Currently working as Assistant Professor of Statistics in Ghazi University, Dera Ghazi Khan.
Completed my Ph.D. in Statistics from the Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan.
l like Applied Statistics, Mathematics, and Statistical Computing.
Statistical and Mathematical software used is SAS, STATA, Python, GRETL, EVIEWS, R, SPSS, VBA in MS-Excel.
Like to use type-setting LaTeX for composing Articles, thesis, etc.
Looking to test your Big Data knowledge? Check out these top Big Data MCQs Questions and Answers for 2025! Perfect for students, professionals, and enthusiasts to assess their understanding of key concepts like Hadoop, Spark, and the 5 Vs of Big Data. Let us Start the Big Data MCQs Questions Quiz now.
Online big data mcqs questions with Answers
Big Data MCQs Questions with Answers
Which is NOT one of the three V’s of Big Data?
Which one of the following is an example of structured data?
What is the reason behind the explosion of interest in big data?
Which of the following is an example of big data utilized in action today?
What reasoning was given for the following: why is the “data storage to price ratio” relevant to big data?
What is the best description of personalized marketing enabled by big data?
Of the following, which are some examples of personalized marketing related to big data?
What is the workflow for working with big data?
Which is the most compelling reason mobile advertising relates to big data?
What are the three types of diverse data sources?
What is an example of machine data?
What is an example of organizational data?
Of the three data sources, which is the hardest to implement and streamline into a model?
Which of the following summarizes the process of using data streams?
Where does the real value of big data often come from?
What does it mean for a device to be “smart”?
What does the term “in situ” mean in the context of big data?
What are the steps required for data analysis?
What are the specific benefit(s) to a distributed file system?
Completely Randomized Block Designs (RCBD) is the design in which homogeneous experimental units are combined in a group called a Block. The experimental units are arranged in such a way that a block contains complete set of treatments. However, these designs are not as flexible as those of Completely Randomized Designs (CRD).
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Introduction to Randomized Complete Block Designs
A Randomized Complete Block Design (RCBD or a completely randomized block design) is a statistical experimental design used to control variability in an experiment by grouping similar (homogeneous) experimental units into blocks. The main goal is to reduce the impact of known sources of variability (e.g., environmental factors, subject characteristics) that could otherwise obscure the effects of the treatments being tested.
The restriction in RCBD is that a single treatment occurs only once in a single block. These designs are the most frequently used. Mostly RCBD is applied in field experiments. Suppose, a field is distributed in block x treatment experimental units $(N = B \times T)$.
Suppose, there are four Treatments: (A, B, C, D), three Blocks: (Block 1, Block 2, Block 3), and randomization is performed, that is, treatments are randomly assigned within each block.
Key Features of RCBD
The key features of RCBD are:
Control of Variability: By grouping/blocking similar units into blocks, RCBD isolates the variability due to the blocking factor, allowing for a more precise estimate of the treatment effects.
Blocks: Experimental units are divided into homogeneous groups called blocks. Each block contains units that are similar to the blocking factor (e.g., soil type, age group, location).
Randomization: Within each block, treatments are randomly assigned to the experimental units. This ensures that each treatment has an equal chance of being applied to any unit within a block. For example,
In agricultural research, if you are testing the effect of different fertilizers on crop yield, you might block the experimental field based on soil fertility. Each block represents a specific soil fertility level, and within each block, the fertilizers are randomly assigned to plots.
Advantages of Completely Randomized Block Designs
Improved precision and accuracy in experiments.
Efficient use of resources by reducing experimental error.
Flexibility in handling heterogeneous experimental units.
When to Use Completely Randomized Block Designs
CRBD is useful in experiments where there is a known source of variability that can be controlled through grouping/ blocking. The following are some scenarios where CRBD is appropriate:
Heterogeneous Experimental Units: When the experimental units are not homogeneous (e.g., different soil types, varying patient health conditions), blocking helps control this variability.
Field Experiments: In agriculture, environmental factors like soil type, moisture, or sunlight can vary significantly across a field. Blocking helps account for these variations.
Clinical Trials: In medical research, patients may differ in age, gender, or health status. Blocking ensures that these factors do not confound the treatment effects.
Industrial Experiments: In manufacturing, machines or operators may introduce variability. Blocking by machine or operator can help isolate the treatment effects.
Small Sample Sizes: When the number of experimental units is limited, blocking can improve the precision of the experiment by reducing error variance.
When NOT to Use CRBD
The Completely Randomized Block Design should not be used in the following scenarios:
If the experimental units are homogeneous, instead of RCBD a CRD may be more appropriate.
If there are multiple sources of variability that cannot be controlled through blocking, more complex designs like Latin Square or Factorial Designs may be needed.
Common Mistakes to Avoid
Incorrect blocking or failure to account for key sources of variability.
Overcomplicating the design with too many blocks or treatments.
Ignoring assumptions like normality and homogeneity of variance.
Assumptions of CRBD Analysis
Normality: The residuals (errors) should be normally distributed.
Homogeneity of Variance: The variance of residuals should be constant across treatments and blocks.
Additivity: The effects of treatments and blocks should be additive (no interaction between treatments and blocks).
Statistical Analysis of Design
The statistical analysis of a CRBD typically involves Analysis of Variance (ANOVA), which partitions the total variability in the data into components attributable to treatments, blocks, and random error.
Formulate Hypothesis:
$H_0$: All the treatments are equal $S_1: At least two means are not equal
$H_0$: All the block means are equal $H_1$: At least two block means are not equal
Partition of the Total Variability:
The total sum of squares (SST) is divided into:
The sum of Squares due to Treatments (SSTr): Variability due to the treatments.
The sum of Squares due to Blocks (SSB): Variability due to the blocks.
The Sum of Squares due to Error (SSE): Unexplained variability (random error).
$$SST=SSTr+SSB+SSESST=SSTr+SSB+SSE$$
Degrees of Freedom
df Treatments: Number of treatments minus one ($t-1$).
df Blocks: Number of blocks minus one ($b-1$).
df Error: $(t-1)(b-1)$.
Compute Mean Squares:
Mean Square for Treatments (MSTr) = SSTr / df Treatments
Mean Square for Blocks (MSB) = SSB / df Blocks
Mean Square for Error (MSE) = SSE / df Error
Perform F-Tests:
F-Test for Treatments: Compare MSTr to MSE. $F=\frac{MSTr}{MSE}$ ​If the calculated F-value exceeds the critical F-value, reject the null hypothesis.
F-Test for Blocks: Compare MSB to MSE (optional, depending on the research question).
ANOVA for RCBD and Computing Formulas
Suppose, for a certain problem, we have three blocks and 4 treatments, that is 12 experimental units are analyzed, and the ANOVA table is
Randomized Complete Block Design is a powerful statistical tool for controlling variability and improving the precision of experiments. By understanding the principles, applications, and statistical analysis of RCBD, researchers, and statisticians can design more efficient and reliable experiments. Whether in agriculture, medicine, or industry, CRBD provides a robust framework for testing hypotheses and drawing meaningful conclusions.
A strong grasp of data mining concepts is essential in today’s data-driven world. This quick question-and-answer guide will help you build a solid foundation, ensuring you understand the core principles behind this powerful field. I have compiled the most common questions (about data mining concepts) with concise answers, making it easy to grasp the fundamental principles of data mining.
Table of Contents
Why are Traditional Techniques Unsuitable for Extracting Information?
The traditional techniques are usually unsuitable for extracting information because of
High dimensionality of data
Enormity of data
Heterogeneous, distributed nature of data
What is Meant by Data Mining Concepts?
“Data mining concepts” refer to the fundamental ideas and techniques used for extracting valuable information from large datasets. It is about understanding how to find meaningful patterns, trends, and knowledge within raw data. The key techniques of data mining concepts are:
Classification
Clustering
Regression
Association Rule mining
Anomaly Detection
What Technological Drivers Are Required in Data Mining?
The technological drivers required in data mining are:
Database size: A powerful system is required to maintain and process a huge amount of data.
Query Complexity: To analyze the complex and large number of queries, a more powerful system is required.
Cloud Computing: Cloud platforms provide the scalability and flexibility needed to handle large data mining projects. It offers access to on-demand computing power, storage, and specialized data mining tools.
High-Performance Computing: Complex data mining tasks require significant computational power, making HPC systems essential for processing huge amounts of datasets and running intensive algorithms.
Programming Languages and Tools: Languages such as R and Python are widely used in data mining due to the availability of extensive libraries for data analysis and machine learning. The data mining software such as IBM, and others, provide comprehensive data mining capabilities.
What do OLAP and OLTP Stand For?
OLAP is an acronym for Online Analytical Processing and OLTP is an acronym for Online Transactional Processing.
What is OLAP?
In a multidimensional model, the data is organized into multiple dimensions, where each dimension contains multiple levels of abstraction defined by concept hierarchies. OLAP provides a user-friendly environment for interactive data analysis.
List the Types of OLAP Server
There are four types of OLAP servers, namely Relational OLAP, Multidimensional OLAP, Hybrid OLAP, and Specialized SQL Servers.
What is a Machine Learning-Based Approach to Data Mining?
Machine learning is mainly used in data mining because it covers automatic computing procedures, and is based on logical or binary operations. Machine learning generally follows the principle that allows us to deal with more general types of data including cases with varying numbers of attributes. Machine learning is one of the popular techniques used for data mining and artificial intelligence too. One may also focus on decision-tree approaches and the results are mainly evolved from the logical sequence of steps.
What is Data Warehousing?
A data warehouse is the repository of data and it is used for management decision support systems. A data warehouse consists of a wide variety of data that has a high level of business conditions a a single point in time. A data warehouse is a repository of integrated information that can be available for queries and analysis.
What is a Statistical Procedure Based Approach?
The statistical procedures are characterized by having a precise fundamental probability model and providing a probability of being in each class instead of a classification. One can assume the techniques that assume variable selection, transformation, and overall structuring of the problem.
A statistical procedure-based approach involves using mathematical models and techniques to analyze data, draw inferences, and make predictions. It relies on the principles of probability and statistics to quantify uncertainty and identify patterns within data. Key aspects of the statistical approach include:
Data Collection and Preparation: Careful collection and cleaning of data ensure its quality and relevance.
Model Selection: Selecting an appropriate statistical model that aligns with the data and research objectives.
Parameter Estimation: Estimating the parameters of the chosen model using statistical methods.
Hypothesis Testing: Evaluating the validity of hypotheses based on the data and the model.
Inference and Prediction: Drawing conclusions and making predictions based on the statistical analysis.
Quantifying uncertainty: using probabilities to understand the certainty of results.
Note that Statistical procedures can range from simple descriptive statistics to complex machine learning algorithms, and they are used in a wide variety of fields to gain insights from data.
Define Medata Data
Metadata is a data about data. One can say that metadata is the summarized data that leads to detailed data.
What is the Difference between Data Mining and Data Warehousing?
Data mining processes explore the data using queries and performing statistical analysis, machine learning algorithms, and pattern recognition. Data Mining helps in reporting, strategy planning, and visualizing meaningful data sets. Data warehousing is a process where the data is extracted from various resources and after that, it is verified and stored in a central repository. Data warehouses are designed for analytical purposes, enabling users to perform complex queries and generate reports for decision-making. It is important to note that data warehousing creates the data repository that data mining uses.