Question

Nambe manufactures a line of metal tableware called Nambeware. A piece of Nambeware is produced by sand-casting a special alloy of several metals. After casting, the piece goes through a series of shaping, grinding, buffing, and polishing steps. Nambe is analyzing how to adjust its production schedule to accommodate the polishing step of a new line of casseroles that it is adding to its product line. Nambe selects a random sample of its tableware pieces and collects data on the type of piece (tray, dish, bowl, plate, or casserole), its diameter (in inches), its polishing time (in minutes), and its price (in dollars). [Source: Data and Story Library (DASL).] The sample data are located in the data set Nambeware. Use the DataView tool to answer the following questions Data Set Sample Variables 4 Observations 59 Nambeware Random sample of Nambeware pieces Data and Story Library (DASL) Observations Variables Type V Form V Qualitative Nonnumeric Numeric Numeric Numeric Variable V Values 59 59 59 59 Missing Observations > Type Diameter Quantitative Time Quantitative Price Quantitative Variable Variable Variable Variable Correlation Correlation Let μ equal the mean polishing time of the population of Nambeware casseroles. The point estimate of μ is minutes Hint: The data contained in the DataView tool includes five different types of Nambeware. To filter the data in order to display the data for the pieces in the sample that are casseroles only, click on a Variable sliding panel on the bottom left of the tool and from the dropdown menu, select Type. In the Filter column, uncheck all the types except Casserole. Check that the bottom of the tool display shows Filter: Type Casserole. Then click another Variable sliding panel and from the dropdown menu, select Time. Statistics for the Nambeware pieces that are casseroles are now shown on the tool display in the column labeled Filtered Now consider a particular casserole in the sample, say, casserole 7. Casserole 7 is identified as C07 in the column labeled Piece in the tool's Observation view. The error involved in using the point estimate of μ as the polishing time for casserole 7 is minutes. (Hint: To see individual observations, click on Data Set in the top left side of the tool, click on Data Set. Then click on the Observations button to switch from Variables view to Observations view.) Nambe uses the 10 pieces in its sample that are casseroles to estimate the simple linear regression model that predicts the polishing time from the casserole's diameter: where ying time of a casserole (in minutes) and xthe casserole's diameter (in inches) The estimated regression equation is y45.83 0.60x Using the estimated regression equation, the estimated mean polishing time for casseroles with a 10-inch diameter is minutes. Consider the contribution of a particular sample observation-say casserole 7-to the SST, SSR, and SSE. Casserole 7's contribution to the SST is , its contribution to the SSR is , and its contribution to the SSE is In this regression analysis, the SSR is 11.9, and the SSE is 2,417.3. What percentage of the variability in the polishing time of Nambeware casseroles can be explained by the estimated regression equation? o 27.2% o 7% 0 0.0049% o 0.5% Use the convention from the social sciences regarding the usefulness of the estimated regression equation based on its r2. The Nambeware managers new line of casseroles. be confident using the estimated regression equation to predict the polishing times for the The sample correlation coefficient rxy is

Answer 1

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