Just eyeballing the table, you can see that there is going to be a positive correlation between sales and GDP. Both tend to go up together. Using Excel, all you have to do is click the Tools drop-down menu, select Data Analysis and from there choose Regression . The popup box is easy to fill in from there; your Input Y Range is your "Sales" column and your Input X Range is the change in GDP column; choose the output range for where you want the data to show up on your spreadsheet and press OK. You should see something similar to what is given in the table below

You can see that the data is clustered closely around the line, and that the line has a downward slope. There is strong negative correlation expressed by two related statistics: the r value, as stated before is, - the r 2 value is therefore . R 2 , called the Coefficient of Determination , expresses how much of the variability in the dependent variable is explained by variability in the independent variable. You may find that a non-linear equation such as an exponential or power function may provide a better fit and yield a higher r 2 than a linear equation.

This course takes place online at the Institute for 4 weeks. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Time Requirement :

About 15 hours per week, at times of your choosing.

where the coefficients of the equation, * a * and * b, * are estimates (based on single observations) of the true population parameters. These constants, * a * and * b, * obtained with the method of ordinary least squares, are called the estimated regression coefficients, and once their numerical values have been determined then they can be used to predict values of the dependent variable from values of the independent variable (Y). For example, if the estimated regression coefficient * of a * and * b * were 1,000 and respectively then the regression equation would be C=1,000 + Y and we could predict that for a disposable income of £10,000, consumer expenditure would be:

where the coefficients of the equation, * a * and * b, * are estimates (based on single observations) of the true population parameters. These constants, * a * and * b, * obtained with the method of ordinary least squares, are called the estimated regression coefficients, and once their numerical values have been determined then they can be used to predict values of the dependent variable from values of the independent variable (Y). For example, if the estimated regression coefficient * of a * and * b * were 1,000 and respectively then the regression equation would be C=1,000 + Y and we could predict that for a disposable income of £10,000, consumer expenditure would be: