![]() ![]() ![]() This value is fairly high, which indicates that the model does a good job of classifying the data into ‘Pass’ and ‘Fail’ categories. To calculate the AUC of the curve, we can simply take the sum of all of the values in column H: A model with an AUC equal to 0.5 is no better than a model that makes random classifications. The closer AUC is to 1, the better the model. This makes it easy to use XLSTAT alongside other Excel features, such as charts and graphs, to create more. To quantify this, we can calculate the AUC (area under the curve) which tells us how much of the plot is located under the curve. XLSTAT is seamlessly integrated with Microsoft Excel. The more that the curve hugs the top left corner of the plot, the better the model does at classifying the data into categories.Īs we can see from the plot above, this logistic regression model does a pretty good job of classifying the data into categories. Then we’ll click the Insert tab along the top ribbon and then click Insert Scatter(X, Y) to create the following plot: To create the ROC curve, we’ll highlight every value in the range F3:G14. We’ll then copy and paste these formulas down to every cell in columns F, G, and H: Next, we’ll calculate the false positive rate (FPR), true positive rate (TPR), and the area under the curve AUC) using the following formulas: Step 3: Calculate False Positive Rate & True Positive Rate Covariance and correlation analysis including Pearson, Spearman and Kendall correlation coefficients, correlation maps, scatter plot matrices and many more. We’ll then copy and paste these formulas down to every cell in column D and column E: Descriptive statistics including several charts (pie charts, bar charts, box plots) Histograms. Next, let’s use the following formula to calculate the cumulative values for the Pass and Fail categories: The following step-by-step example shows how to create and interpret a ROC curve in Excel. This is a plot that displays the sensitivity and specificity of a logistic regression model. One way to visualize these two metrics is by creating a ROC curve, which stands for “receiver operating characteristic” curve. This is also called the “true negative rate.”
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