Virtual Research Assignment (complete in an Excel document): 1. In this VRP assignment you should select two continuous variables (one variable as the dependent variable and the other as the key independent variable) from your research data set to complete the analyses below. If you only have binary variables, with the permission of the instructor, you can use this data set instead: h138_hlthexp . If you are permitted to use the h138_hlthexp , "AGE10X" and "TOTEXP10" will be the two variables and the underlying hypothesis is that older age is related to higher health care spending. 2. Add notes to variable names to mark which variable is the dependent variable and which is the independent variable. 3. Using the data for the two variables in the spreadsheet, produce a scatter plot of the data points for the two variables. It is necessary to move one column next to the other one. You will go to "Insert" and will use the "Scatter" chart function to construct the graph. Basically you just highlight the data of the two variables you wish to graph, and click "Scatter." You need to make sure that you have graphed the two variables on the correct axes. Make sure that the dependent variable goes on the Y axis and the independent variable on the X axis. 4. Study your scatter plot and put down in the same tab in the Excel document whether you think this scatter plot exhibits a relationship between the two variables or not and why you think so. A trendline can be added to make the trend more obvious. 5. Identify any bivariate outliers. Write down the X and Y values of the outliers in the Excel document. 6. Use the data to run a regression to test the relationship between the two variables. Make sure that you have the right variables as your independent variable and dependent variable. Once you have your results, in the same tab in which the output is shown, put down: 1. The OLS value for a 2. The OLS value for b 3. The standard error 4. rsquared 5. The regression equation Next to those values, interpret what they mean in your analysis (interpret the values for a, b, the standard error, and R2. 8. Predict the value of the dependent variable that corresponds to a particular value of the independent variable that you choose. For example, in the study of how patient age affects health care spending, you may predict a patient's health care spending given that he or she is 32 years old