In an experiment to determine the factors affecting tensile strength in steel plates, the tensile strength (in kg/mm?), the manganese content (in parts per thousand), and the thickness (in mm) were measured for a sample of 20 plates. The following MINITAB output presents the results of fitting the model Tensile strength = Bo + B1 Manganese + B2 Thickness. The regression equation is Strength = 26.641 + 3.3201 Manganese – 0.4249 Thickness Predictor Coef StDev т Constant 26.6412.72340 9.78 0.000 Manganese 3.32010.33198 10.00 0.000 Thickness -0.42490.12606 -3.37 0.004 S = 0.8228 R-Sq = 86.2% R-Sq(adj) = 84.6% Analysis of Variance Source DF MS Regression Residual Error 1711.508 0.6769 2 72.0136.00553.190.000 Total 1983.517 Find a 95% confidence interval for the coefficient of Manganese. b. a. Find a 99% confidence interval for the coefficient of Thickness. Can you conclude that B1 > 3? Perform the appropriate hypothesis test. d. Can you conclude that B2 < -0.1? Perform the appropriate hypothesis test. C.

In an experiment to determine the factors affecting tensile strength in steel plates, the tensile strength (in kg/mm?), the manganese content (in parts per thousand), and the thickness (in mm) were measured for a sample of 20 plates. The following MINITAB output presents the results of fitting the model Tensile strength = Bo + B1 Manganese + B2 Thickness. The regression equation is Strength = 26.641 + 3.3201 Manganese – 0.4249 Thickness Predictor Coef StDev т Constant 26.6412.72340 9.78 0.000 Manganese 3.32010.33198 10.00 0.000 Thickness -0.42490.12606 -3.37 0.004 S = 0.8228 R-Sq = 86.2% R-Sq(adj) = 84.6% Analysis of Variance Source DF MS Regression Residual Error 1711.508 0.6769 2 72.0136.00553.190.000 Total 1983.517 Find a 95% confidence interval for the coefficient of Manganese. b. a. Find a 99% confidence interval for the coefficient of Thickness. Can you conclude that B1 > 3? Perform the appropriate hypothesis test. d. Can you conclude that B2 < -0.1? Perform the appropriate hypothesis test. C.

College Algebra

7th Edition

ISBN:9781305115545

Author:James Stewart, Lothar Redlin, Saleem Watson

Publisher:James Stewart, Lothar Redlin, Saleem Watson

Chapter1: Equations And Graphs

Section: Chapter Questions

Problem 10T: Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s...

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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?
Suppose the following data were collected from a sample of 15 houses relating selling price to square footage and the architectural style of the house. Use statistical software to find the following regression equation: PRICEi=b0+b1SQFTi+b2COLONIALi+b3RANCHi+ei . Is there enough evidence to support the claim that on average, houses that are ranch style have lower selling prices than houses that are Victorian style at the 0.05 level of significance? If yes, write the regression equation in the spaces provided with answers rounded to two decimal places. Else, select "There is not enough evidence."Selling Price Square Footage Colonial (1 if house is Colonial style, 0 otherwise) Ranch (1 if house is Ranch style, 0 otherwise) Victorian (1 if house is Victorian style, 0 otherwise) 377640 1941 1 0 0 460996 3397 0 1 0 405781 2764 0 0 1 407216 2906 0 0 1 435139 3401 1 0 0 405275 2600 0 0 1 381141 2203 0 1 0 370490 2046 1 0 0 404070 2210 0 0 1 460196 3692 0 1 0 382780 2172 1 0 0 406466 2606 0 1…
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A researcher interested in explaining the level of foreign reserves for the country of Barbadosestimated the following multiple regression model using yearly data spanning the period 2001 to 2016: ??=?+????+????+????Where FR = yearly foreign reserves ($000’s), OIL = annual oil prices, EXP = yearly total exports ($000’s) and FDI = annual foreign direct investment ($000’s). The sample of data was processed using MINITAB and the following is an extract of the output obtained:Predictor Coef StDev t-ratio p-value Constant 5491.38 2508.81 2.1888 0.0491OIL 85.39 18.46 4.626 0.0006EXP -377.08 112.19 * 0.0057FDI -396.99 160.66 -2.471 ** S = 2.45 R-sq = 96.3% R-sq(adj) = 95.3%Analysis of VarianceSource DF SS MS F pRegression 3 1991.31 663.77 ? ??Error 12 77.4 6.45Total 15a) What is dependent and independent variables? b) Fully write out the regression equation [1] c) Fill in the missing values ‘*’, ‘**’, ‘?’and ‘??’
A researcher interested in explaining the level of foreign reserves for the country of Barbadosestimated the following multiple regression model using yearly data spanning the period 2001 to 2016: ??=?+????+????+????Where FR = yearly foreign reserves ($000’s), OIL = annual oil prices, EXP = yearly total exports ($000’s) and FDI = annual foreign direct investment ($000’s). The sample of data was processed using MINITAB and the following is an extract of the output obtained: Predictor Coef StDev t-ratio p-value Constant 5491.38 2508.81 2.1888 0.0491OIL 85.39 18.46 4.626 0.0006EXP -377.08 112.19 * 0.0057FDI -396.99 160.66 -2.471 ** S = 2.45 R-sq = 96.3% R-sq(adj) = 95.3% Analysis of VarianceSource DF SS MS F pRegression 3 1991.31…