![]() ![]() The functions are located on the StatPlus:mac LE menus. Visit the AnalystSoft Web site, and then follow the instructions on the download page.Īfter you have downloaded and installed StatPlus:mac LE, open the workbook that contains the data that you want to analyze. You can use StatPlus:mac LE to perform many of the functions that were previously available in the Analysis ToolPak, such as regressions, histograms, analysis of variance (ANOVA), and t-tests. Option 2: Download StatPlus:mac LE for free from AnalystSoft, and then use StatPlus:mac LE with Excel 2011. After 30 days you will be able to use the free version that includes the Analysis ToolPak functions, or order one of the more complete solutions of XLSTAT. No Multicollinearity: None of the predictor variables are highly correlated with each other. Linear relationship: There exists a linear relationship between each predictor variable and the response variable. Open the Excel file that contains your data and click on the XLSTAT icon to launch the XLSTAT toolbar.įor 30 days, you'll have access to all XLSTAT functions. However, before we perform multiple linear regression, we must first make sure that five assumptions are met: 1. Select the XLSTAT version that matches your Mac OS and download it.įollow the MAC OS installation instructions. XLSTAT contains more than 200 basic and advanced statistical tools that include all of the Analysis ToolPak features. ![]() Option 1: Download the XLSTAT add-on statistical software for Mac and use it in Excel 2011. There are a few third-party add-ins that provide Analysis ToolPak functionality for Excel 2011. I can't find the Analysis ToolPak in Excel for Mac 2011 Now the Data Analysis command is available on the Data tab. If you get a prompt that the Analysis ToolPak is not currently installed on your computer, click Yes to install it. If Analysis ToolPak is not listed in the Add-Ins available box, click Browse to locate it. In the Add-Ins available box, select the Analysis ToolPak check box, and then click OK. Load the Analysis ToolPak in Excel for MacĬlick the Tools menu, and then click Excel Add-ins. The ToolPak displays in English when your language is not supported. See Supported languages for more information. Some languages aren't supported by the Analysis ToolPak. See I can't find the Analysis ToolPak in Excel for Mac 2011 for more information. Notice that the slope (\(0.541\)) is the same value given previously for \(b_1\) in the multiple regression equation.The Analysis ToolPak is not available for Excel for Mac 2011. The linear regression equation for the prediction of \(UGPA\) by the residuals is The following equation is used to predict \(HSGPA\) from \(SAT\): This slope is the regression coefficient for \(HSGPA\). The final step in computing the regression coefficient is to find the slope of the relationship between these residuals and \(UGPA\). The correlation between \(HSGPA.SAT\) and \(SAT\) is necessarily \(0\). These residuals are referred to as \(HSGPA.SAT\), which means they are the residuals in \(HSGPA\) after having been predicted by \(SAT\). ![]() These errors of prediction are called "residuals" since they are what is left over in \(HSGPA\) after the predictions from \(SAT\) are subtracted, and represent the part of \(HSGPA\) that is independent of \(SAT\). In this example, the regression coefficient for \(HSGPA\) can be computed by first predicting \(HSGPA\) from \(SAT\) and saving the errors of prediction (the differences between \(HSGPA\) and \(HSGPA'\)). Interpretation of Regression CoefficientsĪ regression coefficient in multiple regression is the slope of the linear relationship between the criterion variable and the part of a predictor variable that is independent of all other predictor variables. Note that \(R\) will never be negative since if there are negative correlations between the predictor variables and the criterion, the regression weights will be negative so that the correlation between the predicted and actual scores will be positive. In this example, it is the correlation between \(UGPA'\) and \(UGPA\), which turns out to be \(0.79\). The multiple correlation (\(R\)) is equal to the correlation between the predicted scores and the actual scores. The values of \(b\) (\(b_1\) and \(b_2\)) are sometimes called "regression coefficients" and sometimes called "regression weights." These two terms are synonymous. ![]()
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