# BIO 500 W8 Assignment Rank Correlation

## BIO 500 W8 Assignment Rank Correlation

BIO 500 W8 Assignment Rank Correlation

Details:

Question 1.  Use Excel or SPSS to complete Exercise 1 in the Cumulative Review Exercises on page 485 in the textbook.

APA format is not required, but solid academic writing is expected. Due Tuesday NOV 29th

Question 2. Chapter 11 of the textbook covers “Analysis of Variance”, however the authors state that “Testing for Equality of Three or More Population Means” might be a better chapter title.  Due December 6th

Question 3   Use Excel or SPSS to perform the one-way analysis of variance (ANOVA) for the data in “From Data to Decision” on page 565 of the textbook.

APA format is not required, but solid academic writing is expected.

Question 4     For the following assignment, use the Rank Correlation that was demonstrated in Chapter 12 of the textbook (page 566). Utilizing Excel or SPSS:

Use a rank correlation coefficient to test for a correlation between two variables.

Use a significance level of α=0.05.

The new health care program in the United States makes provisions for capitation programs where health care insurers work with clinical facilities to perform risk analysis of patients to determine the cost of providing care. The following assignment might be used to assess how much a person smokes.

BIO 500 W8 Assignment Rank Correlation

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When nicotine is absorbed by the body, cotinine is produced. A measurement of cotinine in the body is therefore a good indicator of how much a person smokes. The reported number of cigarettes smoked per day and the measured amounts of cotinine (in ng/ml) are provided. (The values are from randomly selected subjects in a National Health Examination Survey.) Is there a significant linear correlation? How would you measure the cotinine level in the body? Explain the result.

Refer to the “Rank Correlation Table.”

APA format is not required, but solid academic writing is expected.

You are not required to submit this assignment to Turnitin

Due Dec 9th

Rank Correlation Table

 x (cigarettes per day) 60 10 4 15 10 1 20 8 7 10 10 20 y(cotinine) 179 283 75.6 174 209 9.51 350 1.85 43.4 25.1 408 344

In statisticscorrelation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. In the broadest sense correlation is any statistical association, though it commonly refers to the degree to which a pair of variables are linearly related. Familiar examples of dependent phenomena include the correlation between the height of parents and their offspring, and the correlation between the price of a good and the quantity the consumers are willing to purchase, as it is depicted in the so-called demand curve.

Correlations are useful because they can indicate a predictive relationship that can be exploited in practice. For example, an electrical utility may produce less power on a mild day based on the correlation between electricity demand and weather. In this example, there is a causal relationship, because extreme weather causes people to use more electricity for heating or cooling. However, in general, the presence of a correlation is not sufficient to infer the presence of a causal relationship (i.e., correlation does not imply causation).

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