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A set of 10 hypothetical patient records from a large database is presented in Table 3.9. Patients with a diabetes value of 1 have type II diabetes and patients with a diabetes value of 0 do not have type II diabetes. It is anticipated that this data set will be used to predict diabetes based on measurements of age, systolic blood pressure, diastolic blood pressure, and weight. 1. For the following variables from Table 3.9, assign them to one of the following categories: constant, dichotomous, binary, discrete, and continuous. a. Name b. Age c. Gender d. Blood group e. Weight (kg) f. Height (m) g. Systolic blood pressure h. Diastolic blood pressure i. Temperature j. Diabetes 2. For each of the following variables, assign them to one of the following scales: nominal, ordinal, interval, ratio. a. Name b. Age c Gender d. Blood group e. Weight (kg) f. Height (m) g. Systolic blood pressure h. Diastolic blood pressure i. Temperature j. Diabetes 3. On the basis of the anticipated use of the data to build a predictive model, identify: a. A label for the observations b. The descriptor variables c. The response variable 4. Create a new column by normalizing the Weight (kg) variable into the range 0 to 1 using the min-max normalization. 5. Create a new column by binning the Weight variable into three categories: low (less than 60 kg), medium (60 100 kg), and high (greater than 100 kg).

product s asset class and style. We would use a high yield benchmark to evaluate the performance of a high yield product; we would use a global index to evaluate the performance of a global bond product; and so on.

4.1. ELEMENTARY BOUNDS FOR P{M > u}

Quantifying Road Geometry: Mapping Dilution of Precision (MDOP)

Bargh, J.A., Chen, M., & Burrows, L. (1996). Automaticity of social behavior: Direct effects of trait construct and stereotype-activation on action. Journal of Personal Social Psychology, 71(2), 230 244.

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Lipsitz, S R, Laird, N M and Harrington, D P (1991) Generalized estimating equations for correlated binary data: using odds ratios as a measure of association, Biometrika, 78, 153 160 Moore, D S and Spruill, M C (1975) Unified large sample theory of general chi squared statistics for tests of fit Annals of Statistics, 3, 599 616 Rao, J N K (1998) Marginal models for repeated observation: Inference with survey data In Proceedings of the Survey Research Methods Section, American Statistical Association (pp 76 82) Washington, DC: American Statistical Association Rao, J N K and Scott, A J (1981) The analysis of categorical data from complex surveys: chi squared tests for goodness for fit and independence in two way tables Journal of the American Statistical Association, 76, 221 230 Rao, J N K, Scott, A J and Skinner, C J (1998) Quasi score tests with survey data.

Figure 6 2: A UML description of the tables used in the online store examples To start with, you need some structures to represent your customers. When a customer registers, you enter his or her details in the customer table. Each customer has an internal unique identifier. As a separate table from the customer table, you have the employee table. This is the list of the employees of the store and is used to identify who is fulfilling a request. Next you have a series of products to define. Like the customer, you need a unique product identifier. Finally, you have an order table that binds all these tables together. The order table contains a unique order identifier, customer ID, product ID, status, and price paid. This means that when an order is made, you will know the price the customer paid. Stores offer specials from time to time, which means that prices change, and stock might not be available at the time the order is made. For these orders, you don't charge the customer until the item has arrived, and you can send it out. So, in the event that the price has gone up since the order was made, you need to keep the original price information in the order so that the customer is treated fairly.

Statistica Sinica, 8, 1059 1070 Rao, J N K and Tausi, M (2004) Estimating function jackknife variance estimators under stratified multistage sampling Communications in Statistics 33, 2087 2100 Rao, J N K and Wu, C F J (1988) Resampling inference with complex survey data Journal of the American Statistical Association, 83, 231 241 Rao, J N K, Wu, C F J and Yue, K (1992) Some recent work on resampling methods Survey Methodology, 18, 209 217 Rust, K and Rao, J N K (1996) Variance estimation for complex surveys using replication techniques Statistical Methods in Medical Research, 5, 283 310 Roberts, G, Rao, J N K and Kumar, S (1987) Logistic regression analysis of sample survey data Biometrika, 74, 1 12 Shields, M and Shooshtari, S (2001) Determinants of self perceived health Health Reports, 13, 35 52 Skinner, C J, Holt, D and Smith, T M F.

Sixteen years after Descartes death, in 1676, an obscure Danish astronomer, Ole Roemer, proved him wrong.

(1989) Analysis of Complex Surveys New York: John Wiley & Sons, Inc..

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