MULTIPLE REGRESSION USING THE DATA ANALYSIS ADD-IN. This requires the Data Analysis Add-in: see Excel 2007: Access and Activating the Data Analysis Add-in The data used are in carsdata.xls. We then create a new variable in cells C2:C6, cubed household size as a regressor. Then in cell C1 give the the heading CUBED HH SIZE.

Linear Regession: Table of Contents Introduction • • • Simple Regression • • • • • • • Multiple Regression • • • • • • • Regression Analysis With Excel In the real world, you will probably never conduct multiple regression analysis by hand. Most likely, you will use computer software (SAS, SPSS, Minitab, Excel, etc.). Excel is a widely-available software application that supports multiple regression. In this lesson, we use Excel to demonstrate multiple regression analysis. (Other software packages produce outputs similar to Excel.) Sample Problem With Excel Consider the table below.

It shows three performance measures for 10 students. Student Test score IQ Study hours 1 100 110 40 2 95 110 40 3 90 120 30 4 85 110 40 5 80 100 20 6 75 110 40 7 70 90 0 8 65 110 40 9 60 80 10 10 55 80 10 In this lesson, using data from the table, we are going to complete the following tasks: • Develop a least-squares regression equation to predict test score, based on (1) IQ and (2) the number of hours that the student studied. • Assess how well the regression equation predicts test score, the dependent variable. • Assess the contribution of each independent variable (i.e., IQ and study hours) to the prediction. These are common tasks in regression analysis.

With the right software, they are easy to accomplish. We'll walk you step by step through each task, starting with setting up Excel.

How to Enable Excel When you open Excel, the module for regression analysis may or may not be enabled. So, before you do anything else, you need to determine whether Excel is enabled. Here's how to do that: • Open Excel. • Click the Data tab. • If you see the Data Analysis button in the upper right corner, the Analysis TookPak is enabled and you are ready to go. If the Data Analysis button is not visible, the Analysis ToolPak is not enabled.

In that case, do the following: • Click the File tab. • Select Options to open the Excel Options dialog box. • Click the Add-Ins item, from the left column.

This opens the View and Manage Microsoft Office Add-ins screen. • From the Manage drop-down box, choose Excel Add-Ins and click the Go button. This opens the Add-Ins dialog box. • From the Add-Ins dialog, check the box beside Analysis ToolPak and click Go. This enables the Analysis ToolPak. Now, when you click the Data tab, you will see a Data Analysis button in the upper right corner under the Data tab.

Data Entry With Excel Data entry with Excel is easy. There are three main steps: • Enter data on spreadsheet. • Identify independent and dependent variables. • Specify desired analyses. To illustrate the process, we'll walk through each step, using data from our sample problem. First, we want to enter data on an Excel spreadsheet.

This means listing data for each variable in adjacent columns, as shown below. By default, Excel will produce a standard set of outputs. For this sample problem, that's all we need; so click OK to generate standard regression outputs. Note: If desired, you can request additional outputs in the form of and normal probability plots. To produce the plots, check the appropriate box(es) under Output options on the Regression dialog box.

Data Analysis With Excel Excel provides everything we need to address the tasks we defined for this sample problem. Recall that we wanted to do three things: • Develop a least-squares regression equation to predict test score, based on (1) IQ and (2) the number of hours that the student studied. • Assess how well the regression equation predicts test score, the dependent variable. • Assess the contribution of each independent variable (i.e., IQ and study hours) to the prediction. Let's review the output produced by Excel and see how it addresses each task. Regression Equation The first task in our analysis is to define a linear, least-squares regression equation to predict test score, based on IQ and study hours. Since we have two independent variables, the equation takes the following form: ŷ = b 0 + b 1x 1 + b 2x 2 In this equation, ŷ is the predicted test score.

The independent variables are IQ and study hours, which are denoted by x 1 and x 2, respectively. The regression coefficients are b 0, b 1, and b 2. On the right side of the equation, the only unknowns are the regression coefficients; so to specify the equation, we need to assign values to the coefficients.

Mac mini boot from usb. This file system is necessary if you plan on using the drive for Time Machine backups–otherwise, you’ll want to use exFAT for maximum compatibility. • OS X Extended (Case-sensitive, Journaled): On a case-sensitive file system, “file” is different from “File”.