Probability Distribution Quiz 8

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The post is about the MCQs Probability Distributions Quiz. There are 20 multiple-choice questions about probability distributions covering distributions such as discrete and continuous Binomial Probability Distribution, Bernoulli Probability Distribution, Poisson Probability Distribution, Poisson Probability, Distribution, Geometric Probability Distribution, Hypergeometric Probability Distribution, Chi-Square distribution, Normal distribution, and F-distribution. Let us start with the MCQs Discrete Probability Distributions Quiz.

MCQs Probability Distribution Quiz

Probability Distribution Quiz with Answers

1. If $X$ is a F-distributed random variable with $m$ and $n$ df, then $W=\frac{mX/n}{1+mX/n}$ has a

 
 
 
 

2. You find a z-score of -1.99. Which statement(s) is/are true?

 
 
 
 

3. The P-value for a normally distributed right-tailed test is P=0.042. Which of the following is INCORRECT?

 
 
 
 

4. The spread of the normal curve depends upon the value of:

 
 
 
 

5. When the experiment is repeated a variable number of times to obtain a fixed number of success is

 
 
 
 

6. If $Z$ has a standard normal distribution, if $U$ has a chi-square distribution with $k$ degrees of freedom and if $Z$ and $U$ are independent then the distribution of $X=\frac{Z}{\sqrt{\frac{U}{\sqrt{k}}}}$ is

 
 
 
 

7. A test is administered annually. The test has a mean score of 150 and a standard deviation 20. If Chioma’s z-score is 1.50, what was her score on the test?

 
 
 
 

8. The number of parameters in multivariate normal distribution having $p$ variables are

 
 
 
 

9. The distribution function of the random variable $X$ is given by $F_X(x)=1-\frac{1}{x^2}$ for $x \ge c$, 0 otherwise, where $c$ is a constant. What is the set of possible values of the constant $c$?

 
 
 
 

10. The time X taken by a cashier in a grocery store express lane to complete a transaction follows a normal distribution with a mean of 90 seconds and a standard deviation of 20 seconds. What is the first quartile of the distribution of X (in seconds)?

 
 
 
 

11. Which of the following can best be described as a normal distribution?

 
 
 
 

12. The moment generating function of normal distribution is

 
 
 
 

13. The moment generating function of Gamma distribution with parameter $\lambda$ and $k$ is

 
 
 
 

14. We look for a model, as realistic as possible, for a continuous random variable $X$ that represents the lifetime of a machine, and whose mean and variance are equal to 1 and 3, respectively. Which of the following distributions can be acceptable?

  • Uniform
  • Exponential
  • Gamma
  • Gaussian
  • The square of a Gaussian N(1, 3)
 
 
 
 

15. Expected values are properties of what?

 
 
 
 

16. If the mean of the Chi-Square distribution is 4 then its variance is

 
 
 
 

17. A random variable $Y$ has the following distribution
y:     -1   0   1    2
p(y):  3C 2C 0.4 0.1

The value of the constant C is

 
 
 
 

18. In its standardized form, the normal distribution

 
 
 
 

19. If you got a 75 on a test in a class with a mean score of 85 and a standard deviation of 5, the z-score of your test score would be

 
 
 
 

20. Green sea turtles have normally distributed weights, measured in kilograms, with a mean of 134.5 and a variance of 49.0. A particular green sea turtle’s weight has a z-score of -2.4. What is the weight of this green sea turtle? Round to the nearest whole number.

 
 
 
 

Online Probability Distribution Quiz

  • You find a z-score of -1.99. Which statement(s) is/are true?
  • Expected values are properties of what?
  • If you got a 75 on a test in a class with a mean score of 85 and a standard deviation of 5, the z-score of your test score would be
  • The spread of the normal curve depends upon the value of:
  • Which of the following can best be described as a normal distribution?
  • In its standardized form, the normal distribution
  • A test is administered annually. The test has a mean score of 150 and a standard deviation 20. If Chioma’s z-score is 1.50, what was her score on the test?
  • The P-value for a normally distributed right-tailed test is P=0.042. Which of the following is INCORRECT?
  • The time X taken by a cashier in a grocery store express lane to complete a transaction follows a normal distribution with a mean of 90 seconds and a standard deviation of 20 seconds. What is the first quartile of the distribution of X (in seconds)?
  • Green sea turtles have normally distributed weights, measured in kilograms, with a mean of 134.5 and a variance of 49.0. A particular green sea turtle’s weight has a z-score of -2.4. What is the weight of this green sea turtle? Round to the nearest whole number.  
  • We look for a model, as realistic as possible, for a continuous random variable $X$ that represents the lifetime of a machine, and whose mean and variance are equal to 1 and 3, respectively. Which of the following distributions can be acceptable?
    Uniform
    Exponential
    Gamma
    Gaussian
  • The square of a Gaussian N(1, 3)
  • The distribution function of the random variable $X$ is given by $F_X(x)=1-\frac{1}{x^2}$ for $x \ge c$, 0 otherwise, where $c$ is a constant. What is the set of possible values of the constant $c$?
  • A random variable $Y$ has the following distribution y:     -1   0   1    2 p(y):  3C 2C 0.4 0.1 The value of the constant C is
  • If $Z$ has a standard normal distribution, if $U$ has a chi-square distribution with $k$ degrees of freedom and if $Z$ and $U$ are independent then the distribution of $X=\frac{Z}{\sqrt{\frac{U}{\sqrt{k}}}}$ is
  • If $X$ is a F-distributed random variable with $m$ and $n$ df, then $W=\frac{mX/n}{1+mX/n}$ has a
  • The number of parameters in multivariate normal distribution having $p$ variables are
  • The moment generating function of Gamma distribution with parameter $\lambda$ and $k$ is
  • The moment generating function of normal distribution is
  • When the experiment is repeated a variable number of times to obtain a fixed number of successes is
  • If the mean of the Chi-Square distribution is 4 then its variance is

MCQs General Knowledge

Classification in Data Mining

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The post is about Classification in Data Mining. It is in the form of questions and answers for easy of understanding and learning the classification techniques and their applications in real-life.

What is Classification in Data Mining? Explain with Examples.

Classification in data mining is a supervised learning technique used to categorize data into predefined classes or labels based on input feature data. The classification technique is widely used in various applications, such as spam detection, image recognition, sentiment analysis, and medical diagnosis.

The following are some of the real life examples that make use of classification algorithms:

  • A bank loan officer may need to analyze the data to know which customers are risky or which are safe.
  • A marketing manager may need to analyze a customer with a given profile, who will buy a new product item.
  • Banks and financial institutions use classification algorithms to identify potentially fraudulent transactions by classifying them as “Fraudulent” or “Legitimate” transactions based on transaction patterns.
  • Mobile apps and digital assistants use classification algorithms to convert handwritten text into digital format by identifying and classifying individual characters or words.
  • News channels and companies use classification algorithms to categorize their articles into different sections (such as Sports, Politics, Business, Technology, etc.) based on the content of the articles.
  • Businesses analyze customer reviews, feedback, and social media posts to classify sentiments as “Positive,” “Negative,” or “Neutral,” helping them gauge public perception about their products or services.

What is the Goal of Classification?

Classification aims to develop a model that can accurately predict the class of unseen instances based on patterns learned from a training dataset.

Write about the Key Components of Classification.

Key components of classification in Data Mining are:

  1. Training Data: A dataset where the class labels are known, which will be used to train the classification model.
  2. Model: An algorithm (such as decision trees, neural networks, support vector machines, etc.) that learns to distinguish between different classes based on the training data.
  3. Features: The input variables or attributes that are used to make predictions about the class labels.
  4. Prediction: Once a model is trained, the model can classify new, unseen instances by assigning them to one of the predefined classes.
  5. Evaluation: The performance of the classification model can be assessed using metrics like accuracy, precision, F1 score, recall, and confusion matrix.

Why Classification is Needed?

In today’s world of Big Data, a large dataset is becoming a norm. For example, image a dataset/database with many terabytes such as Facebook alone crunches 4 Petabyte of data every single day. On the other hand primary challenge of big data is how to make sense of it. Moreover, the sheer volume is not the only problem. also, big data needs to be diverse, unstructured, and fast changing.

Similalry, consider the audio and video data, social media posts, 3D data or geospatial data. These kind of data are not easy to categorize or organized.

Classification in Data Mining

Name Methods of Classification Methods

The following are some population methods of classification methods.

  • Statistical procedure based approach
  • Machine Learning based approach
  • Neural network
  • Classification algorithms
  • ID3 algorithm
  • 4.5 Algorithm
  • Nearest neighbour algorithm
  • Naive bayes algorithm
  • SVM algorithm
  • ANN algorithm
  • Deision Trees
  • Support vector machine
  • Sense Clusters (an adaption of the K-means clustering algorithm)

Explain ID3 Algorithm

The ID3 (Iterative Dichotomiser 3) algorithm is a decision tree learning algorithm, primarily used for classification tasks in data mining and machine learning.

What are the Key Features of ID3 Classification?

  • Categorical Attributes: ID3 algorithm is designed to work primarily with categorical attributes. It does not handle continuous attributes directly, but they can be converted into categorical ones through binning.
  • Information Gain: The algorithm uses information gain as a criterion to select the attribute that best separates the data into different classes. Information gain measures the reduction in entropy (uncertainty) after a dataset is split based on a specific attribute.
  • Recursive Tree Building: ID3 classification algorithm builds the decision tree recursively, splitting the data into subsets based on attribute values.

MCQs Data Mining

Data Analysis in R Programming Language

Design of Experiments Quiz 6

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Online Quiz about Design of Experiments Quiz Questions with Answers. There are 20 MCQs in this DOE Quiz cover the basics of the design of experiments, hypothesis testing, basic principles, and single-factor experiments, fixed effect models, random effect models. Let us start with “Design of Experiments MCQs with Answer”. Let us start with the Design of Experiments Quiz Questions with Answers now.

Design of Experiments Quiz with Answers

Please go to Design of Experiments Quiz 6 to view the test

Design of Experiments Quiz with Answers

  • If an interaction effect in a factorial design is significant the main effects of the factors involved in that interaction may be difficult to interpret.
  • Factorial experiments cannot be used to detect the presence of interaction.
  • An interaction term in a factorial model with quantitative factors introduces curvature in the response surface representation of the results.
  • A factorial experiment can be run as an RCBD by assigning the runs from each replicate to separate blocks.
  • One of the ANOVA assumptions is that treatments have:
  • For ANOVA we assume that treatments are applied to the experimental units:
  • One factor ANOVA means, there is only:
  • For one factor ANOVA, the model contains:
  • Single-factor ANOVA is also called:
  • In a fixed effect model:
  • In a random effect model:
  • The treatment effect is associated with:
  • If the experiment were to be repeated and the same set of treatments would be included, we choose:
  • The experimenter is interested in treatment means only. The model used is called:
  • A fixed effect model is used when the effect of —————– is assumed to be fixed during the experiment.
  • A researcher is interested in measuring the rate of production of five particular machines. The model will be a:
  • To compare the IQ level of five students a series of tests is planned and IQ is computed based on their results. The model will be:
  • If the treatments in a particular experiment are a random sample from a large population of similar treatments. we choose:
  • If the experimenter is interested in the variation among treatment means not the treatment means themselves. The model used is called:
  • In a random effects model ————- are randomly chosen from a large population.

MCQs General Knowledge