This document describes a three-level factorial experiment with two factors: variety and spacing. It discusses main effects, interaction effects, and types of factorial experiments. The experiment uses three varieties of crops planted at three different spacing levels, for a total of nine treatment combinations. The results are analyzed to determine the sum of squares and averages for each variety and spacing level, as well as the interaction between variety and spacing.
Basic Concepts of Experimental Design & Standard Design ( Statistics )Hasnat Israq
This gives the basic description of Design and Analysis of Experiment . This is one of the most important topic in Statistics and also for Mathematics and for Researchers-Scientists
Basic Concepts of Experimental Design & Standard Design ( Statistics )Hasnat Israq
This gives the basic description of Design and Analysis of Experiment . This is one of the most important topic in Statistics and also for Mathematics and for Researchers-Scientists
Experimental design is inferential procedure or scientific method in Statistics wherein cause and effect relationship is studied by planning an experiment. In Experimental Design methodology, proper experiments are planned in order to achieve desired objective. Copy the link given below and paste it in new browser window to get more information on Experimental Design:- www.transtutors.com/homework-help/statistics/experimental-design.aspx
DESIGN OF EXPERIMENTS (DOE)
DOE is invented by Sir Ronald Fisher in 1920’s and 1930’s.
The following designs of experiments will be usually followed:
Completely randomised design(CRD)
Randomised complete block design(RCBD)
Latin square design(LSD)
Factorial design or experiment
Confounding
Split and strip plot design
FACTORIAL DESIGN
When a several factors are investigated simultaneously in a single experiment such experiments are known as factorial experiments. Though it is not an experimental design, indeed any of the designs may be used for factorial experiments.
For example, the yield of a product depends on the particular type of synthetic substance used and also on the type of chemical used.
ADVANTAGES OF FACTORIAL DESIGN.
Factorial experiments are advantageous to study the combined effect of two or more factors simultaneously and analyze their interrelationships. Such factorial experiments are economic in nature and provide a lot of relevant information about the phenomenon under study. It also increases the efficiency of the experiment.
It is an advantageous because a wide range of factor combination are used. This will give us an idea to predict about what will happen when two or more factors are used in combination.
DISADVANTAGES
It is disadvantageous because the execution of the experiment and the statistical analysis becomes more complex when several treatments combinations or factors are involved simultaneously.
It is also disadvantageous in cases where may not be interested in certain treatment combinations but we are forced to include them in the experiment. This will lead to wastage of time and also the experimental material.
2(square) FACTORIAL EXPERIMENT
A special set of factorial experiment consist of experiments in which all factors have 2 levels such experiments are referred to generally as 2n factorials.
If there are four factors each at two levels the experiment is known as 2x2x2x2 or 24 factorial experiment. On the other hand if there are 2 factors each with 3 levels the experiment is known as 3x3 or 32 factorial experiment. In general if there are n factors each with p levels then it is known as pn factorial experiment.
The calculation of the sum of squares is as follows:
Correction factor (CF) = (𝐺𝑇)2/𝑛
GT = grand total
n = total no of observations
Total sum of squares = ∑▒〖𝑥2−𝐶𝐹〗
Replication sum of squares (RSS) = ((𝑅1)2+(𝑅2)2+…+(𝑅𝑛)2)/𝑛 - CF
Or
1/𝑛 ∑▒𝑅2−𝐶𝐹
2(Cube) FACTORIAL DESIGN
In this type of design, one independent variable has 2 levels, and the other independent variable has 3 levels.
Estimating the effect:
In a factorial design the main effect of an independent variable is its overall effect averaged across all other independent variable.
Effect of a factor A is the average of the runs where A is at the high level minus the average of the runs
Basic Concepts of Standard Experimental Designs ( Statistics )Hasnat Israq
This gives the basic description of Design and Analysis of Experiment . This is one of the important topic in Statistics and also for Mathematics and for Researchers - Scientists . Good Luck .
LATIN SQUARE DESIGN RESEARCH DESIGN: DESCRIPTION OF LATIN SQUARE DESIGN, PROCEDURE, TABLES, LINEAR MODEL, ANALYSIS, ADVANTAGES AND DISADVANTAGES OF LATIN SQUARE DESIGN
Experimental design is inferential procedure or scientific method in Statistics wherein cause and effect relationship is studied by planning an experiment. In Experimental Design methodology, proper experiments are planned in order to achieve desired objective. Copy the link given below and paste it in new browser window to get more information on Experimental Design:- www.transtutors.com/homework-help/statistics/experimental-design.aspx
DESIGN OF EXPERIMENTS (DOE)
DOE is invented by Sir Ronald Fisher in 1920’s and 1930’s.
The following designs of experiments will be usually followed:
Completely randomised design(CRD)
Randomised complete block design(RCBD)
Latin square design(LSD)
Factorial design or experiment
Confounding
Split and strip plot design
FACTORIAL DESIGN
When a several factors are investigated simultaneously in a single experiment such experiments are known as factorial experiments. Though it is not an experimental design, indeed any of the designs may be used for factorial experiments.
For example, the yield of a product depends on the particular type of synthetic substance used and also on the type of chemical used.
ADVANTAGES OF FACTORIAL DESIGN.
Factorial experiments are advantageous to study the combined effect of two or more factors simultaneously and analyze their interrelationships. Such factorial experiments are economic in nature and provide a lot of relevant information about the phenomenon under study. It also increases the efficiency of the experiment.
It is an advantageous because a wide range of factor combination are used. This will give us an idea to predict about what will happen when two or more factors are used in combination.
DISADVANTAGES
It is disadvantageous because the execution of the experiment and the statistical analysis becomes more complex when several treatments combinations or factors are involved simultaneously.
It is also disadvantageous in cases where may not be interested in certain treatment combinations but we are forced to include them in the experiment. This will lead to wastage of time and also the experimental material.
2(square) FACTORIAL EXPERIMENT
A special set of factorial experiment consist of experiments in which all factors have 2 levels such experiments are referred to generally as 2n factorials.
If there are four factors each at two levels the experiment is known as 2x2x2x2 or 24 factorial experiment. On the other hand if there are 2 factors each with 3 levels the experiment is known as 3x3 or 32 factorial experiment. In general if there are n factors each with p levels then it is known as pn factorial experiment.
The calculation of the sum of squares is as follows:
Correction factor (CF) = (𝐺𝑇)2/𝑛
GT = grand total
n = total no of observations
Total sum of squares = ∑▒〖𝑥2−𝐶𝐹〗
Replication sum of squares (RSS) = ((𝑅1)2+(𝑅2)2+…+(𝑅𝑛)2)/𝑛 - CF
Or
1/𝑛 ∑▒𝑅2−𝐶𝐹
2(Cube) FACTORIAL DESIGN
In this type of design, one independent variable has 2 levels, and the other independent variable has 3 levels.
Estimating the effect:
In a factorial design the main effect of an independent variable is its overall effect averaged across all other independent variable.
Effect of a factor A is the average of the runs where A is at the high level minus the average of the runs
Basic Concepts of Standard Experimental Designs ( Statistics )Hasnat Israq
This gives the basic description of Design and Analysis of Experiment . This is one of the important topic in Statistics and also for Mathematics and for Researchers - Scientists . Good Luck .
LATIN SQUARE DESIGN RESEARCH DESIGN: DESCRIPTION OF LATIN SQUARE DESIGN, PROCEDURE, TABLES, LINEAR MODEL, ANALYSIS, ADVANTAGES AND DISADVANTAGES OF LATIN SQUARE DESIGN
Parameter Optimization of Shot Peening Process of PMG AL2024 Alloy CoverIOSRJMCE
Shot peening leads to local plastic deformations in the near-surface regions, which result in the development of compressive residual stress and the improvement of surface hardness in the aerospace structural components. These properties can be enhanced by careful selection of the peening parameters. PMG Cover of AL2024 Aluminum Alloy is widely used in the generator manufacturing cover due to its high specific static strength. In this study a Taguchi Grey Relational Analysis is presented to optimize the surface properties of residual stress, micro hardness. The effects of four peening parameters (Shot Diameter, Shot Velocity, Impact Angle, Nozzle Distance) on micro hardness and residual stress are investigated Design of Experiment work is carried out by MINITAB 14 software tools of Taguchi Grey relational method, for getting excellent shot peening process parameter combination by MAT LAB R2009 software tools of advanced Optimization method as Genetic Algorithm, Simulated Annealing. Compare of the above reading for the investigation.
Here is a piece of detailed information about the experimental design used in the field of statistics. This also features some information on the three most widely accepted and most widely used designs.
This presentation deals with the basics of design of experiments and discusses all the three basic statistical designs i.e. CRD, RBD and LSD. Further it explains the guidelines for developing experimental research.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
2. FACTORIAL
Factorial experiments involve
simultaneously more than one factor
each at two or more levels. Several
factors affect simultaneously the
characteristic under study in factorial
experiments and the experimenter is
interested in the main effects and the
interaction effects among different
factors.
3. MAIN EFFECT
The main effect of a factor is
defined to be the change in
response produced by a change
in the level of a factor.
The main effect of A is the
difference between the average
response at A1 and A2.
4. INTERACTION EFFECT
In some experiments we may find that
the difference in response between
the levels of one factor is not the
same at all levels of the other factor.
When this occurs, there is an
interaction between the factors.
5. TYPES OF FACTORIAL
Based on number of factors:
A factorial experiment is two factor factorial
experiment, three factor factorial
experiment. A factorial experiment with five
varieties and three doses of nitrogen is a
two factor factorial experiment. An
experiment with three irrigation schedule,
five varieties and three doses of nitrogen is
a three factor factorial experiment.
6. Based on level of factors:
A factorial experiment is either symmetrical
or asymmetrical depending upon the
equality or inequality in levels of factors put
under experimentation. A factorial
experiment is symmetrical, if the no. of
levels for all the factors are same. For
example, a two factor factorial experiment
with five varieties and five different doses
of nitrogen, is a symmetrical factorial
experiment. On the other hand a two factor
factorial experiment with five varieties and
any no. of doses of nitrogen, is an
asymmetrical factorial experiment.
7. ADVANTAGES
Factorial experiments give the opportunity to an
experimenter to combine the effects of more than one
factor at a time.
Compared to single factor experiments factorial
experiments are effective because of the fact that the
interaction effects can be worked out from these
experiments which is not possible in single factor
experiments.
Factorial experiments are not only time saving but also to
some extent cost saving also.
8. DISADVANTAGES
If the no. of factors or the levels of the factors or both the
no. and levels of factors are more, then the no. of
treatment combinations will be more, resulting in
requirement of bigger experimental area and bigger block
size. As the block size increases, it is very difficult under
field condition to maintain homogeneity among the plots
within the block. Thus there is a possibility of increasing
the experimental error vis-a-vis decrease precision of
experiment.
9. Statistical procedure and calculation of factorial
experiments are more complicated than single factor
experiments.
As the no. of factors or the levels of the factor or both
increases the no. of effects, including the interaction
effects also increases. Sometimes it becomes very
difficult to extract the information from interactions
particularly the higher order interaction effects.
Utmost care is needed to meticulously conduct the
experiment because the failure in one experiment may
result in loss of information greater compared to single
factor experiment.
10. 32 FACTORIAL
EXPERIMENT
This is the three level experiment. It has two
factors, each at three levels. Let the levels of A be
denoted by a0, a1, a2 and the levels of B be
denoted by b0, b1, b2. Since every factor has three
levels, 2 d.f. will be attached with each factor.
