Evaluating Regression Models Quiz 11

by

The post is about Evaluating Regression Models Quiz with answers. There are 20 multiple-choice questions about regression models and their evaluation, covering regression analysis, assumptions of regression, coefficient of determination, predicted and predictor variables, etc. Let us start with the Evaluating Regression Models Quiz now.

Evaluating Regression Models Quiz

Online MCQs about Evaluating Regression Models

1. A testing set is —————.

 
 
 
 

2. The ratio of explained variation to the total variation of the following regression model is called $y_i = \beta_0 + \beta_1 x_{1i} + \beta_2x_{2i} + \varepsilon_i, \quad i=1,2,\cdots, n$.

 
 
 
 

3. A training set is ————–.

 
 
 
 

4. The test used to test the individual partial coefficient in the multiple regression is

 
 
 
 

5. What does regularization introduce into a model that results in a drop in variance?

 
 
 
 

6. When we fit a linear regression model we make strong assumptions about the relationships between variables and variance. These assumptions need to be assessed to be valid if we are to be confident in estimated model parameters. The questions below will help ascertain that you know what assumptions are made and how to verify these.

Which of these is not assumed when fitting a linear regression model?

 
 
 
 

7. One cannot apply test of significance if $\varepsilon_i$ in the model $y_i = \alpha + \beta X_i+\varepsilon_i$ are

 
 
 
 

8. What is a strategy you can employ to address an underfit model?

 
 
 
 

9. Which situations are helped by using the cross-validation method to train your model?

 
 
 
 

10. A third-order polynomial regression model is described as which of the following?

 
 
 
 

11. Regression coefficients may have the wrong sign for the following reasons

 
 
 
 

12. Parveen previously fitted a linear regression model to quantify the relationship between age and lung function measured by FEV1. After she fitted her linear regression model she decided to assess the validity of the linear regression assumptions. She knew she could do this by assessing the residuals and so produced the following plot known as a QQ plot.

QQ Plot Regression model residuals

How can she use this plot to see if her residuals satisfy the requirements for a linear regression?

 
 
 
 

13. An underfit model is said to have which of the following?

 
 
 
 

14. When evaluating models, what is the term used to describe a situation where a model fits the training data very well but performs poorly when predicting new data?

 
 
 
 

15. When tuning a model, a grid search attempts to find the value of a parameter that has the smallest —————-.

 
 
 
 

16. Let the value of the $R^2$ for a model is 0.0104. What does this tell?

 
 
 

17. When using the poly() function to fit a polynomial regression model, you must specify “raw = FALSE” so you can get the expected coefficients.

 
 

18. The residuals are the distance between the observed values and the fitted regression line. If the assumptions of linear regression hold how would we expect the residuals to behave?

 
 
 
 

19. How can the following plot be used to see if residuals satisfy the requirements for a linear regression?

Evaluating Regression Models Quiz 11

 
 
 
 

20. What is the difference between Ridge and Lasso regression?

 
 
 
 

MCQs Evaluating Regression Models Quiz with Answers

  • When using the poly() function to fit a polynomial regression model, you must specify “raw = FALSE” so you can get the expected coefficients.
  • A third-order polynomial regression model is described as which of the following?
  • When evaluating models, what is the term used to describe a situation where a model fits the training data very well but performs poorly when predicting new data?
  • An underfit model is said to have which of the following?
  • What does regularization introduce into a model that results in a drop in variance?
  • When tuning a model, a grid search attempts to find the value of a parameter that has the smallest —————-.
  • Which situations are helped by using the cross-validation method to train your model?
  • What is a strategy you can employ to address an underfit model?
  • What is the difference between Ridge and Lasso regression?
  • A training set is ————–.
  • A testing set is —————.
  • Regression coefficients may have the wrong sign for the following reasons
  • The ratio of explained variation to the total variation of the following regression model is called $y_i = \beta_0 + \beta_1 x_{1i} + \beta_2x_{2i} + \varepsilon_i, \quad i=1,2,\cdots, n$.
  • One cannot apply test of significance if $\varepsilon_i$ in the model $y_i = \alpha + \beta X_i+\varepsilon_i$ are
  • The test used to test the individual partial coefficient in the multiple regression is
  • When we fit a linear regression model we make strong assumptions about the relationships between variables and variance. These assumptions need to be assessed to be valid if we are to be confident in estimated model parameters. The questions below will help ascertain that you know what assumptions are made and how to verify these. Which of these is not assumed when fitting a linear regression model?
  • Parveen previously fitted a linear regression model to quantify the relationship between age and lung function measured by FEV1. After she fitted her linear regression model she decided to assess the validity of the linear regression assumptions. She knew she could do this by assessing the residuals and so produced the following plot known as a QQ plot. How can she use this plot to see if her residuals satisfy the requirements for a linear regression?
  • How can the following plot be used to see if residuals satisfy the requirements for a linear regression?
  • Let the value of the $R^2$ for a model is 0.0104. What does this tell?
  • The residuals are the distance between the observed values and the fitted regression line. If the assumptions of linear regression hold how would we expect the residuals to behave?
Evaluating Regression Models Quiz

Performing Statistical Models in R

MCQs Big Data Questions 2

by

The post is about MCQs Big Data Questions with Answers. There are 20 multiple-choice questions with answers. “Ready to test your big data knowledge? Take a quiz today and see how you fare! Share your results in the comments and let us know what topics you’d like to see covered in future quizzes.” Let us start with the Online MCQs Big Data Questions now.

Please go to MCQs Big Data Questions 2 to view the test

MCQs Big Data Questions

Online MCQs Big Data Questions with Answers
  • Which is the most compelling reason why mobile advertising is related to big data?
  • Which of the following summarizes the process of using data streams?
  • These two characteristics define the ratio between populated and unpopulated cells in a data source.
  • What does it mean for a device to be “smart”?
  • At-rest and in-transit data each have unique security concerns.
  • What does the term “in situ” mean in the context of big data?
  • What are data silos and why are they bad?
  • —————— is a measure of how fast the data is coming in.
  • These two characteristics are critical to implementing a successful high-velocity data strategy
  • What are the steps required for data analysis?
  • Which of the following is a technique mentioned in the videos for building a model?
  • Which of the Big Data processing tools provides distributed storage and processing of Big Data?
  • What does the attribute “Veracity” imply in the context of Big Data?
  • Defining the ————– ————– is the first step in any big data strategy.
  • A well-defined and comprehensive big data strategy makes the benefits of big data ————— for the organization.
  • What are the ways to address data quality issues?
  • Data in a data lake is most commonly stored in its natural or raw form.
  • What is the benefit of using pre-built Hadoop images?
  • Which of the following are general requirements for a programming language to support big data models?
  • Which of the following is the best description of why it is important to learn about the foundations of big data?

R Programming Language

Data Mining Short Questions and Answers

by

This post is about Data Mining Short Questions and Answers. The Data Mining Short Questions and Answers are related to Different levels of Analysis, Techniques used for Data Mining, Steps Used in Data Mining, Steps involved in Data Mining Knowledge Process, Data Aggregation, Data Generalization, and Book names related to Data Mining.

Data Mining Short Questions and Answers

What is the History of Data Mining?

In the 1960s, statisticians used the terms Data Fishing or Data Dredging. Consequently, the term Data Mining appeared in 1990, especially in the database community.

Name Different Levels of Analysis of Data Mining

  1. Artificial Neural Networks (ANNs)
  2. Genetic Algorithms
  3. Nearest Neighbour Method
  4. Rule Induction
  5. Data Visualization

What Techniques are Used for Data Mining?

The following techniques are used for data mining:

  • Artificial Neural Networks: Generally, data mining is used in many ways. Artificial Neural Networks (ANNs), a type of machine learning algorithm, are used in data mining to identify patterns, make predictions, and extract knowledge from large datasets, forming the basis of deep learning. It is also used for non-linear predictive models.
  • Decision Trees: Generally, tree-shaped structures are used to represent sets of decisions. It is also used for the classification of dataset rules are generated. A decision tree is a non-parametric supervised learning algorithm, utilized for both classification and regression tasks. It has a hierarchical, tree structure, which consists of a root node, branches, internal nodes, and leaf nodes.
  • Genetic Algorithm: The genetic algorithms are present with the use of data mining as a powerful optimization technique to find the best solutions for complex problems, mimicking evolution to improve a population of potential solutions iteratively. Genetic algorithms are genetic combination, mutation, and natural selection for optimization techniques.
Data Mining Short Questions and Answers Data Mining Applications

Name the Steps Used in Data Mining

  • Business Understanding
  • Data Understanding
  • Data Preparation
  • Modeling
  • Evaluation
  • Deployment

Explain the Steps Involved in the Data Mining Knowledge Process

  • Data Cleaning: In the Data Cleaning Step, the noise and inconsistent data are removed.
  • Data Integration: In the Data Integration Step, multiple data sources are combined.
  • Data Selection: In the Data Selection Step, data relevant to the analysis tasks are retrieved from the data (or database).
  • Data Transformation: In the Data Transformation Step, data is transformed into different forms appropriate for data mining. The summary and aggregation operations are also performed in this step.
  • Data Mining: In the Data Mining Step, intelligent methods are applied to extract data patterns.
  • Pattern Evaluation: In The Pattern Evaluation Step, data patterns are evaluated.
  • Knowledge Presentation: In the Knowledge Presentation Step, knowledge is presented.

Name Some Data Mining Books

  • Introduction to Data Mining by Tan, Steinbach & Kumar (2006)
  • Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners
  • Data Science for Business: What you need to know about data mining and data analytic thinking
  • Probabilistic Programming and Bayesian Methods for Hackers
  • Data Mining: Practical Machine Learning Tools and Techniques
  • Data Mining: The Text Book by Charu C. Aggarwal (2015)
  • Data Mining: Practical Machine Learning Tools and Techniques by Ian Witten (2016)
  • Data Mining and Machine Learning: Fundamental Concepts and Algorithms by Mohammed J. Zaki, (2020)

What is Data Aggregation and Generalization?

Data Aggregation: Data aggregation is the process of combining and summarizing data from multiple sources into a single, more manageable format to facilitate analysis and decision-making

Generalization: It is a process where low-level data is replaced by high-level concepts so that the data can be generalized and meaningful. Generalization is often used to enhance privacy or summarize data for easier analysis, such as replacing specific dates with months or specific values with ranges. 

Learn R Programming