This tutorial explains how to train and test a logistic regression model in Excel with one click using an add-in.

This add-in is 100% free and can run on any version of MS Excel to build logistic regression models.

When we download add-in from internet, Microsoft blocks them from running and shows the source of this file is untrusted. Please follow the steps below to make it trusted.

- Go to the folder where the downloaded add-in file is located.
**Right-click**the file and choose**Properties**from the menu.- At the bottom of the
**General Tab**, select the**Unblock**checkbox under**security**option and then click**OK**.

Refer the following steps to install Logistic Regression add-in.

- Open Excel and click on the
`File`

tab in the ribbon. - Click on
`Options`

and then select`Add-ins`

from the left-hand menu. - In the
`Manage`

drop-down menu at the bottom of the screen, select`Excel Add-ins`

and click on the`Go`

button. - Click on the
`Browse`

button and locate the add-in file that you downloaded. - Select the add-in file and click on the
`OK`

button. - You should see the name of the add-in file in the
`Add-Ins`

dialog box. Check the box next to the add-in name to activate it. - Once you are done with the above steps, a new tab called
`LogisticRegression`

should be visible in your Excel workbook.

## Step 1 : Split Data into Training and Testing Sets

The first step of building a predictive model is to split data into training and validation data sets. In Excel, under the **LogisticRegression** tab, click on `Split Data`

. It creates two sheets for training and testing data.

**Select Data (including header) :**Select the cells containing data for both dependent and independent variables including their variable names.**Enter the split percentage for training data :**Enter a value between 0 and 100 for data to be used for training model. For example, 70 refers to 70% of the data used to train the model with the remaining 30% kept for testing.

## Step 2 : Model Training

The next step is to fit the logistic regression model on the training data. In Excel, under the **LogisticRegression** tab, click on `Build Model`

. It returns predicted probabilities along with coefficient table and model evaluation metrics such as AUC and AIC.

**Select Independent Variables :**Select the cells containing independent variables including their variable names.**Select Dependent Variable :**Select the cells containing dependent variable including its variable name.

The add-in returns the following columns at observation level.

**Probability :**Predicted probabilities for dependent event occuring i.e. prob(y=1).**Log Likelihood :**Log of predicted probabilities for actual dependent variable

The add-in returns the following metrics for indepedent variables in a tabular format.

**Coefficients**: It estimates of the relationship between each independent variable and the log odds of the dependent event occurring.**Standard Errors**: It tells us the precision of the estimates.**z-statistic**: It is the ratio of a coefficient to its standard error.**P-value**: It shows the significance of the independent variable.**Lower95%**: The lower bound of the 95% confidence interval for the coefficient.**Upper95%**: The upper bound of the 95% confidence interval for the coefficient.**Odds Ratio**: It is the exponential value of coefficient.**Std. coeff.**: It is used to find out the relative importance of each independent variable.

The add-in returns the following metrics to check the performance of model.

**AUC**: Area Under the ROC Curve. It measures how well model can distinguish between events(1) and non-events(0).**AIC**: Akaike Information Criterion. It measures the relative quality of a logistic regression model.

## Step 3 : Predict on Testing Data

It refers to the process of using the trained logistic regression model to make predictions on a testing or validation dataset that was not used during the model building step. In Excel, under the **LogisticRegression** tab, click on `Model Predictions`

. It returns predicted probabilities of testing dataset.

**Select Independent Variables :**Select the cells containing independent variables.**DO NOT include their variable names.****Select Coefficients :**Select the cells containing coefficients of variables including intercept.

You can check the model's performance based on the predictions.

You can use the **AUC()** formula to compute area under curve on testing or new data.

=AUC(probability_column,dependent_variable_column)

=AUC(G2:G80,A2:A80)

The confusion matrix is shown in the table below.

Predicted Negative(0) | Predicted Positive(1) | |
---|---|---|

Actual Negative(0) | True Negative (TN) | False Positive (FP) |

Actual Positive(1) | False Negative (FN) | True Positive (TP) |

You can use the **ConfusionMatrix()** formula to calculate the following metrics.

- Accuracy
- True Positive Rate
- False Positive Rate
- True Negative Rate
- False Negative Rate

=ConfusionMatrix(probability_column,dependent_variable_column,threshold)

**threshold:** Threshold for converting probabilities to 0/1 predictions. Many softwares use threshold of 0.5 but it should be decided based on the youden-index.

=ConfusionMatrix(G2:G80,A2:A80,0.5)

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