Basic Research Methods and Statistical Data Analysis | Book Publisher International

 

Research is an integral component of scientific enquiry and involves the objective investigation of phenomena. Statistical analyses provide an indispensable tool for conducting unbiased testing of scientific hypotheses. While qualitative research uses narratives, phenomenology, ethnographies, grounded theory and case studies in social or behavioural studies, quantitative approaches involve designed experiments and statistical analyses of instrument-based, performance, observational or attitude data. Valid statistical analyses rely on probability sampling to ensure random and unbiased collection of the data.  These include simple random sampling, systematic sampling, stratified random sampling and cluster sampling. Key concepts in statistics are central tendency, which is reflected by the mean, median and mode of a data set. Range, variance and standard deviation indicate the spread and variability of the data. The definition and classification of variables in a study is important as it specifies the type of data being collected, the statistical models that are appropriate, and the statistical test to be used.

 

Probability theory involves making predictions about the chances of the occurrence of events based on assumptions about the underlying probability process. Probability mass functions describe the possible outcomes and their probabilities for discrete random variables, while probability density functions are used to summarise the information in probability distributions for continuous random variables. Binomial and Poisson distributions are examples of discrete probability distributions, whereas t-distribution, normal distribution, Chi-square distribution and F-distribution are continuous distributions.  Degrees of freedom in statistics indicate the possible number for which a factor or parameter is “free to vary” and is usually one less than the number of variables in each source factor. Exploratory data analysis, done before the actual statistical analyses, helps researchers to understand the nature of the data and to choose the best methods to analyse it. Four types of EDA are univariate non-graphical, univariate graphical, multivariate non-graphical and multivariate graphical techniques.

 

Non-parametric tests are methods of analysing data that do not require the data to follow a distribution. They are generally used when the data do not meet the required assumptions for applying the parametric test, such as the t-test or one-way analysis of variance. The Chi-square goodness of fit test is used to evaluate the probability of an expected outcome when it can be approximated by a Chi-square distribution, and is commonly used for categorical data. The chi-square test of independence is used to determine whether two categorical variables are dependent upon each other or not. The Wilcoxon signed-rank test is used to compare two populations when the assumptions for the t-test do not hold. The Wilcoxon signed rank test can be used as a substitute for the paired t-test and employs both the magnitudes and signs of the differences between pairs of measurements that are ranked and compared to a fixed value D0. The Kruskal-Wallis test is an extension of the sum rank test used to compare more than two populations, and therefore, is an alternative to the one-way analysis of variance.

 

True experiments require random assignment of treatments to subjects, and the tests used assume that the data to be analysed are continuous and follow a normal (Gaussian) distribution. The t-test, completely randomised design, two-way analysis of variance, factorial experiments and split-plot or split-block designs are common experimental designs used in true experiments. When a statistical test establishes a significant difference among the treatments, it may be wished to further determine which treatments differ significantly from the others and which do not. Fisher’s Least Significant Difference and Tukey’s W procedure are two popular methods for conducting multiple means comparisons.

 

Regression analysis is done to establish the relationship between a dependent variable and one or more independent variables. Linear regression is used when a dependent variable is related to a single independent variable. The least-squares method minimises the sum of squares of the errors of prediction for fitting a straight line to the data set. When a straight line does not adequately represent the relationship between a dependent and independent variable, non-linear regression models may be used. These can include exponential, power or polynomial equation fitting of the data set. Multiple regression entails using a polynomial model relating a dependent variable to a set of quantitative independent variables.

 

Author(s) Details

Roshan Man Bajracharya
Kathmandu University, Nepal.

Please see the book here:- https://doi.org/10.9734/bpi/mono/978-81-990309-3-0

Optimisation of Cutting Parameters in Dry Turning of EN19 Steel Material | Chapter 7 | Engineering Research: Perspectives on Recent Advances Vol. 5

The thrust of cost-cutting, in manufacturing enterprises recently, had been focused on energy efficiency and quality improvement consideration, largely, as an effort to respond to environmental apprehensions regarding legislation, standardisation and market growth. In steel material mechanical cutting, on the lathe machine, the convergence adequacy of the cost-quality-time matrix effectually is contingent upon the supreme choice combination of the set cutting parameters. This experimental investigation examined, the minimisation of energy consumption and enhancement of the generated component surface quality, by optimising the applied machining parameter settings, as a means of fostering sustainability in the dry cutting of EN19 material by turning on the conventional lathe. Optimising mechanical machining operating parameters points to a significant challenge confronting the machine shops industry as they endeavour to realise reduced electrical energy consumption and improved component surface finish quality, generated from their businesses.  The study aimed to reconnoiter and establish the association of the machining process strategy factors with the consequence of minimum energy consumption and surface smoothness quality, of the components, as the machining input factors were adjusted from minimum to the highest level of setting respectively. Taguchi's Full Factorial investigational strategy was employed in organising the empirical machining experiments. Analysis of variance (ANOVA) and the main effects plot (MEP) signal-to-noise ratio optimising computation were utilised, in the study, to determine the impact of the variable input-cutting process factors on the dependent parameters – surface roughness and energy use. Optimum, minimum energy consumption and good surface roughness generating, machining conditions were determined. Results of the all-encompassing experimental investigation yielded optimum cutting conditions of, respectively, energy consumption minimisation (100 m/min cutting speed, 0.1 mm/rev feed rate and 0o rake angle) and surface smoothness quality (100 m/min cutting speed, 0.4 mm/rev feed rate and 0o rake angle). Validation experiments corroborated the results findings of the developed model within 4.7% variability.

 

Author (s) Details

 

N. Tayisepi
Department of Industrial and Manufacturing Engineering National University of Science and Technology, Bulawayo, Zimbabwe.

 

A N Mnkandla
Department of Industrial and Manufacturing Engineering National University of Science and Technology, Bulawayo, Zimbabwe.

 

G Tigere
Department of Industrial and Manufacturing Engineering Harare Institute of Technology, Harare, Zimbabwe.

 

O Gwatidzo
Department of Industrial and Manufacturing Engineering Harare Institute of Technology, Harare, Zimbabwe.

 

L M Wagoneka
Department of Industrial and Manufacturing Engineering Harare Institute of Technology, Harare, Zimbabwe.

 

E Ndala
Department of Industrial and Manufacturing Engineering Harare Institute of Technology, Harare, Zimbabwe.

 

Please see the book here:- https://doi.org/10.9734/bpi/erpra/v5/2645

Statistical Grouping of Countries Based on Fertilizer Consumption | Chapter 8 | Science and Technology - Recent Updates and Future Prospects Vol. 4

Fertilizers are crucial in modern agriculture which provides essential plant nutrients to enhance crop yields. This study investigates fertilizer consumption patterns across 41 countries from 1961 to 2021. Fertilizer consumption data was obtained from the World Bank's World Development Indicators database. The dataset includes fertilizer consumption data for 41 countries from 1961 to 2021. Descriptive statistics reveal substantial variation in average fertilizer use (kg/hectare) and variability between countries. Statistical analyses confirm significant differences in consumption patterns, with fertilizer consumption identified as a major explanatory factor. Cluster analysis further differentiates countries into two groups based on their consumption levels. These findings highlighted the need to consider fertilizer use within a country-specific context. The collected data suggests that fertilizer consumption patterns differ considerably, with some countries exhibiting high average use and others demonstrating lower consumption levels. Further research exploring the underlying factors driving these variations and their impact on crop yields could inform strategies for optimizing fertilizer use and promoting agricultural sustainability.

 

Author(s)details:-

 

Dr. Rajarathinam Arunachalam
Department of Statistics, Manonmaniam Sundaranar University, India.

 

Please See the book here :- https://doi.org/10.9734/bpi/strufp/v4/597

Using Grey Relational Analysis for Optimization of Dry Sliding Wear Parameters of Aluminium Matrix Composites (AA7068/TiC) | Chapter 4 | Current Approaches in Engineering Research and Technology Vol. 1

The present study highlights about multi-Objective Optimization of Dry sliding wear parameters of Aluminium Matrix Composites (AA7068/TiC) using Grey Relational Analysis. Metal matrix composites are supplanting conventional materials due to their prevalent properties like high strength of weight ratio, high specific stiffness, high fracture toughness, high thermal stability and wear resistance etc. AA7068 is one of the industrially accessible strongest aluminium alloys that was taken as a matrix material and the reinforcement is titanium carbide (TiC) particles of 4 µm size. In this investigation, Al-TiC composites consist of TiC particles of an average size 4µm whose wt% of reinforcement varied from 2 to 10 wt% in steps of 2 wt%, composites have been prepared using the stir casting technique. Dry-sliding wear experiments have been performed on pin-on-disc apparatus according to Taguchi’s L25 in the design of experiments. The parameters considered are wt% of TiC, rotational speed (Nr), load (P) and sliding velocity (Vs). The motivation behind the Analysis of Variance is to figure out the process parameter that strongly influences the wear characteristics of AA7068/TiC MMCs. This can be accomplished by estimating the amount of the sum of squared deviations from the total mean of the grey relational grade for each process parameter and their error variance. Optimum combinations of parameters have been identified based on grey relational grade (GRG) to solve the wear response of AA7068/TiC MMCs. Also, analysis of variance (ANOVA) is applied to recognize the main factors affecting the wear response. Confirmation experiments with optimum conditions show that the results were nearer to the anticipated outcomes. The confirmation experiments confirm that the proposed GRA can track down the optimal combination of process parameters with multiple quality characteristics.

Author(s) Details:

Syed Altaf Hussain,
Department of Mechanical Engineering, Rajeev Gandhi Memorial College of Engineering & Technology, Nandyal-51850, India.

Please see the link here: https://stm.bookpi.org/CAERT-V1/article/view/14159

Design of Experiments for Milling Al2024-T4 under Optimum Lubricant Use Using Taguchi Method | Chapter 4 | Theory and Applications of Engineering Research Vol. 5

Performance of the machining and the efficiency of milling operation depend on several process variables among which hardness of work material is of great significant. The use of cutting fluids is an important part of a machining process system. Without cutting fluid, tools have only a short life, which makes the machining process costly. In this study, experimentation was carried out to investigate the effect of work hardness on end milling process. Workpiece material hardness is used as a noise factor. Input parameters used are spindle speed, feed; depth of cut and tool diameter. The experiments performed under wet and minimum Quantity lubrication and results of both compared. further for getting optimal lubricant conditions the experiments performed for various levels of flow rates of minimum quantity lubrication to get the best optimal setting. Taguchi method is used for single objective optimization. The S/N is used by Taguchi approach to analyze experimental data. Output parameters are surface roughness, material removal rate, cutting force and tool wear. Design of Experiment (DOE) with Taguchi L27 Orthogonal Array (OA) has been explored to produce 27 specimens on Al2024 aluminium by end milling operation at three different levels of hardness of material. The present study can be used for implementing the Minimum Quantity Lubrication Technique in place of conventional Wet Lubrication. The experiments performed under wet and minimum quantity lubrication condition and results compared. Further For optimal lubricant condition the experiments performed at various flow rate of Minimum Quantity Lubrication and “best” optimal setting is identified.

Author(s) Details:

Shilpa B. Sahare,
Department of Mechanical Engineering, Yeshwantrao Chavan College of Engineering, Wanadongri 441110, Nagpur, India.

Sachin P. Untawale,
Department of Mechanical Engineering, Yeshwantrao Chavan College of Engineering, Wanadongri 441110, Nagpur, India.

Prashant D. Kamble,
Department of Mechanical Engineering, Yeshwantrao Chavan College of Engineering, Wanadongri 441110, Nagpur, India.

S. S. Chaudhari,
Raisoni Group of Institution, Maharashtra, India.

Trupati Balvir,
Department of Anatomy, Datta Meghe Medical College Wanadongri, Hingana, Nagpur, Maharashtra, India.

Please see the link here: https://stm.bookpi.org/TAER-V5/article/view/13295

Processing and Characterization of Tribological Properties of Al6061-TiB2 Composite by Taguchi Technique | Chapter 4 | Technological Innovation in Engineering Research Vol. 5

In today's engineering design and development operations, composite materials, which are the combination of two or more materials that are distinct from one another in form and chemical composition, are rapidly assuming increasing significance as a structural material. Many academics are working hard right now to create new and innovative materials for a variety of engineering applications employing various AMCs.

In order to explore the tribological behaviour, attempts are undertaken in this research to create Al-6061/TiB2 composites with varied weight percentages of reinforcement via in-situ stir casting. To assess wear loss and coefficient of friction, abrasive wear experiments were carried out using a pin-on-disc (POD) wear test device in accordance with the Design of Experiments (DOE). The wear loss and COF were further optimised using the Taguchi Technique, Analysis of Variance (ANOVA), and Regression studies. To further examine the wear behaviour of the produced composites, scanning electron microscopy (SEM) photographs of worn-out surfaces are employed. With a rise in the weight percent of reinforcement, wear loss and COF decrease. The ANOVA and regression analyses lead to the conclusion that reinforcement is essential to the growth of the Al 6061-TiB2 composite system.

Author (s) Details:

H. S. Manjunatha,
Department of Mechanical Engineering, JSS Science and Technology University, SJCE, Mysuru 570006, Karnataka, India.

V. T. Satish,
Department of Mechanical Engineering, MCE, Hassan, Karnataka, India.

G. Mallesh,
Department of Mechanical Engineering, Sri Jayachamarajendra College of Engineering, Mysuru, Karnataka, India.

S. Ezhil Vannan,
Department of Mechanical Engineering, MCE, Hassan, Karnataka, India.

Please see the link here: https://stm.bookpi.org/TIER-V5/article/view/7463

Exploring Effective Feature Quantities for Machine Learning to Predict Developmental Degree of Musical Expression in Early Childhood: A Recent Study | Chapter 7 | Current Research in Language, Literature and Education Vol. 7

In order to create an evaluation model based on those feature quantities, this study aims to identify the developmental features of musical expression in young children from the perspective of changing body movement aspects.

In this study, the author examined brand-new feature amounts for machine learning classification and discriminating of the level of musical expression in young children. First, the author provided evidence for the findings of statistical analysis of movement components in early childhood musical expression utilising 3D motion capture and machine learning to assess levels of musical development. In this study, full-body motions were first subjected to an ANOVA. A three-way non-repeated ANOVA was used to quantitatively assess the motion capture data of 3-, 4-, and 5-year-old children in child facilities (n=178). Consequently, there was a statistically significant variation in how the bodily parts moved. Right hand movements, including moving distance and moving average acceleration, showed a significant difference. Second, machine learning techniques such as decision trees, the Sequential Minimum Optimization algorithm (SMO), support vector machines (SVM), and neural networks (multi-layer perceptrons) were used to construct classification models for evaluating the degree of musical development as determined by educators using simultaneously recorded children's video and related motion capture data. The multi-layer perceptron gave the best confusion matrix results among the various trained classification models, and it showed reasonable classifying precision and utility to support educators in assessing children's musical development stages. As a result of multilayered perceptron machine learning, the movement of the pelvis has a significant correlation with the degree of musical progression. Its consistency in categorization accuracy suggests that the model may be used to assist educators in determining how well youngsters can express themselves musically.

The author then provided some results of eye tracking on musical expression in a recent study based on the classification and discriminating by machine learning of the developmental degree of musical expression in order to figure out additional feature quantities. In order to research human bodily reaction in relation to cognitive and emotional components, eye-tracking is now often employed. According to the author, eye-tracking data on gazepath, fixations, and saccades provides information that can help us grasp musical expression. Children at child facilities aged 3, 4, and 5 years old (n=118) took part in eye tracking while singing a song while wearing an eye tracker (Tobii3). On the calculated data, quantitative analysis using ANOVA was done. The rise in data, including the frequency and magnitude of saccades as well as the saccade's moving average velocity, revealed that saccades during early childhood musical expression tended to be greater in major keys than in minor keys. The outcome demonstrated that it was possible to extract useful feature values for machine learning from the computed data of eye movement during musical expression.

Author(s) Details:

Mina Sano,
Tokoha University, Japan.

Please see the link here: https://stm.bookpi.org/CRLLE-V7/article/view/7540

Transformation of Data in Agricultural Research | Chapter 7 | Current Topics in Agricultural Sciences Vol. 8

When variances are diverse and some functions of means, data transformation is the most suitable corrective action. This method involves converting the original data to a new scale, producing a new data set that is anticipated to fulfil the homogeneity of variances. The comparative values between treatments are not changed, and comparisons between them are still valid, because a same transformation scale is applied to all data. The remedy for heterogeneity in trials, when certain treatments have mistakes that are noticeably larger (lower) than others because of the nature of the treatments investigated, is called error partitioning. We covered the most popular data transformation methods in this chapter along with instances from the real world.

Author(s) Details:

Bhim Singh,
Department of Basic Science, College of Agriculture, Sardar Vallabhbhai Patel University of Agriculture and Technology, Meerut, (U.P.), India.

Amar Singh,
Department of Agricultural Statistics, CSSS PG College (Affiliated to CCS University, Meerut, U.P.), Machhra, Meerut, (U.P.), India.

Prerna Sharma,
Department of Basic Science, College of Agriculture, Sardar Vallabhbhai Patel University of Agriculture and Technology, Meerut, (U.P.), India.

Please see the link here: https://stm.bookpi.org/CTAS-V8/article/view/7256

Study of the Compressive Strength Characteristics of Cement Mortar Reinforced with Kenaf Fibre | Chapter 14 | Recent Trends in Chemical and Material Sciences Vol. 2

 Kenaf fibre is one of the various products derived from the kenaf plant, which is found in abundance in many places of the globe. The use of kenaf fibre to strengthen cement mortars as a building material yielded positive results in research. Cement mortar mixtures were proportioned to include 1-3 percent fibre volume and 10-30mm fibre length. The composite's performance is measured in terms of density, water absorption, and compressive strength. According to the findings, water absorption increased with fibre volume but remained within ASTM C 211-77 limitations. The density of the kenaf fibre cement mortar did not change significantly. There is no statistical difference between the means of the compressive strength of plain mortar and those containing 1-3 percent fibre volume at 10mm fibre length, according to an analysis of variance (ANOVA) at a 5% level of significance. The compressive strength data revealed a substantial link between fibre volume, fibre length, and curing age when regression models were built.

Author (S) Details

T. M. Omoniyi
Civil Engineering Department, Abubakar Tafawa Balewa University, PMB 0248 Bauchi, Bauchi State, Nigeria.

Duna Samson
DG/CEO, Nigerian Building and Road Research Institute, Abuja, Nigeria.

Othman Musa Waila
Civil Engineering Department, Abubakar Tafawa Balewa University, PMB 0248 Bauchi, Bauchi State, Nigeria.

View Book :- https://stm.bookpi.org/RTCAMS-V2/article/view/2915

Analyzing the Impact of Employment on Gold Buying: An Indian Perspective | Chapter 5 | Insights into Economics and Management Vol. 9

 Since ancient times, the uniqueness of gold has been widely debated in India. The Indian government has taken many steps to limit gold consumption in order to reduce the impact on the trade deficit and foreign exchange reserves. Their efforts, however, have been in vain. This study paper was written in light of the foregoing, as well as the significant demand for gold in the form of jewelry. This research aims to investigate the factors that influence retail gold purchases as well as the effects of reference groups on gold purchases. The study's main emphasis was retail consumers, and data was obtained from a sample of 600 of them. Using factor analysis, several factors were discovered. KMO (0.903) and Bartlet tests proved the validity and reliability of factor analysis (Sig. 0.000). The impact of reference groups/employment function on the identified factors was determined using ANOVA. For several of the identified components, the ANOVA results suggested that the proposed hypothesis was statistically significant. The Tukey Post Hoc Test revealed that matching other functions of work (retired) with either Marketing or IT resulted in statistically significant differences in means. Our research found that one of the social components, namely the job function, had a similar effect on "gold." Gold has been the subject of secondary research in both the foreign and Indian contexts for several years. However, given the negative impact of gold purchases (jewelry) on India's trade deficit and other government actions, this research is relevant in the Indian context. More so now since India is a developing country, and globalization and technology have made it simple and practical to buy or invest in gold in the form of paper rather than the physical form.

Author (s) Details

Swati Shrikant Godbole
Department of Finance and General Management, K. J. Somaiya Institute of Management Studies and Research, Mumbai, India.

Gita Sashidharan
Department of Finance and General Management, K. J. Somaiya Institute of Management Studies and Research, Mumbai, India.

View Book :- https://stm.bookpi.org/IEAM-V9/article/view/943

Evaluating Equity Crowd Funding Platforms in Europe - A New Financial Phenomenon for Gen-Z Entrepreneurs | Chapter 1 | Current Strategies in Economics and Management Vol.6

 

Today’s α is Tomorrow’s β and Future’s γ. The fresh winds and waves in the Science of Analytics,
Digitalisation, Artificial Intelligence (AI), Virtual and Viral Technologies forcing the ‘Alternative
Investing’ into the 3-Phase. Equity Crowd Funding evolved as an ‘Alternative Investment-3.0’ (KPMG)
and emerged as a new financial phenomenon for raising capital from the crowd online (Forbes). GenZ Entrepreneurs are plugged in like no other Generations and targeting Start-Ups and Small
Businesses to focus on next wave of growth. The Equity CrowdFunding is set to be valued more than
$93 billion by 2025, is an indicative of its progress in recent years(World Bank).
In European Context, the positive and progressive tax regime and regulations, high volumes and
valorem, independency and transparency of European Taxonomy, the crystal and clear regulatory
and legislative system in Europe, indeed, a mooting point for the study. The EU capital seekers
(participants) and capital providers (crowd funders) differ according to the needs and preferences.
Hence, the ECF is on the high rise and rapidly evolving financial landscape that connects Start-Ups,
Stand-Ups and Small Business to flourish in EU. The present study is diagnostic, innovative, and exploratory and considers Top-12 Operative ECF Platforms in Europe. The prime objective of the study is to evaluate the financial as well as operational performance of ECF. The ‘Inductive Content Analysis, Cross Sectional Study, and One-Way ANOVA’ form the basis, in order to get insights and draw inferences. The analytics and dynamics of ECF are presented and recommended ‘Regulatory Sand-Box, Algorithm-Driven Mechanism and Market Orientation and Digital Compliance’ to attract and retain investors. In dictum, the future, progress and prosperity of ECF rely on the ignited interests, innovative ideas, and inspiring involvement of Gen-Z Entrepreneurs, Intrapreneurs, Investors, and Policy Makers in tot .

Author (s) Details

Dr. K. Bhanu Prakash
Professor &CFO, MN INFRA PVT., LTD., Tadepalligudem, Andhra Pradesh, India.

Mr. P. Siva Reddy
Assistant Professor, School of Management Studies, Lakireddy Bali Reddy College of Engineering (Autonomous), Mylavaram, Krishna District, Andhra Pradesh, India.

View Book :- http://bp.bookpi.org/index.php/bpi/catalog/book/254