Design of Experiments Quiz Test 9

by
Post Views: 8

Online Design of Experiments Quiz Test Questions with Answers. There are 20 MCQs in this DOE Quiz that cover the basics of the design of experiments, hypothesis testing, basic principles, single-factor experiments, sums of squares and means squares. Let us start with the “Design of Experiments Quiz Test with Answer”. Let us start with the Design of Experiments Quiz Test Questions with Answers now.

MCQs Design of Experiments Quiz Test with Answers

Online Design of Experiments Quiz Test with Answers

1. Which one is a non-parametric test?

 
 
 
 

2. The expected value of the mean square of error is equal to:

 
 
 
 

3. For $a$ treatments the degree of freedom of treatment is:

 
 
 
 

4. To study how a particular group of antibiotics acts on the body, an experimenter takes a random sample from such antibiotics and observes their working. The model used is called:

 
 
 
 

5. Between the sum of squares is also called:

 
 
 
 

6. Cochran’s theorem concludes that, under the assumption of normality, the various quadratic forms are independent and have:

 
 
 
 

7. Within treatments or error sum of squares is also called:

 
 
 
 

8. A market analyst is interested in examining a particular brand of machines, he draws a random sample of the brand and examines the working of machines. We choose:

 
 
 
 

9. The Theorem which tells us about the distributions of partitioned sums of squares of normally distributed random variables is called:

 
 
 
 

10. The expected value of the mean square of treatment is equal to:

 
 
 
 

11. The mean square of treatment is computed by dividing the sum of the squares of error by:

 
 
 
 

12. Error degree of freedom is computed by subtracting treatment degree of freedom from:

 
 
 
 

13. The chi-square distribution is the ratio of two —————— variables.

 
 
 
 

14. The expected value of the mean square of treatment is always ————– to/than the expected value of the mean square of error.

 
 
 
 

15. The error sum of squares can be computed by ———————-.

 
 
 
 

16. The mean square of error is computed by dividing the sum of the squares of error by:

 
 
 
 

17. Which one is not a model adequacy-checking tool?

 
 
 
 

18. The sum of squares of the total can be partitioned into ——————–

 
 
 
 

19. The sum of observations from their mean is equal to:

 
 
 
 

20. The Kruskal-Wallis Test can be used to check:

 
 
 
 

Design of Experiments Quiz Test

  • A market analyst is interested in examining a particular brand of machines, he draws a random sample of the brand and examines the working of machines. We choose:
  • To study how a particular group of antibiotics acts on the body, an experimenter takes a random sample from such antibiotics and observes their working. The model used is called:
  • The sum of squares of the total can be partitioned into ——————–
  • The error sum of squares can be computed by ———————-.
  • Error degree of freedom is computed by subtracting treatment degree of freedom from:
  • The Theorem which tells us about the distributions of partitioned sums of squares of normally distributed random variables is called:
  • Cochran’s theorem concludes that, under the assumption of normality, the various quadratic forms are independent and have:
  • The chi-square distribution is the ratio of two —————— variables.
  • Within treatments or error sum of squares is also called:
  • Between the sum of squares is also called:
  • The sum of observations from their mean is equal to:
  • The mean square of error is computed by dividing the sum of the squares of error by:
  • The expected value of the mean square of error is equal to:
  • For $a$ treatments the degree of freedom of treatment is:
  • The mean square of treatment is computed by dividing the sum of the squares of error by:
  • The expected value of the mean square of treatment is equal to:
  • The expected value of the mean square of treatment is always ————– to/than the expected value of the mean square of error.
  • Which one is not a model adequacy-checking tool?
  • Which one is a non-parametric test?
  • The Kruskal-Wallis Test can be used to check:

R Programming Language

MCQs if vlookup Logical Operators 6

by
Post Views: 6

The post is about MS Excel MCQs if vlookup Logical Operators Quiz Questions. It contains 20 multiple-choice questions covering the if statement, vlookup, xlookup, logical functions (And function, OR function), match, and index functions in MS Excel. Let us start with the MS Excel MCQs if vlookup Logical Operators Quiz Questions now.

Please go to MCQs if vlookup Logical Operators 6 to view the test

Online MS Excel MCQs if vlookup Logical Operators Quiz

  • When we use the IF function, how many arguments does Excel expect?
  • Using the IF function to determine the maximum of 2 numeric cells C2 and B2, the Excel formula could look like:
  • If we want to report whether C2 and B2 (formatted as ‘Number’) are the same in value using the IF function, the Excel formula could look like:
  • If we want to report whether C2 and B2 (formatted as Number) are different in value using the IF function, the Excel function could look like:
  • How many input arguments are allowed by the AND function?
  • How many input arguments are allowed by the OR function?
  • To detect whether 3 values, A2, B2, and C2 are all different and in ascending order, the Excel formula for giving an output in Ascending Order, and not giving this output for any other scenario could look like this:
  • To detect whether A2 contains a duplicate in cell B2 or a duplicate in cell C2, the Excel formula for giving an output Duplicate, for this scenario and no other scenario could look like this:
  • To detect whether 3 values, A2, B2, and C2 contain exactly (and only) 2 values that are the same (not 3 values, just 2 exact values), the Excel formula for giving an output Duplicate, for this scenario and no other scenario could look like:
  • When using VLOOKUP, what is the lookup_value? https://rfaqs.com
  • When using VLOOKUP, what is the col_index_num?
  • What are some features of an approximate match VLOOKUP?
  • What are some of the things to note when you are writing a VLOOKUP function?
  • Ali has asked you to explain what the fourth argument is for in the VLOOKUP function ([range lookup]).
  • When is it appropriate to use the exact match VLOOKUP?
  • What are the features of using tables as the table_array in the VLOOKUP function?
  • What are the arguments (including their order) of the XLOOKUP function?
  • What are the properties of the MATCH function?
  • What are the differences between VLOOKUP and INDEX?
  • If the value of the lookup_value is not found in the table_array, what type of error will you get?
MS Excel MCQs if vlookup Logical Operators Quiz

Computer MCQs Online Test

Cluster Analysis in Data Mining

by
Post Views: 4

The post is about cluster Analysis in Data mining. It is in the form of questions and answers.

What is a Cluster Analysis in Data Mining?

Cluster analysis in data mining is used to group similar data points into clusters. Cluster analysis relies on similarity metrics (e.g., distance) to determine how similar data points are. Therefore, cluster analysis helps to make sense of large amounts of data by organizing it into meaningful groups, revealing underlying structures and patterns.

What is Clustering?

Clustering is a fundamental technique in data analysis and machine learning. In clustering, a group of abstract objects into classes of similar objects is made. We treat a cluster of data objects as one group.

While performing cluster analysis, we first partition the set of data into groups, as it is based on data similarity. Then we assign the labels to the groups. Moreover, a main advantage of over-classification is that it is adaptable to changes. Also, it helps single out useful features that distinguish different groups.

Explain in Detail About Clustering Algorithm

The clustering algorithm is used on groups of datasets that are available with a common characteristic, they are called clusters.

As the clusters are formed, it helps to make faster decisions, and exporting the data is also fast.

First, the algorithm identifies the relationships that are available in the dataset and based on that it generates clusters. The process of creating clusters is also repetitive.

Cluster Analysis in Data Mining

Discuss the Types of Clustering

There are various clustering algorithms in data mining, including:

  • K-means clustering: Partitions data into a predefined number of clusters.
  • Hierarchical clustering: Builds a hierarchy of clusters.
  • Density-based clustering: Identifies clusters based on the density of data points.

Name Some Methods of Clustering

The following are the names of Clustering Methods:

  • Partitioning Method
  • Hierarchical Method
  • Density-based Method
  • Grid-Based Method
  • Model-Based Method
  • Constraint-Based Method

What are the applications of Cluster Analysis in Data Mining?

The following are some Applications of Cluster Analysis in Data Mining:

  • Market segmentation: Grouping customers with similar purchasing behaviors.
  • Anomaly detection: Identifying unusual data points that don’t fit into any cluster.
  • Social network analysis: Identifying communities within social networks.
  • Image segmentation: Dividing an image into distinct regions.
  • Bioinformatics: Grouping genes or proteins with similar functions.

What are important Considerations when Performing Cluster Analysis in Data Mining?

The following are key considerations when performing cluster Analysis in data mining:

  • Choosing the Right Algorithm: The best algorithm depends on the data’s characteristics and the goal of the analysis.
  • Determining the Number of Clusters: Some algorithms require specifying the number of clusters beforehand (e.g., k-means), while others can determine it automatically.
  • Evaluating Clustering Results: Assessing the quality of clusters can be challenging, as there’s no single “correct” answer.

Write about Distribution-Based Clustering

The distribution-based clustering algorithms assume that data points belong to clusters based on probability distributions. The Gaussian Mixture Models (GMMs) assume that data points are generated from a mixture of Gaussian distributions. The GMM method is very useful when you have reason to believe that your data is generated from a mixture of well-understood distributions.

Write about Density-based Clustering

The density-based clustering algorithms group data points based on their density. The DBSCAN (Density-Based Spatial Clustering of Applications with Noise) can discover clusters of arbitrary shapes and handle outliers. These are good at finding irregularly shaped clusters.

Write about Hierarchical Clustering

The hierarchical clustering algorithms build a hierarchy of clusters. They can be:

  • Agglomerative: Starting with each data point as its cluster and merging them.
  • Divisive: Starting with one large cluster and dividing it.

The hierarchical clustering algorithm produces a dendrogram, which visualizes the hierarchy.

Write about Centroid-based Clustering

The Centroid-based clustering algorithms represent each cluster by a central vector (centroid).

K-Means: A popular algorithm that aims to partition data into $k$ clusters, where $k$ is a user-defined number.

The centroid-based clustering algorithms are efficient but sensitive to initial conditions and outliers.

MCQs General Knowledge