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Games Won (y) 10 11 11 13 10 11 10 11 4 2 7 10 9 9

Teams Detroit Green Bay Houston Kansas City Miami New Orleans New York Giants New York Jets Philadelphia St. Louis San Diego San Francisco Seattle Tampa Bay

Public Shared Function ExecuteDataSet( _ ByVal cn As SqlConnection, _ ByVal cmdText As String) As DataSet Return ExecuteDataSet( _ cn, CommandType.Text, cmdText, Nothing) End Function

Games Won (y) 6 5 5 5 6 4 3 3 4 10 6 8 2 0

(a) Calculate the least squares estimates of the slope and intercept. What is the estimate of 2 Graph the regression model. (b) Find an estimate of the mean number of games won if the opponents can be limited to 1800 yards rushing. (c) What change in the expected number of games won is associated with a decrease of 100 yards rushing by an opponent (d) To increase by 1 the mean number of games won, how much decrease in rushing yards must be generated by the defense (e) Given that x 1917 yards (Cincinnati), nd the tted value of y and the corresponding residual. 11-5. An article in Technometrics by S. C. Narula and J. F. Wellington ( Prediction, Linear Regression, and a Minimum Sum of Relative Errors, Vol. 19, 1977) presents data on the selling price and annual taxes for 24 houses. The data are shown in the following table. (a) Assuming that a simple linear regression model is appropriate, obtain the least squares t relating selling price to taxes paid. What is the estimate of 2 Taxes Sale (Local, School), Price/1000 County)/1000 25.9 29.5 27.9 25.9 29.9 29.9 30.9 28.9 35.9 31.5 31.0 30.9 4.9176 5.0208 4.5429 4.5573 5.0597 3.8910 5.8980 5.6039 5.8282 5.3003 6.2712 5.9592 Taxes Sale (Local, School), Price/1000 County)/1000 30.0 36.9 41.9 40.5 43.9 37.5 37.9 44.5 37.9 38.9 36.9 45.8 5.0500 8.2464 6.6969 7.7841 9.0384 5.9894 7.5422 8.7951 6.0831 8.3607 8.1400 9.1416

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Testing the DataComponent Before adding DataComponent to the Global Assembly Cache, the assembly is tested by rewriting the ProductList.aspx page. After the DataComponent is working, you add it to the Global Assembly Cache, and add the Registry entry to expose the DataComponent in the .NET tab of the add references. 1. Right-click the Inventory project in the Solution Explorer, and click Set As Startup Project. 2. Right-click the ProductList.aspx page, and click Set As Start Page. 3. Right-click the References node in the Inventory project, and click Add Reference. Add a reference to the DataComponent project. 4. Open the ProductList.aspx.vb code-behind page. Add the Imports System.Data.SqlData directive to the top of the page. 5. Notice that the BindProducts method has an if statement that checks to see whether the session variable called Products exists. If the session variable does not exist, an ArrayList is populated and assigned to the session variable. 6. Comment the code that is inside the if statement. 7. Inside the if statement, add code to create a DataSet called Products, and assign it to the session variable called Products. The data is retrieved from the Products table of the Northwind database. Be sure to use the new DataComponent. Your BindProducts method should look like the following:

(b) Find the mean selling price given that the taxes paid are x 7.50. (c) Calculate the tted value of y corresponding to x 5.8980. Find the corresponding residual. (d) Calculate the tted yi for each value of xi used to t the model. Then construct a graph of yi versus the corresponding observed value yi and comment on what this plot would look like if the relationship between y and x was a deterministic (no random error) straight line. Does the plot actually obtained indicate that taxes paid is an effective regressor variable in predicting selling price 11-6. The number of pounds of steam used per month by a chemical plant is thought to be related to the average ambient temperature (in F) for that month. The past year s usage and temperature are shown in the following table: Month Temp. Usage/1000 Jan. Feb. Mar. Apr. May June 21 24 32 47 50 59 185.79 214.47 288.03 424.84 454.58 539.03 Month Temp. Usage/1000 July Aug. Sept. Oct. Nov. Dec. 68 74 62 50 41 30 621.55 675.06 562.03 452.93 369.95 273.98

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