaluate the adjusted coefficient of determination,Ra2, for the model.
  4. Give the null and alternative hypotheses for testing if the rate of increase of ratio (y) with diameter (x) is slower for larger pipe sizes.
  5. Carry out the test, part d, using α=0.01.
  6. Locate, on the printout, a 95% prediction interval for the ratio of repair to replacement cost for a pipe with a diameter of 240 millimeters. Interpret the result.

Question: Write a first-order model relating to

  1. Two quantitative independent variables.
  2. Four quantitative independent variables.
  3. Five quantitative independent variables.

Question: Predicting elements in aluminum alloys. Aluminum scraps that are recycled into alloys are classified into three categories: soft-drink cans, pots and pans, and automobile crank chambers. A study of how these three materials affect the metal elements present in aluminum alloys was published in Advances in Applied Physics (Vol. 1, 2013). Data on 126 production runs at an aluminum plant were used to model the percentage (y) of various elements (e.g., silver, boron, iron) that make up the aluminum alloy. Three independent variables were used in the model: x1 = proportion of aluminum scraps from cans, x2 = proportion of aluminum scraps from pots/pans, and x3 = proportion of aluminum scraps from crank chambers. The first-order model, , was fit to the data for several elements. The estimates of the model parameters (p-values in parentheses) for silver and iron are shown in the accompanying table.

(A) Is the overall model statistically useful (at α = .05) for predicting the percentage of silver in the alloy? If so, give a practical interpretation of R2.

(b)Is the overall model statistically useful (at a = .05) for predicting the percentage of iron in the alloy? If so, give a practical interpretation of R2.

(c)Based on the parameter estimates, sketch the relationship between percentage of silver (y) and proportion of aluminum scraps from cans (x1). Conduct a test to determine if this relationship is statistically significant at α = .05.

(d)Based on the parameter estimates, sketch the relationship between percentage of iron (y) and proportion of aluminum scraps from cans (x1). Conduct a test to determine if this relationship is statistically significant at α = .05.

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