# Distributed Computing

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1. List the sequence of steps taken by a server when an RPC call message arrives. At what point are data values converted from the external representation to native representation?

2. How would you pass a linked list (pointers and nodes) as an argument to a remote procedure?

3. Explain the role of the registry in RMI

4. Consider matrix multiplication as a remote operation. Suppose the local machine does the I/O and a remote server does the multiplication. Why would we choose to divide the problem in this way?

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#### Solution Summary

Distributed Computing is featured.

Statistics: factors in the process of distribution

Problem 13.5 A mail-order catalog business selling personal computer supplies, software, and hardware maintains a centralized warehouse. Management is currently examining the process of distribution from the warehouse and wants to study the factors that affect warehouse distribution costs. Currently, a small handling fee is added to each order, regardless of the amount of the order. Data collected over the past 24 months indicate that the warehouse distribution costs (in thousands of dollars), the sales (in thousands of dollars), and the number of orders. (WARECOST.xls)

Cost Sales Orders

52.95 386 4015

71.66 446 3806

85.58 512 5309

63.69 401 4262

72.81 457 4296

68.44 458 4097

52.46 301 3213

70.77 484 4809

82.03 517 5237

74.39 503 4732

70.84 535 4413

54.08 353 2921

62.98 372 3977

72.3 328 4428

58.99 408 3964

79.38 491 4582

94.44 527 5582

59.74 444 3450

90.5 623 5079

93.24 596 5735

69.33 463 4269

53.71 389 3708

89.18 547 5387

66.8 415 4161

a. State the multiple regression equation.

b. Interpret the meaning of the slopes B1 & B2 in this problem..

c. Explain why the regression coefficient B0 has no practical meaning in the context of this problem.

d. Predict the average monthly warehouse distribution cost when sales are $400,000 and the number of orders is 4,500.

e. Set up a 95% confidence interval estimate for the average monthly distribution cost when sales are $400,000 and the number of orders is 4,500.

f. Set up a 95% prediction interval estimate for the average monthly distribution cost at a particular warehouse when sales are $400,000 and the number of orders is 4,500

g. Determine the coefficient of multiple determination r2 (y12) and interpret its meaning

h. Determine the adjusted r2

Problem 13.9 In problem 13.5 (above), sales and number of orders were used to predict distribution costs at a mail order catalog business. Using the computer output you obtained to solve that problem.

a. Perform a residual analysis on your results and determine the adequacy of fit of the model.

b. Plot the residuals against the months. Is there any evidence of a pattern in the residuals. Explain.

c. Determine the Durbin Watson statistic.

d. At the .05 level of significance, is there evidence of positive autocorrelation in the residuals?

Problem 13.16 In problem 13.5 (above), sales and number of orders were used to predict distribution costs at a mail order catalog business. Using the computer output you obtained to solve that problem:

a. Determine whether there sis a significant relationship between distribution costs and the two explanatory variables (sales and number of orders) at the .05 level of significance .

b. Interpret the meaning of the p-value.

Problem 13.23 In problem 13.5 (above), sales and number of orders were used to predict distribution costs at a mail order catalog business. Using the computer output you obtained to solve that problem:

a. Set up a 95% confidence interval estimate of the population slope between distribution costs and sales.

b. At the .05 level of significance, determine whether each explanatory variables make a significant contribution to the regression model. On the basis of these results, indicate the independent variables that should be included in this model.

Problem 13.29 In problem 13.5 (above), sales and number of orders were used to predict distribution costs at a mail order catalog business. Using the computer output you obtained to solve that problem:

a. At the .05 level of significance, determine whether each explanatory variable makes a significant contribution to the regression model. On the basis of these results, indicate the regression model that should be used in the problem.

b. Compute the coefficients of partial determination r2 (y1.2) and r2 (y2.1) and interpret the meaning.

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