Business Analytics: Regression Models Overview
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Questions and Answers

What type of variable is used in predicting whether a person will buy a new insurance product?

  • Nominal variable
  • Continuous variable
  • Ordinal variable
  • Discrete variable (correct)

    • Multiple linear regression is used when predicting a categorical dependent variable.

    False

    What is the primary approach used by Experian to predict customer conversion?

    Logistic Regression

    In multiple linear regression, multiple independent variables are used to predict a ________ dependent variable.

    <p>continuous</p> Signup and view all the answers

    Match the following projects with their respective predictions:

    <p>Insurance Store = purchase/no-purchase Opticians Credit Default = default/non-default Gym Customer Churn = churner/non-churner Customer Conversion Modelling = convert/no-convert</p> Signup and view all the answers

    Which of the following is NOT a project conducted by Experian?

    <p>Customer Satisfaction Survey</p> Signup and view all the answers

    Experian uses historical data to train its multiple linear regression models.

    <p>True</p> Signup and view all the answers

    What does the abbreviation MLR stand for?

    <p>Multiple Linear Regression</p> Signup and view all the answers

    What is a characteristic of a parametric model?

    <p>It makes strong assumptions about the form of the mapping function.</p> Signup and view all the answers

    Optimization involves defining a function that represents 'error.'

    <p>True</p> Signup and view all the answers

    What algorithm is often used for parameter optimization in models?

    <p>Optimizer or solver</p> Signup and view all the answers

    The objective function in MLR minimizes the sum of ______ errors.

    <p>squared</p> Signup and view all the answers

    Match the following types of models with their characteristics:

    <p>Parametric Model = Assumes a fixed form for the mapping function Non-Parametric Model = Uses a flexible number of parameters MLR = Uses least squares for error minimization k-Nearest Neighbors = A type of non-parametric model</p> Signup and view all the answers

    Which of the following examples illustrates a non-parametric model?

    <p>k-Nearest Neighbors</p> Signup and view all the answers

    A non-parametric model has no parameters at all.

    <p>False</p> Signup and view all the answers

    What is the main purpose of testing an optimized model?

    <p>To verify the trustworthiness of the results</p> Signup and view all the answers

    What are the two fundamental analytics models covered in this week?

    <p>Multiple Linear Regression and Multiple Logistic Regression</p> Signup and view all the answers

    Multiple Linear Regression is primarily used for classification tasks.

    <p>False</p> Signup and view all the answers

    Which project focuses on predicting the number of customers for a store location?

    <p>Opticians Store Locations</p> Signup and view all the answers

    Regression problems always involve predicting categorical variables.

    <p>False</p> Signup and view all the answers

    What is a hyperplane in the context of regression and classification models?

    <p>A hyperplane is a decision boundary used to separate different classes in classification tasks.</p> Signup and view all the answers

    Experian operates in _____ countries and has approximately _____ employees.

    <p>39, 18k</p> Signup and view all the answers

    What kind of regression is used when there is more than one input variable?

    <p>multiple linear regression</p> Signup and view all the answers

    Match the following aspects of Experian with their respective project focus:

    <p>Credit Risk = Managing customer portfolios Market Intelligence = Acquiring consumers Customer Insight = Application processing and fraud solutions Vehicle Insight = Growing customer relationships</p> Signup and view all the answers

    The ________ variable is being predicted when assessing the likelihood of a customer's default.

    <p>amount</p> Signup and view all the answers

    Which application is likely to be focused on market intelligence?

    <p>Opticians Store Locations</p> Signup and view all the answers

    Match the following regression projects with their predictions:

    <p>Opticians Store Locations = Customer count prediction Credit Rating Prediction = Default loss amount Northern Power Grid = Customer vulnerability to outages Insurance Product Purchase = Purchase likelihood</p> Signup and view all the answers

    What is the main goal of the Northern Power Grid project?

    <p>Evaluate vulnerability to power outages</p> Signup and view all the answers

    Optimization is unimportant in business analytics.

    <p>False</p> Signup and view all the answers

    What is the significance of 'Parametric Modelling' in analytics?

    <p>Parametric Modelling is important as it relies on assumptions about the data distribution to estimate relationships.</p> Signup and view all the answers

    The mean and median are both measures used in regression analysis.

    <p>True</p> Signup and view all the answers

    List one input feature used in customer numbers modeling.

    <p>Daytime Population</p> Signup and view all the answers

    What is the primary purpose of performing an F-test in regression analysis?

    <p>To assign a p-value to each variable's coefficient</p> Signup and view all the answers

    Linear regression is suitable for predicting categorical outcomes.

    <p>False</p> Signup and view all the answers

    What type of model is often preferred when predicting classes in business analytics?

    <p>Classifier</p> Signup and view all the answers

    The outcome categories for classification problems can include _____ or _____ predictions.

    <p>purchase, no-purchase</p> Signup and view all the answers

    Match the following classification terms with their corresponding categories:

    <p>malignant = benign defaulter = non-defaulter churner = non-churner converter = non-converter</p> Signup and view all the answers

    Why might linear regression produce nonsensical results in certain situations?

    <p>It assumes a continuous response variable.</p> Signup and view all the answers

    Logistic regression is utilized to model relationships in probability space.

    <p>True</p> Signup and view all the answers

    Provide an example of a classification problem.

    <p>Purchase/no purchase</p> Signup and view all the answers

    Study Notes

    The Story So Far

    • Supervised learning and unsupervised learning are the two main types of machine learning
    • Classification and clustering are both discrete types of supervised learning
    • Regression is a continuous type of supervised learning
    • Point models and linear regression are two examples of regression models
    • Dimensionality reduction and regression are both components of unsupervised learning

    Today’s Learning Objectives

    • The two fundamental analytics models covered: Multiple Linear Regression (MLR) and Multiple Logistic Regression.
    • Understanding the equations behind both models.
    • Comprehending the differences between regression and classification techniques and what a hyperplane is.
    • Understanding the concept of “Parametric Modelling” and its importance in business analytics.
    • Recognizing why “optimization” is crucial to successful business analytics.

    Experian

    • A leading user of modern business analytics
    • They use analytics for internal processes and externally by selling analytics services and products in 80 countries.
    • They have 18,000 employees in 39 countries with a revenue of approximately £4.7 billion.
    • Experian provides examples of specific projects in industries such as credit risk, market intelligence, consumer insight, and vehicle insight.

    Experian Projects

    • Opticians Store Locations: This project aims to predict the likely number of customers to a specific store location.
    • Credit Rating Prediction: This project aims to predict total losses to the business if customers default.
    • Northern Power Grid: This aims to predict the vulnerability of customers in certain locations to power outages.

    Customer “Numbers” Modelling:

    • Experian uses more than one input variable to make predictions.
    • When your linear regression problem has more than one input variable and one output, you're doing multiple linear regression.

    Customer “Conversion” Modelling

    • Experian uses Logistic Regression to predict whether potential customers will convert based on direct mail campaigns.

    Multiple Linear Regression (MLR)

    • MLR is used when predicting a continuous dependent variable (output) from more than one independent variable (input).
    • In MLR, the relationship between variables is visualized with the help of planes instead of simple straight lines.
    • MLR models are trained by tuning parameters so the model's predictions minimize error when tested on historical data.

    “Parametric” Models

    • Parametric models use equations with parameters that must be trained.
    • Optimization uses an "optimizer" or "solver" to find the best parameters for the model.

    Optimizing Parameters

    • To find the best parameters, an objective function (generally an equation that represents error) is defined.
    • The optimizer/solver receives instructions on which parameters to adjust.
    • The optimization process continues until it finds a parameterization with minimal identified error or until time or patience runs out.

    Optimizing Parameters in “MLR”

    • The objective function for MLR is the total error the model produces when tested on historical data.
    • This objective function traditionally minimizes the sum of squared errors (residuals) or the difference between the model's prediction and the actual value for each data point.

    Testing an Optimized Model

    • To determine the trustworthiness of the model, formal testing procedures are used.
    • Various parametric tests assign a p-value to each coefficient in a regression, which indicates significance and reliability.
    • The model that makes the best predictions is usually chosen.

    Classification Problems

    • Classification problems involve predicting a class or category when a continuous variable isn't being predicted.
    • Examples of classification problems include predicting purchase/no-purchase, defaulter/non-defaulter, churner/non-churner, and converter/non-converter.
    • Linear regression isn't suitable for classification problems as it's designed for predicting continuous variables.

    Classification Business Problems

    • Classifiers are more common than regressors in business analytics.
    • Examples of classification problems in various fields include: malignant/benign in public analytics, breach/non-breach in consumer analytics, upturn/downturn in financial analytics, win/loss/draw in sports analytics, outbreak/no outbreak in public health analytics, lapser/non-lapser in customer retention, give credit/reject in credit scoring, and buy/sell in stock trading.

    Linear Regression and Classification Problems

    • Linear regression isn't suitable for classification problems because it cannot give accurate classifications when predicting categorical variables.

    The Solution: Logistic Regression

    • Logistic Regression solves classification problems by using a different line for the model and a probabilistic space.

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    FBA Lecture 04 - MLR and Classification PDF

    Description

    This quiz covers the fundamentals of business analytics with a focus on Multiple Linear Regression (MLR) and Multiple Logistic Regression. You will explore the equations behind these models, the differences between regression and classification techniques, and the significance of parametric modeling in business analytics. Additionally, the role of optimization in achieving successful outcomes will be highlighted.

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