Analysis – Regression and Correlation Student’s Name Institutional Affiliation Introduction All over the world there are people who look fat but in the end weigh less than their slender counterparts and there are slender counterparts who weigh more than their fat friends. This has led to many people question if there is any relationship between body fat and body weight. With the increased cases of obesity it is important that analysis to determine any correlation between body fat and weight be conducted. Regarding this this paper contains results of an analysis of the relationship between body fat and body weight. In this study there are two variables which are involved in the analysis. The two variables are body fat and weight. Body fat is defined as the portion of body weight body fat is not only correlated with body weight but there is a causal relationship between body fat and body weight where an increase in body fat causes an increase in body weight. Also a decrease in body fat will consequently lead to a decrease in the body weight of an individual. The findings from this analysis are very important in the health sector. with this knowledge health facilities can be able to know how to control somebody’s weight and that is through controlling a person’s body fat. To lose weight one will be advised to focus on losing the body fat. This is a great step in understanding ways in which people can deal with the current epidemic which is obesity. References Sedgwick P. (2012). Pearson’s correlation coefficient. Bmj 345(7). [...]
Title: Analysis Regression and Correlation Week 5 Outcomes: Following completion of this assignment the student will: 1. Describe the relationship between variables 2. Define correlation coefficient 3. Apply correlation and regression concepts Based on your previous Case Study work and discussions this week, identify a hypothesis that could be tested using the dataset and concepts of correlation and regression. State the hypothesis to be tested and indicate the following: 1. When performing a regression analysis, it is important to first identify the independent/predictor variable versus the dependent/response variable, or simply put, your x versus y variables. 2. How do you decide which variable is your predictor variable and which is your response variable? 3. Based on the dataset, which variable is the predictor variable? Which variable is the response variable? Explain. 4. Using Excel, construct a scatter plot of your data. How to create a scatter plot in excel: check youtube. 5. Using the graph and intuition, determine whether there is a positive correlation, a negative correlation, or no correlation. How did you come to this conclusion? 6. Calculate the correlation coefficient, r, and verify your conclusion with your scatter plot. What does the correlation coefficient determine? 7. Add a regression line to your scatter plot, and obtain the regression equation. 8. Does the line appear to be a good fit for the data? Why or why not? Regression equations help you make predictions. Using your regression equation, discuss what the slope means, and determine the predicted value of (y) when length (x) equals 0. Interpret the meaning of this equation. The paper should include the following: 1. Introduction (overview of the issue-as related to the data set) 2. Hypothesis to be tested based on the data set 3. Independent and dependent variables 4. Scatter plot of the data 5. Determinations of correlation and regression (answer above questions) 6. Conclusion A total of 3-page paper following APA style formatting. Title page and reference list are expected to be included.