Regression Analysis Assignment Help

Regression Analysis Assignment Help

It is a statistical approach that is used to measure relationship between two or more variables. This analysis includes two types of variables, one type is dependent variable and second type is independent variable. Regression analysis is also a useful technique to forecast or predict changes in dependent variables on the basis of changes in independent variables. In order to effectively perform a regression analysis, it is important to firstly identify, which variable is independent/predictor variable and which variable is dependent/response variable in the given data set. An independent variable is the cause, while a dependent variable is an effect or output (Keller, 2011).

It means an independent variable represents the reason, while a dependent variable represents an effect of that cause. So, the decision, which variable is predictor variable and which is response variable, depends on the nature of variable in given data set. For example, the demand for coffee may be specified as a function of price in which price is an independent variable, while demand for coffee is dependent variable. It is because demand is responded according to changes in prices of coffee that’s why price is predictor variable and demand is response variable (Thrall, 2002). Predictor and Response Variable of Silver’s Gym Data Set In case of regression, the weight is considered as the independent variable, while body fat is considered as dependent variable. Based on body fat versus weight data set, weight is a predictor variable, but body fat is a response variable. It is because increase weight is the main source of body fat and it is useful to predict weight of an individual. On the basis this, it is decided that weight is the predictor variable and body fat is the response variable. Scatter Diagram The scatter diagram is a graphical method that is useful to find out correlation between two variables.

In this diagram, independent variable is represented on X-axis and dependent variable is represented on Y-axis. According to scatter diagram, the correlation between variables is either positive, negative or no correlation (Coon, 2009). The scatter diagram for body fat versus weight data set is as below- Scatter DiagramThe above scatter diagram shows that there is a positive correlation between body fat data and weight data. It is because the increase in body fat is matched with increase in weight. Along with this, most of points appear to move on an upward direction from left side to right side. But, there is not a perfect correlation between body fat and weight data set because points are not close to each other and all point are not plotted in the shape of straight line (Hirschey, 2008). On the basis of this, it is concluded that there is a positive correlation. Correlation Coefficient The correlation coefficient (r) is a statistical tool that is used to measure strength of relationship between two or more variables. In this case, the correlation coefficient is useful to verify whether conclusion of above scatter plot is right or wrong.

By using the excel spreadsheet, the correlation coefficient of body fat versus weight data set is calculated as below- Correlation coefficient (r) = +0.613 The correlation coefficient is positive that means there is a positive correlation between body fat and weight data. The positive value of correlation coefficient also indicates that both correlated variables move in the same direction. Additionally, it also indicates that weight is increased with rise in body fat, but not always (Plotnik & Kouyoumdjian, 2010). All these findings explain that there is a strong positive correlation between body fat and weight. This finding is similar to conclusion with scatter diagram, so it can be said that finding of scatter diagram is correct. Regression Analysis Regression Line The following diagram is shown the regression line as well as regression equation on scatter plot. statistical analysis assignment helpAccording to above diagram, the regression equation will be- y = 0.161x – 9.995 Regression Line Good Fit for Data or Not Yes, the regression line appears on scatter plot to be a good fit for the body fat versus weight data set.

It is because almost all points are plotted around the line with obvious pattern of differences. Along with this, due to absence of any kind of curve, it can be stated that regression line is a good fit for the data (Hirschey, 2008). Use of Regression Equation The regression equation is a mathematical equation that is useful to determine prediction about a variable with respect of other variable. The regression analysis also includes slope and intercept. The slope is a rate at which y increases according to increase in x. According to above diagram, the slope means that values of body fat are gained with each pound of increase in weight (Wissing & Thiel, 2006).

In this case, regression equation is also helpful to determine predicted value of body fat (y) when n weight (x) equals to 0. The calculation of y is as follows: y = 0.161x – 9.995 = 0.161*(0) – 9.995 y = -9.995 The outcome depicts that predicted value of body fat (y) is -9.995 when weight is 0. But, the value of body fat does not make any sense because of negative value. The regression equation indicates that when weight is 0 than value of body fat (y) is also 0. Conclusion On the basis of above analysis and results, it is concluded that body fat in 252 men, who are attending gym is lower from expected level of 20%.

From the statistical measures, it is identified that the mean of body fat is 18.9% that is statistically lower than from average body fat of 20%. From the regression analysis, it is concluded that there is a strong relationship between body fat and weight of trainees. It is also found that coefficient of correlation is 0.613 and predicted value of body fat (y) is negative with respect of weight. Overall from regression and correlation analysis, it is summarized that there is strong positive correlation between body fat and weight data set. et 100% original and complete Regression Analysis Assignment Help, Hypothesis Testing Assignment Help and statistical analysis assignment help with From our experts you will get complete and original assignment help on time. If you are first time using our assignment help services then you can first check our quality of work then you can transfer the money in our account.