MATH 225 Week 8 Final Exam

MATH 225 Week 8 Final Exam

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Chamberlain University

MATH-225 Statistical Reasoning for the Health Sciences

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Date

Independent Variables in Regression Analysis of BMI

Regression analysis serves as a valuable tool in examining the relationship between one or more independent variables and a continuous dependent variable. In the case of body mass index (BMI), potential independent variables include total cholesterol levels measured in milligrams per deciliter (mg/dL), age, and gender. Each of these variables can influence BMI in various ways. For instance, total cholesterol intake may correlate with body fat and overall health, age can reflect metabolic changes, and gender often impacts body composition and fat distribution (Creswell & Creswell, 2018).

Although BMI is a useful indicator of body weight relative to height, it has limitations, as it does not differentiate between fat, muscle, or bone mass and does not indicate fat distribution within individuals. Nonetheless, understanding how cholesterol intake, age, and gender relate to BMI provides a comprehensive overview of the factors that can affect this measurement (Centers for Disease Control and Prevention, 2015).

The correlation coefficient is a critical statistic that demonstrates the strength and direction of the relationship between BMI and the chosen independent variables. This statistic can be calculated using statistical software like Excel or SPSS. A strong, positive correlation would indicate that as one variable increases, so does BMI, while a negative correlation would suggest an inverse relationship. These insights are essential for interpreting how various factors impact BMI, as emphasized by Holmes, Illowsky, and Dean (2018).

Summary of Key Points

References

Centers for Disease Control and Prevention. (2015). Body mass index: considerations for practitioners. Retrieved from https://www.cdc.gov/obesity/downloads/bmiforpactitioners.pdf

Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Thousand Oaks, CA: Sage.

MATH 225 Week 8 Final Exam

Holmes, A., Illowsky, B., & Dean, S. (2018). Introductory business statistics. Houston, TX: OpenStax.