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Here is a log sheet for a patient information system used by nurses at a convalescent home to record patient visitors and activities during their shifts.

Date Patient Visitors Relationship Activities
02/14 Clarke 2 Mother, Father Walked about halls, attended chapel, meals in cafeteria
Coffey 6 Coworkers Played games, party in room
Martine 0 - Meals in room
Laury 4 Husband and Friends Games in sunroom, watched TV
Finney 2 Son, Daughter Conversation, meals in cafeteria
Cartwright 1 Sister Conversation, crafts room
Goldstein 2 Sister, Brother Conversation, Games out of room, whirlpool

a. Design a printed report that provides a summary for the charge nurse of each shift and a report for the activities coordinator at the end of the week. Be sure to use proper conventions to indicate constant data, variable data, and so on. These reports will be used to determine staffing patterns and future activity offerings.

b. Design display output for Problem A using form design software. Make any assumptions about system capability necessary and follow display design conventions for onscreen instructions. (Hint: You can use more than one display screen if you wish.)

c. In a paragraph, discuss why you designed each report as you did in Problems A and B. What are the major differences in your approach to each one? Can the printed reports be successfully transplanted to displays without changes? Why or why not?

d. Some of the nurses are interested in a Web-based system that patients' families can access from home with a password. Design an output screen for the Web. In a paragraph, describe how your report had to be altered so that it could be viewed by one patient's family.

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Statistics Problems - Regression Analysis, Autocorrelation, Multicollinearity

1. Suppose an appliance manufacturer is doing a regression analysis, using quarterly time-series data, of the factors affecting its sales of appliances. A regression equation was estimated between appliance sales (in dollars) as the dependent variable and disposable personal income and new housing starts as the independent variables. The statistical tests of the model showed large t-values for both independent variables, along with a high r2 value. However, analysis of the residuals indicated that substantial autocorrelation was present.

a. What are some of the possible causes of this autocorrelation?

b. How does this autocorrelation affect the conclusions concerning the significance of the individual explanatory variables and the overall explanatory power of the regression model?

c. Given that a person uses the model for forecasting future appliance sales, how does this autocorrelation affect the accuracy of these forecasts?

d. What techniques might be used to remove this autocorrelation from the model?

2. Suppose the appliance manufacturer discussed in Exercise 1 also developed another model, again using time-series data, where appliance sales was the dependent variable and disposable personal income and retail sales of durable goods were the independent variables. Although the r2 statistic is high, the manufacturer also suspects that serious multicollinearity exists between the two independent variables.

a. In what ways does the presence of this multicollinearity affect the results of the regression analysis?

b. Under what conditions might the presence of multicollinearity cause problems in the use of this regression equation in designing a marketing plan for appliance sales?

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