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Suppose you fit the second-order model y=β0+β1x+β2x2+εto n = 25 data points. Your estimate ofβ2isβ^2= 0.47, and the estimated standard error of the estimate is 0.15.

  1. TestH0:β2=0againstHa:β2≠0. Useα=0.05.
  2. Suppose you want to determine only whether the quadratic curve opens upward; that is, as x increases, the slope of the curve increases. Give the test statistic and the rejection region for the test forα=0.05. Do the data support the theory that the slope of the curve increases as x increases? Explain.

Short Answer

Expert verified
  1. At 95% significance level, it can be concluded that β2≠0.
  2. At 95% significance level, β2>0. There is sufficient evidence to conclude that the slope of the curve increases as x increases.

Step by step solution

01

Significance of β2

H0:β2=0Ha:β2≠0

Here, t-test statistic=β^2sβ^2=0.470.15=3.133

Value of t0.05,24is 1.711.

H0is rejected iftstatistics >t0.05,24. Forα=0.05since t >t0.05,24

Sufficient evidence to reject H0at 95% significance level.

Therefore,β2≠0

02

Signification of β2

To check whether the quadratic curve opens upwards, that is as x increases, the slope of the curve also increases, the hypotheses would be

H0:β2=0againstHa:β2>0

The t-test statistic would beb^2sb^2=3.133

H0is rejected when t statistic > t0.05,24. For α=0.05, the value of t0.05,24is 1.711

Since t > t0.05,24, there is sufficient evidence to reject H0at a 95% significance level.

Thus,localid="1649833131364" β2>0. There is sufficient evidence to conclude that the slope of the curve increases as x increases.

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Most popular questions from this chapter

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: 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.

Consider the model:

E(y)=β0+β1x1+β2x2+β3x22+β4x3+β5x1x22

where x2 is a quantitative model and

x1=(1receivedtreatment0didnotreceivetreatment)

The resulting least squares prediction equation is

localid="1649802968695" yÁåœ=2+x1-5x2+3x22-4x3+x1x22

a. Substitute the values for the dummy variables to determine the curves relating to the mean value E(y) in general form.

b. On the same graph, plot the curves obtained in part a for the independent variable between 0 and 3. Use the least squares prediction equation.

Question: Write a regression model relating E(y) to a qualitative independent variable that can assume three 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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