Concept explainers
The accompanying data on y 5 energy output (W) and x 5 temperature difference (°K) was provided by the authors of the article “Comparison of Energy and Exergy Efficiency for Solar Box and Parabolic Cookers” (J. of Energy Engr., 2007: 53–62). The article’s authors fit a cubic regression model to the data. Here is Minitab output from such a fit.
x | 23.20 | 23.50 | 23.52 | 24.30 | 25.10 | 26.20 | 27.40 | 28.10 | 29.30 | 30.60 | 31.50 | 32.01 |
y | 3.78 | 4.12 | 4.24 | 5.35 | 5.87 | 6.02 | 6.12 | 6.41 | 6.62 | 6.43 | 6.13 | 5.92 |
x | 32.63 | 33.23 | 33.62 | 34.18 | 35.43 | 35.62 | 36.16 | 36.23 | 36.89 | 37.90 | 39.10 | 41.66 |
y | 5.64 | 5.45 | 5.21 | 4.98 | 4.65 | 4.50 | 4.34 | 4.03 | 3.92 | 3.65 | 3.02 | 2.89 |
a. What proportion of observed variation in energy output can be attributed to the model relationship?
b. Fitting a quadratic model to the data results in R2 = .780. Calculate adjusted R2 for this model and compare to adjusted R2 for the cubic model.
c. Does the cubic predictor appear to provide useful information about y over and above that provided by the linear and quadratic predictors? State and test the appropriate hypotheses.
d. When x = 30,
e. Interpret the hypotheses
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Chapter 13 Solutions
Probability and Statistics for Engineering and the Sciences
- Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?arrow_forwardWhat does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?arrow_forwardA researcher records age in years (x) and systolic blood pressure (y) for volunteers. They perform a regression analysis was performed, and a portion of the computer output is as follows: ŷ = 3.3 +12.7x Coefficients (Intercept) X Estimate Std. Error Test statistic O Ho: B₁: = 0 Ha: B₁ 0 O Ho: B₁ = 0 Ha: B₁ 0 12.7 2.2 6.4 1.5 1.98 P-value Specify the null and the alternative hypotheses that you would use in order to test whether a positive linear relationship exists between x and y. 0.08 0.03arrow_forward
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- Wrinkle recovery angle and tensile strength are the two most important characteristics for evaluating the performance of crosslinked cotton fabric. An increase in the degree of crosslinking, as determined by ester carboxyl band absorbance, improves the wrinkle resistance of the fabric (at the expense of reducing mechanical strength). The accompanying data on x = absorbance and y = wrinkle resistance angle was read from a graph in the paper "Predicting the Performance of Durable Press Finished Cotton Fabric with Infrared Spectroscopy".t x 0.115 0.126 0.183 0.246 0.282 0.344 0.355 0.452 0.491 0.554 0.651 y 334 342 355 363 365 372 381 400 392 412 420 Here is regression output from Minitab: Predictor Constant absorb S = 3.60498 Coef 321.878 156.711 SOURCE Regression Residual Error Total R-Sq= 98.5% DF SE Coef 2.483 6.464 1 9 10 SS 7639.0 117.0 7756..0 T 129.64 24.24 P 0.000 0.000. R-Sq (adj) 98.3% MS 7639.0 13.0 F 587.81 (a) Does the simple linear regression model appear to be appropriate?…arrow_forwardThe flow rate in a device used for air quality measurement depends on the pressure drop x (inches of water) across the device's filter. Suppose that for x values between 5 and 20, these two variables are related according to the simple linear regression model with true regression line y = -0.11 + 0.097x. (a.1) What is the true average flow rate for a pressure drop of 10 in.?(a.2) A drop of 15 in.?(b) What is the true average change in flow rate associated with a 1 inch increase in pressure drop?(c) What is the average change in flow rate when pressure drop decreases by 5 in.?arrow_forward1. (30 pts) We wish to determine a regression equation that relates the length of an infant (in cm) to age (in days), gender and weight at birth (in kg). Below is portion of the regression analysis derived using a software. *Note: under the gender variable: male and female categories are assigned a value of 1 and 0, respectively. Std. Err. 0.0980 Source Coef. Model Residual 316.8866 age weight gender intercept 0.4798 0.4020 1.3113 71.4734 8 1.0454 1.9591 19.53 7.7829 a. What is the sample size for this problem? b. Write the estimated regression equation, interpret each slope coefficients, use the proper unit of measurement. c. Test for significance of the Bage, Bweight, and Bgender at the 99% confidence level. d. From (b) which parameter/s is/are statistically significant. e. Test whether or not there is a significant relationship between the infant's length and the independent variables. Use a .01 level of significance. Use only the critical value approach. f. Provide the Coefficient…arrow_forward
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