The paper ” Annual Income and Amount Spent on Car” is a worthy example of an assignment on finance and accounting. Based on the data we can assume that if the annual income increases then the amount of money spent on the car increases too. The relationship between the annual income and amount of money spent looks to be positive and strong which in turn means that the correlation is close to +1. The direction of causality in this relationship looks like earning more money make you spend more on your car. The Dependent variable (Y) is the amount spent on the car whereas the Independent variable (X) is the annual income level. The relationship between the dependent and independent variable can be tested by both ANOVA and regression, but regression provides more information about the relationship. ANOVA analyzes mean differences between groups satisfactorily but if there is a continuous variable like age then ANOVA does not provide the desired results while regression does. Regression analysis, unlike ANOVA, assumes independence, normality, and constant variance but in addition, the linear relationships between the dependent and independent variables are assumed. (Frederick T. L. Leong & James T. Austin, 2005, p. 300). The graphical representation of the regression shows that there is a positive relationship between the annual income and the amount of money spent on the car. It is observed that with the variation of the income there is variation in the amount spent on the car.
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