MATH 225N Week 8 Discussion|MATH225N Week 8 Discussion: Correlation and Regression, (Answered)

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/*If a regression analysis was to be completed on body mass index (BMI), what could be an independent variable in that analysis? Why? If we could, what other independent variables should be included... in the analysis? What statistic(s) would show the value of that regression in understanding BMI? Regression analysis can be used to explain the relationship between one independent or predictor variable and one continuous dependent or outcome variable (Creswell & Creswell, 2018). A regression analysis on body mass index (BMI) would explain its relationship with an independent variable, such as total cholesterol in milligrams per deciliter (mg/dL), age, or gender. While BMI does not distinguish between excess fat, muscle, or bone mass, nor does it provide any indication of the distribution of fat among individuals, it can be affected by one’s cholesterol intake, age, and gender (Centers for Disease Control and Prevention, 2015). Including these independent variables in the analysis can provide an overall picture of the relationship between BMI and independent variables. The statistic of value is the correlation coefficient, which can be obtained using Excel or SPSS. The correlation coefficient can show whether the association between BMI and the independent variable is strong, weak, positive, or negative as noted by Holmes, Illowsky, and Dean (2018). 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, [Show More]

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