/*! This file is auto-generated */ .wp-block-button__link{color:#fff;background-color:#32373c;border-radius:9999px;box-shadow:none;text-decoration:none;padding:calc(.667em + 2px) calc(1.333em + 2px);font-size:1.125em}.wp-block-file__button{background:#32373c;color:#fff;text-decoration:none} Q129E Question: Failure times of silic... [FREE SOLUTION] | 91影视

91影视

Question: Failure times of silicon wafer microchips. Refer to the National Semiconductor study of manufactured silicon wafer integrated circuit chips, Exercise 12.63 (p. 749). Recall that the failure times of the microchips (in hours) was determined at different solder temperatures (degrees Celsius). The data are repeated in the table below.

  1. Fit the straight-line modelEy=0+1xto the data, where y = failure time and x = solder temperature.

  2. Compute the residual for a microchip manufactured at a temperature of 149掳C.

  3. Plot the residuals against solder temperature (x). Do you detect a trend?

  4. In Exercise 12.63c, you determined that failure time (y) and solder temperature (x) were curvilinearly related. Does the residual plot, part c, support this conclusion?

Short Answer

Expert verified
  1. From the excel output below, the straight-line model for y on x can be written asEy=30855.91-191.567x.

  2. Residual value; 蔚 = (2312.427 - 1,100) = 1212.427

  3. From the excel output, the residual plot denotes a curvilinear relationship amongst the residuals against solder temperature (x).

  4. From the graph, it can be concluded that there exists a curvilinear relationship between failure time (y) and solder temperature (x) as the residual plot indicates a curvilinear relationship

Step by step solution

01

Straight-line model

From the excel output below, the straight-line model for y on x can be written as Ey= 30855.91 - 191.567x.


02

Prediction value

The residual for a microchip manufactured at a temperature of 149掳C can be computed using

蔚 = y^-y For x = 149, y = 1, 100 and y^= 30855.91 - 191.567149y^= 2312.427

Therefore, 蔚 = 2312.427-1,100= 1212.427

03

Residual plot

From the excel output, the residual plot denotes a curvilinear relationship amongst the residuals against solder temperature (x).

04

Residual plot interpretation

From the graph, it can be concluded that there exists a curvilinear relationship between failure time (y) and solder temperature (x) as the residual plot indicates a curvilinear relationship.

Unlock Step-by-Step Solutions & Ace Your Exams!

  • Full Textbook Solutions

    Get detailed explanations and key concepts

  • Unlimited Al creation

    Al flashcards, explanations, exams and more...

  • Ads-free access

    To over 500 millions flashcards

  • Money-back guarantee

    We refund you if you fail your exam.

Over 30 million students worldwide already upgrade their learning with 91影视!

One App. One Place for Learning.

All the tools & learning materials you need for study success - in one app.

Get started for free

Most popular questions from this chapter

Consider a multiple regression model for a response y, with one quantitative independent variable x1 and one qualitative variable at three levels.

a. Write a first-order model that relates the mean response E(y) to the quantitative independent variable.

b. Add the main effect terms for the qualitative independent variable to the model of part a. Specify the coding scheme you use.

c. Add terms to the model of part b to allow for interaction between the quantitative and qualitative independent variables.

d. Under what circumstances will the response lines of the model in part c be parallel?

e. Under what circumstances will the model in part c have only one response line?

Question: Cooling method for gas turbines. Refer to the Journal of Engineering for Gas Turbines and Power (January 2005) study of a high-pressure inlet fogging method for a gas turbine engine, Exercise 12.19 (p. 726). Consider a model for heat rate (kilojoules per kilowatt per hour) of a gas turbine as a function of cycle speed (revolutions per minute) and cycle pressure ratio. The data are saved in the file.

a. Write a complete second-order model for heat rate (y).

b. Give the null and alternative hypotheses for determining whether the curvature terms in the complete second-order model are statistically useful for predicting heat rate (y).

c. For the test in part b, identify the complete and reduced model.

d. The complete and reduced models were fit and compared using SPSS. A summary of the results are shown in the accompanying SPSS printout. Locate the value of the test statistic on the printout.

e. Find the rejection region for 伪 = .10 and locate the p-value of the test on the printout.

f. State the conclusion in the words of the problem.


Question: There are six independent variables, x1, x2, x3, x4, x5, and x6, that might be useful in predicting a response y. A total of n = 50 observations is available, and it is decided to employ stepwise regression to help in selecting the independent variables that appear to be useful. The software fits all possible one-variable models of the form

E(Y)=0+1xi

where xi is the ith independent variable, i = 1, 2, 鈥, 6. The information in the table is provided from the computer printout.

a. Which independent variable is declared the best one variable predictor of y? Explain.

b. Would this variable be included in the model at this stage? Explain.

c. Describe the next phase that a stepwise procedure would execute.

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鈥 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.

Impact of race on football card values. University of Colorado sociologists investigated the impact of race on the value of professional football players鈥 鈥渞ookie鈥 cards (Electronic Journal of Sociology, 2007). The sample consisted of 148 rookie cards of National Football League (NFL) players who were inducted into the Football Hall of Fame. The price of the card (in dollars) was modeled as a function of several qualitative independent variables: race of player (black or white), card availability (high or low), and player position (quarterback, running back, wide receiver, tight end, defensive lineman, linebacker, defensive back, or offensive lineman).

  1. Create the appropriate dummy variables for each of the qualitative independent variables.
  2. Write a model for price (y) as a function of race. Interpret the鈥檚 in the model.
  3. Write a model for price (y) as a function of card availability. Interpret the鈥檚 in the model.
  4. Write a model for price (y) as a function of position. Interpret the鈥檚 in the model.
See all solutions

Recommended explanations on Math Textbooks

View all explanations

What do you think about this solution?

We value your feedback to improve our textbook solutions.

Study anywhere. Anytime. Across all devices.