English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 2h 29m | 1.45 GB

Excel, R, and Power BI are applications universally used in data science and across businesses and organizations around the world. If you’ve spent any time trying to figure out how to better model your data to get useful insights from it that you can act upon, you’ve most likely encountered these applications. In this course, Helen Wall shows how to use Excel, R, and Power BI for logistic regression in order to model data to predict the classification labels like detecting fraud or medical trial successes. Helen walks through several examples of logistic regression. She shows how to use Excel to tangibly calculate the regression model, then use R for more intensive calculations and visualizations. She then illustrates how to use Power BI to integrate the capabilities of Excel calculations and R in a scalable, sharable model.

## Table of Contents

**Introduction**

1 Apply logistic regressions to solve problems

2 What you should know

3 Introduction to the course project

4 Configuring the Excel Solver Add-in

5 Working with R

6 Configuring R in Power BI

**Distributions and Probabilities**

7 Introducing AI and logistic regression

8 Differentiating between odds and probabilities

9 Differentiating between distributions

10 Calculating logs and exponents

11 Sigmoid curve

12 Utilizing training and testing data sets

**Binomial Logistic Regression**

13 Calculating linear regression

14 Working with the logit model

15 Calculating log likelihood

16 Constructing MLE

17 Solving MLE

18 Predicting outcomes

19 Visualizing logistic regression

20 Challenge Calculating logistic regression

21 Solution Calculating logistic regression

**Fine-Tuning the Model**

22 Adding more independent variables

23 Transforming variables

24 Calculating correlations

25 Using statistics

26 Configuring confusion tables

27 Challenge Fine-tuning the model

28 Solution Fine-tuning the model

**Multinomial Regression**

29 Calculating odds for multinomial models

30 Calculating probabilities for multinomial models

31 Calculating multinomial log likelihoods

32 Running MLE

33 Making the predictions

**Working in Power BI with R**

34 Running R scripts in the Power Query Editor

35 Running R standard visuals

36 Interacting between visual components

37 Challenge Moving into Power BI

38 Solution Moving into Power BI

**Conclusion**

39 Next steps with logistic regressions

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