/*! 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} Q.68E Question: The Excel printout bel... [FREE SOLUTION] | 91影视

91影视

Question: The Excel printout below resulted from fitting the following model to n = 15 data points: y=0+1x1+2x2+

Where,

x1=(1iflevel20ifnot)x2=(1iflevel30ifnot)

Short Answer

Expert verified

Answer:

  1. From the excel printout, the coefficient values can be used to write the least square prediction equation for the model. Here,y^=80+16.8x1+40.4x2+
  2. 1and2denotes the difference between the mean levels for different dummy variables. This means that1=2-1while2=3-1
  3. Here, the null hypothesis becomes that the means for the three groups are equal meaning1=2=3while the alternate hypothesis implies that at least two of the three means 123differ
  4. At 95% confidence level,120 Hence two of the three means differ in the model.

Step by step solution

01

Least squares prediction equation

From the excel printout, the values of the coefficients can be used to write the least square prediction equation for the model

Here,y^=80+16.8x1+40.4x2+

02

Interpretation of   β1 and β2

1and2 denotes difference between the mean levels for different dummy variables.

This means 1=2-1while2=3-1

03

Simplification of hypothesis

H0:1=2=0

Ha:At least one of parameters1 and2 differs from 0

Here, the null hypothesis becomes that the means for the three groups are equal meaning 1=2=3while the alternate hypothesis implies that at least two of the three means role="math" localid="1649851966245" (渭1,2and3)differ

04

Hypothesis testing

H0:1=2=0

Ha:At least one of parameters1or2is non zero

Here, F test statistic=SSEn-k+1=24.72and the p-value is 0

H0is rejected ifP-value<aFor=0.05since p- value is less than 0.05

Sufficient evidence to rejectH0at 95% confidence interval.

Therefore, 120Hence two of the three means differ in the model

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

Question: Women in top management. Refer to the Journal of Organizational Culture, Communications and Conflict (July 2007) study on women in upper management positions at U.S. firms, Exercise 11.73 (p. 679). Monthly data (n = 252 months) were collected for several variables in an attempt to model the number of females in managerial positions (y). The independent variables included the number of females with a college degree (x1), the number of female high school graduates with no college degree (x2), the number of males in managerial positions (x3), the number of males with a college degree (x4), and the number of male high school graduates with no college degree (x5). The correlations provided in Exercise 11.67 are given in each part. Determine which of the correlations results in a potential multicollinearity problem for the regression analysis.

  1. The correlation relating number of females in managerial positions and number of females with a college degree: r =0.983.

  2. The correlation relating number of females in managerial positions and number of female high school graduates with no college degree: r =0.074.

  3. The correlation relating number of males in managerial positions and number of males with a college degree: r =0.722.

  4. The correlation relating number of males in managerial positions and number of male high school graduates with no college degree: r =0.528.

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)=0+1x1+2x2+3x3 , 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.

Production technologies, terroir, and quality of Bordeaux wine. In addition to state-of-the-art technologies, the production of quality wine is strongly influenced by the natural endowments of the grape-growing region鈥攃alled the 鈥渢erroir.鈥 The Economic Journal (May 2008) published an empirical study of the factors that yield a quality Bordeaux wine. A quantitative measure of wine quality (y) was modeled as a function of several qualitative independent variables, including grape-picking method (manual or automated), soil type (clay, gravel, or sand), and slope orientation (east, south, west, southeast, or southwest).

  1. Create the appropriate dummy variables for each of the qualitative independent variables.
  2. Write a model for wine quality (y) as a function of grape-picking method. Interpret the鈥檚 in the model.
  3. Write a model for wine quality (y) as a function of soil type. Interpret the鈥檚 in the model.
  4. Write a model for wine quality (y) as a function of slope orientation. Interpret the鈥檚 in the model.

Question: 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) andx2forx1=0,1,2, where.

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 each x1=0,1,2.

e. Use your graph from part b to determine how much E(y) changes when3x15and1x23.

Question: Failure times of silicon wafer microchips. Researchers at National Semiconductor experimented with tin-lead solder bumps used to manufacture silicon wafer integrated circuit chips (International Wafer-Level Packaging Conference, November 3鈥4, 2005). The failure times of the microchips (in hours) were determined at different solder temperatures (degrees Celsius). The data for one experiment are given in the table. The researchers want to predict failure time (y) based on solder temperature (x).

  1. Construct a scatterplot for the data. What type of relationship, linear or curvilinear, appears to exist between failure time and solder temperature?
  2. Fit the model,E(y)=0+1x+2x2, to the data. Give the least-squares prediction equation.
  3. Conduct a test to determine if there is upward curvature in the relationship between failure time and solder temperature. (use.=0.05)
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.