It's a classification problem-solving supervised machine learning algorithm. The estimate of the probability of a specific event occurring is the output of a logistic regression algorithm. When it comes to probability, it's always between 0 and 1.
Select Data Analysis from the Analysis category on the Data tab.
Click OK after selecting Regression.
Configure the following options in the Regression dialogue box: The Input Y Range, which is your dependent variable, should be selected.
After clicking OK, look at the Excel regression analysis output.
Note that you can use either OLS or Logistic Regression, however I'm demonstrating OLS regression. I'll walk you through the fundamental analytic procedures below by pointing and clicking. As you add dependent and independent variables to the model, Sheets will apply transformations to them. Categorical (factors) variables can also be used.
The link function is log(p/1-p). We can model a non-linear link in a linear way by using a logarithmic transformation on the outcome variable. In Logistic Regression, this is the equation that is employed. The odd ratio here is (p/1-p).
The null hypothesis that the coefficient is equal to zero is tested by the p-value for each term (no effect). A low p-value (0.05) suggests that the null hypothesis can be rejected.
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