In their previous papers, Team XX looks at age and wage using several statistical techniques. Team XX will cover bivariate regression for their Week Five assignment. As per Doane-Seward (2007), scatter plots and correlation coefficients provide clues about the relationship between variables and to model the relationship, researches use a process called regression. "Bivariate regression is a flexible way of analyzing relationships between two quantitative variables" (Doane-Seward, 2007, p. 500). Using the Wages and Wage Earners Data Set (University of Phoenix, 2010), Team B complete the regression process by measuring the strength of the relationship between the independent variable and independent variable of study aided by graphs.
The Wages and Wage Earners Data Set (University of Phoenix, 2010) shows 100 observations with the various attributes listed in columns. Team B defines the problem in their Research Process Paper to discover the relationship of age and wage of the 100 observations. In the team's understanding, a wage earner should earn more as his or her age increases. The hypothesis is to see if a person's wage is independent of his or her age. The linear relationship between age (an independent variable X ) and wage (a dependent variable) is best displayed in a mathematically corresponding line; a linear model of the relationship (y=a+Bx). In the line y=a+Bx, y-intercept and B=slope. As with all research steps, in regression one must first state the hypothesis, H0: Β1 = 0, and the null hypothesis H1: Β1 ≠ 0.
Perform Regression Hypothesis
Ho: b1 = 0 vs Ha: b1 ≠ 0
t- test for significant population slope with level of significance assumed as 5% (a = 0.05)
A Complete, Neat and Step-by-step Solution is provided. Excel file also attached.