Due 7 P.M. EST: 400 Words Not Including Title Reftopic Is Ob

Due 130 7 Pm Est400 Words Not Including Title Reftopic Is Obesit

Due 1/30 7 p.m EST 400 words not including title & ref TOPIC is obesity Variables that will be used is Height, weight, age, BMI The purpose of statistics is to produce study results from a sample and to allow the researcher to infer them to the population where the sample was taken. Inferential statistics are the techniques that allow for this type of generalization to occur. The methods of inferential statistics include estimating population parameters from sample statistics and testing statistical hypotheses. Common testing methods are t-test, analysis of variance (ANOVA), analysis of covariance (ANCOVA), simple and multiple linear regressions, chi-square test, and simple and multiple logistic regressions.

The variable type or level of measurement for the dependent variables and independent variables influence the selection of the testing methods. For this Discussion, review the inferential statistics you are using for your Scholar Practitioner Project. Post a description of the statistics you will include in your inferential analysis for your Scholar Practitioner Project. Explain the rationale behind using these specific statistics, including what statistics you chose not to use and explaining why. Support your response

Paper For Above instruction

The field of research surrounding obesity necessitates the application of specific inferential statistical methods to analyze the relationships among variables such as height, weight, age, and BMI. These variables are critical in understanding obesity patterns and outcomes, and choosing the appropriate inferential statistics ensures valid and reliable results. In the context of my Scholar Practitioner Project, I intend to utilize multiple linear regression analysis to explore the association between the predictor variables (height, age) and the outcome variable (BMI), which is a continuous variable.

Multiple linear regression is appropriate because it allows for the examination of how multiple independent variables simultaneously influence a dependent variable, providing insight into which factors have significant effects on BMI. Additionally, this method handles continuous data effectively and can control for confounding variables, leading to more precise estimations of relationships. The inclusion of height, weight, and age as predictors aligns with the level of measurement for these variables, all of which are interval or ratio scales, making regression analysis suitable.

I also plan to incorporate descriptive statistics such as means, standard deviations, and ranges in preliminary analyses to understand the data distribution and identify any outliers. These foundational steps are essential before conducting inferential tests. Inferentially, I will perform hypothesis testing within the regression framework to assess the significance of each predictor and the overall model fit, utilizing t-tests for individual coefficients and F-tests for the model.

I considered using analysis of variance (ANOVA) to compare mean BMI across different categorical groups, such as age categories or other relevant groupings; however, since my primary focus is on the continuous predictors' influence on BMI, regression analysis is more appropriate. Chi-square tests could be used if categorical variables (e.g., obesity status) were to be analyzed, but since the focus is on continuous predictors and BMI as a continuous outcome, chi-square is less suitable.

One statistical method I opted not to use is the t-test for comparing two independent groups because my research involves multiple predictors and potential confounders, making regression techniques more comprehensive. Also, analysis of covariance (ANCOVA), which combines ANOVA with regression, could be useful if I needed to control for additional covariates, but given the present scope, multiple regression sufficiently addresses the research questions.

In summary, the use of multiple linear regression in my project is justified by its ability to assess the relationship between several continuous variables simultaneously, control for confounders, and provide detailed insights into the factors influencing BMI. This approach aligns with current statistical best practices for analyzing the complex interactions among variables related to obesity.

References

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