This document discusses various types of experimental designs used in research, including factorial designs, response surface methods, and historical research. Factorial designs are used to study the effects of multiple factors simultaneously and identify interactions. Response surface methods employ designs like central composite designs and Box-Behnken designs to model nonlinear responses and find optimal conditions. Historical research systematically collects and evaluates past data without experimentation to understand and explain past events as accurately as possible through primary and secondary sources.
Formulation and development is a process of selection of component and processing.
Now days computer tools used in the formulation and development of pharmaceutical product.
Various technique, such as design of experiment are implemented for optimization of formulation and processing parameter.
Many times finding the correct answer is not simple and straight forward in such cases use of computer tools (optimization procedure) for best compromise is the smarter way to solve problem.
Optimization techniques and various method of optimization
Full Factorial Design
Introduction to Contour Plots
Introduction of Response Surface Design
Classification
Characteristics of Design
Matrix and analysis of design with case study
Equation of Full and Reduced mathematical models in experimental designs
Central Composite designs
Taguchi and mixtures designs
Application of experimental designs in pharmacology for reduction of animal
Formulation and development is a process of selection of component and processing.
Now days computer tools used in the formulation and development of pharmaceutical product.
Various technique, such as design of experiment are implemented for optimization of formulation and processing parameter.
Many times finding the correct answer is not simple and straight forward in such cases use of computer tools (optimization procedure) for best compromise is the smarter way to solve problem.
Optimization techniques and various method of optimization
Full Factorial Design
Introduction to Contour Plots
Introduction of Response Surface Design
Classification
Characteristics of Design
Matrix and analysis of design with case study
Equation of Full and Reduced mathematical models in experimental designs
Central Composite designs
Taguchi and mixtures designs
Application of experimental designs in pharmacology for reduction of animal
Optimization techniques in formulation Development Response surface methodol...D.R. Chandravanshi
The term “optimize” is “to make as perfect”. It is defined as follows: choosing the best element from some set of variable alternatives.
An art ,process ,or methodology of making something (a design system or decision ) as perfect ,as functional, as effective as possible .
Optimization techniques in formulation Development Response surface methodol...D.R. Chandravanshi
The term “optimize” is “to make as perfect”. It is defined as follows: choosing the best element from some set of variable alternatives.
An art ,process ,or methodology of making something (a design system or decision ) as perfect ,as functional, as effective as possible .
In this ppt the viewer will able to know about designing of experiments. How experimental design helps to improve the quality & purity of the products. In this example, our experimental design is a planned experiment that is used to determine how reactor temperature and residence time affect purity so we can find the optimum operating conditions. Experimental design is needed to rectify the error in materials, methods & machines.
Portion explained:
1. Introduction to the problem
2. EXPERIMENTAL DESIGN TERMINOLOGY
3. EXPERIMENTAL DESIGN DATA
4. EFFECTS AND MAIN EFFECTS
5. INTERACTIONS BETWEEN FACTORS
6. ARE THE EFFECTS, MAIN EFFECTS AND INTERACTIONS SIGNIFICANT?
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.
Experimental design is a way to carefully plan experiments in advance so that results are both objective and valid. Ideally, an experimental design should:
• Describe how participants are allocated to experimental groups. A common method is completely randomized design, where participants are assigned to groups at random. A second method is randomized block design, where participants are divided into homogeneous blocks (for example, age groups) before being randomly assigned to groups.
• Minimize or eliminate confounding variables, which can offer alternative explanations for the experimental results.
• Allows making inferences about the relationship between independent variables and dependent variables.
• Reduce variability, to make it easier to find differences in treatment outcomes.
Types of Experimental Design
1. Between Subjects Design.
2. Completely Randomized Design.
3. Factorial Design.
4. Matched-Pairs Design.
5. Observational Study
• Longitudinal Research
• Cross Sectional Research
6. Pretest-Posttest Design.
7. Quasi-Experimental Design.
8. Randomized Block Design.
9. Randomized Controlled Trial
10. Within subjects Design.
NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
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Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
Acute scrotum is a general term referring to an emergency condition affecting the contents or the wall of the scrotum.
There are a number of conditions that present acutely, predominantly with pain and/or swelling
A careful and detailed history and examination, and in some cases, investigations allow differentiation between these diagnoses. A prompt diagnosis is essential as the patient may require urgent surgical intervention
Testicular torsion refers to twisting of the spermatic cord, causing ischaemia of the testicle.
Testicular torsion results from inadequate fixation of the testis to the tunica vaginalis producing ischemia from reduced arterial inflow and venous outflow obstruction.
The prevalence of testicular torsion in adult patients hospitalized with acute scrotal pain is approximately 25 to 50 percent
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
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Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
Report Back from SGO 2024: What’s the Latest in Cervical Cancer?bkling
Are you curious about what’s new in cervical cancer research or unsure what the findings mean? Join Dr. Emily Ko, a gynecologic oncologist at Penn Medicine, to learn about the latest updates from the Society of Gynecologic Oncology (SGO) 2024 Annual Meeting on Women’s Cancer. Dr. Ko will discuss what the research presented at the conference means for you and answer your questions about the new developments.
ARTIFICIAL INTELLIGENCE IN HEALTHCARE.pdfAnujkumaranit
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. It encompasses tasks such as learning, reasoning, problem-solving, perception, and language understanding. AI technologies are revolutionizing various fields, from healthcare to finance, by enabling machines to perform tasks that typically require human intelligence.
Prix Galien International 2024 Forum ProgramLevi Shapiro
June 20, 2024, Prix Galien International and Jerusalem Ethics Forum in ROME. Detailed agenda including panels:
- ADVANCES IN CARDIOLOGY: A NEW PARADIGM IS COMING
- WOMEN’S HEALTH: FERTILITY PRESERVATION
- WHAT’S NEW IN THE TREATMENT OF INFECTIOUS,
ONCOLOGICAL AND INFLAMMATORY SKIN DISEASES?
- ARTIFICIAL INTELLIGENCE AND ETHICS
- GENE THERAPY
- BEYOND BORDERS: GLOBAL INITIATIVES FOR DEMOCRATIZING LIFE SCIENCE TECHNOLOGIES AND PROMOTING ACCESS TO HEALTHCARE
- ETHICAL CHALLENGES IN LIFE SCIENCES
- Prix Galien International Awards Ceremony
2. 2
Factorial design
These are the designs of choice for simultaneous
determination of the effects of several factors & their
interactions.
Used in experiments where the effects of different factors or
conditions on experimental results are to be elucidated.
Two types
Full factorial- Used for small set of factors
Fractional factorial- Used for optimizing more number of
factors
2012 CIPS Guntur
3. One Factor Designs
These are the designs where only one factor is under investigation, and
the objective is to determine whether the response is significantly
different at different factor levels. The factor can be qualitative or
quantitative. In the case of qualitative factors (e.g. different suppliers,
different materials, etc.), no extrapolations (i.e. predictions) can be
performed outside the tested levels, and only the effect of the factor on
the response can be determined. On the other hand, data from tests
where the factor is quantitative (e.g. temperature, voltage, load, etc.) can
be used for both effect investigation and prediction, provided that
sufficient data are available.
4. 2 Factorial Designs
In factorial designs, multiple factors are investigated
simultaneously during the test. As in one factor designs,
qualitative and/or quantitative factors can be considered. The
objective of these designs is to identify the factors that have a
significant effect on the response, as well as investigate the effect
of interactions (depending on the experiment design used).
5. • Predictions can also be performed when quantitative factors are
present, but care must be taken since certain designs are very
limited in the choice of the predictive model. For example, in
two level designs only a linear relationship between the
response and the factors can be used, which may not be
realistic.
6. • General Full Factorial Designs
In general full factorial designs, each factor can have a different
number of levels, and the factors can be quantitative, qualitative or
both.
• Two Level Full Factorial Designs
These are factorial designs where the number of levels for each factor
is restricted to two. Restricting the levels to two and running a full
factorial experiment reduces the number of treatments (compared to
a general full factorial experiment) and allows for the investigation
of all the factors and all their interactions. If all factors are
quantitative, then the data from such experiments can be used for
predictive purposes, provided a linear model is appropriate for
modeling the response (since only two levels are used, curvature
cannot be modeled).
7. • Two Level Fractional Factorial Designs
This is a special category of two level designs where not all
factor level combinations are considered and the experimenter
can choose which combinations are to be excluded. Based on the
excluded combinations, certain interactions cannot be
determined.
8. • Plackett-Burman Designs
This is a special category of two level fractional factorial
designs, proposed by R. L. Plackett and J. P. Burman, where
only a few specifically chosen runs are performed to investigate
just the main effects (i.e. no interactions).
• 3 Response Surface Method Designs
These are special designs that are used to determine the settings
of the factors to achieve an optimum value of the response
9. Basic terminology
Factor
A factor is an assigned variable such as concentration, temperature, lubricating agent,
drug treatment,
or diet
Levels
The levels of a factor are the values or designations assigned to the factor. Examples
of levels are 30◦ and 50◦ for the factor ‘temperature,” 0.1 molar and 0.3 molar for the
factor “concentration,” and “drug” and “placebo” for the factor “drug treatment.”
10. The runs or trials that comprise factorial experiments consist of all combinations of all levels of all
factors.
11. • Effects The effect of a factor is the change in response caused by varying the level(s) of the factor.
The main effect is the effect of a factor averaged over all levels of the other factors. the main effect
due to drug would be the difference between the average response when drug is at the high level
(runs b and ab) and the average response when drug is at the low level [runs (1) and a].
12. • The design and analysis of experiments revolves around the understanding of the
effects of different variables on other variable(s). In mathematical jargon, the
objective is to establish a cause-and-effect relationship between a number of
independent variables and a dependent variable of interest. The dependent variable,
in the context of DOE, is called the response, and the independent variables are
called factors. Experiments are run at different factor values, called levels.
• Each run of an experiment involves a combination of the levels of the investigated
factors. Each of the combinations is referred to as a treatment.
13. • In a single factor experiment, each level of the factor is referred to as a
treatment. In experiments with many factors, each combination of the
levels of the factors is referred to as a treatment. When the same number of
response observations are taken for each of the treatments of an
experiment, the design of the experiment is said to be balanced.
14. • For this example the main effect can be
characterized as a linear response, since the
effect is the difference between the two points
shown in Figure 9.1.
15. • Figure 9.2 shows an example of a
curved (quadratic) response
based on experimental results
with a factor at three levels. In
many cases, an important
objective of a factorial experiment
is to characterize the effect of
changing levels of a factor or
combinations of factors on the
response variable.
16. Interaction
Interaction may be thought of as a lack of “additivity of factor effects.” For example, in a two factor
experiment, if factor A has an effect equal to 5 and factor B has an effect of 10, additivity
would be evident if an effect of 15 (5 + 10) were observed when both A and B are at their high levels
(in a two-level experiment).
If the effect is greater than 15 when both factors are at their high levels, the result is synergistic (in
biological notation) with respect to the two factors.
If the effect is less than 15 when A and B are at their high levels, an antagonistic effect is said to exist.
17. More specifically, this means that the drug effect
measured when the lubricant is at the low level [a−(1)] is
different from the drug effect measured when the
lubricant is at the high level (ab − b).
If the drug effects are the same in the presence
of both high and low levels of lubricant, the system is
additive, and no interaction exists.
18.
19. In the absence of interaction, factorial designs have maximum efficiency in estimating main
effects. If interactions exist, factorial designs are necessary to reveal and identify the interactions.
Since factor effects are measured over varying levels of other factors, conclusions apply to awide
range of conditions.
Maximum use is made of the data since all main effects and interactions are calculated fromall of the
data (as will be demonstrated below).
Factorial designs are orthogonal; all estimated effects and interactions are independent of
effects of other factors. Independence, in this context, means that when we estimate a main
effect, for example, the result we obtain is due only to the main effect of interest, and
is not influenced by other factors in the experiment.
In nonorthogonal designs (as is the
case in many multiple-regression-type “fits”—see App. III), effects are not independent.
Confounding is a result of lack of independence.
20. Example 1: TWO SIMPLE HYPOTHETICAL EXPERIMENTS TO ILLUSTRATE THE ADVANTAGES
OF FACTORIAL DESIGNS
The problem is to determine the effects of a special diet
and a drug on serum cholesterol levels
However, suppose that patients given neither drug nor diet
would have shown a decrease of serum cholesterol of 10
mg% had they been included in the experiment.
without a fourth group, the control group (low level of diet
and drug), we have no way of assessing the presence of
interaction
.
21. (1) Group on normal diet without drug (drug and special diet at low level).
a Group on drug only (high level of drug, low level of diet).
b Group on diet only (high level of diet, low level of drug).
ab Group on diet and drug (high level of drug and high level of diet).
22. We can assume that there is no interaction, a very
reasonable assumption in the present example. (The
weights of the combined items should be the sum of the
individual weights.) The estimate of interaction in this
example is
Example 2:
23. The experiment that we will analyze is designed to
investigate the effects of three components (factors)—In
this example, two levels were chosen for each factor
stearate, drug, and starch—on the thickness of a tablet
formulation
Example 3:
24. 24
LEVELS OF FACTORS IN THIS FACTORIAL DESIGN
FACTOR LOWLEVEL(mg) HIGH
LEVEL(mg)
A:stearate 0.5 1.5
B:Drug 60.0 120.0
C:starch 30.0 50.0
2012 CIPS Guntur
25. The average effects can be calculated using these signs as
follows. To obtain the average effect, multiply the response times
the sign for each of the eight runs in a column, and divide
the result by
2n−1, where n is the number of factors (for three factors, 2n−1 is equal to 4). This
will be illustrated for the calculation of the main effect of A (stearate). The main effect
for factor
A is
26.
27. The main effect of A is interpreted to mean that the net
effect of increasing the stearate
concentration from the low to the high level (averaged
over all other factor levels) is to increase
the tablet thickness by 0.022 cm. This result is illustrated
in Figure 9.6.
28.
29. 29
EXAMPLE OF FULL FACTORIAL EXPERIMENT
Factor
combination
Stearate Drug Starch Response
Thickness
Cm*103
(1) _ _ _ 475
a + _ _ 487
b _ + _ 421
ab + + _ 426
c _ _ + 525
ac + _ + 546
bc _ + + 472
abc + + + 522
2012 CIPS Guntur
30. Response surface designs
The following designs are widely used for fitting a
quadratic model:
• Central Composite Design (uniform precision of effect estimates)
• Box-Behnken Design (almost uniform precision of effect estimates, but
usually fewer runs required than for CCD)
The choice between these models is usually decided by the
availability of these designs for a given number of runs
and number of factors.
Note that there are other suitable designs (usually
available in statistical software that supports DOE).
32. Central Composite Design
A CCD consists of 3 parts:
• factorial points
• axial points
• centre points
A CCD is often executed by adding
points to an already performed
2p-design (highly efficient, but beware
of blocking!).
33. Rotatability
In a CCD there are 2 possible choices:
• number of centre points
• location axial points
By choosing the axial points at the locations (,0,…,0) etc.
with = (# factorial points)¼ , the design becomes rotatable,
i.e. the precision (variance) of the model depends on the
distance to the origin only. In other words, one has the same
precision for all factor estimates.
34. Box-Behnken designs
These are designs that consists
of
combinations from 2p-designs.
Properties:
• efficient (few runs)
• (almost) rotatable
• no corner points of
hypercube
(these are extreme
conditions
which are often hard to set)
35. Stationary point
Near the optimum usually a quadratic model
suffices:
j
i
j
i ij
i
k
i
ii
i
k
i
i x
x
x
x
Y 2
1
1
0
How do find the optimum after we correctly estimated the parameters
using a response surface design (CCD or Box-Behnken)?
The next slides show the tools to derive optimal settings and the pitfalls
that have to be avoided.
36.
37.
38. • But, when there are more than two independent variables, graphs are difficult or almost impossible
to use to illustrate the response surface, since it is beyond 3-dimension. For this reason, response
surface models are essential for analyzing the unknown function f.
• Response Surface Methods are designs and models for working with continuous treatments when
finding the optima or describing the response is the goal (Oehlert 2000). The first goal for Response
Surface Method is to find the optimum response. When there is more than one response then it is
important to find the compromise optimum that does not optimize only one response
• In this graph, each value of x1 and x2 generates a y-value. This three-dimensional graph shows the
response surface from the side and it is called a response surface plot.
39. • What is Historical Research?
The systematic collection and evaluation of data to describe, explain, and understand actions or
events that
occurred sometime in the past. There is no manipulation or control of variables as in experimental
research.
An attempt is made to reconstruct what happened during a certain period of time as completely and
accurately as possible.
40. • What is Historical Research?What is Historical Research? The
systematic collection and evaluation of data to describe,
explain, and understand actions or events that occurred
sometime in the past.There is no manipulation or control of
variables as in experimental research.An attempt is made to
reconstruct what happened during a certain period of time as
completely and accurately a
41. Steps Involved in Historical Researchin Historical Research
Defining the Problem
Locating relevant sources
Documents
Numerical records
Oral statements
Relics
Summarizing information obtained from historical sources
Evaluation of historical sources
Internal criticism External criticis
42. Numerical recordscan be considered as a separate type of
source in and of themselves or as a subcategory of documents.
Oral Statementsare stories or other forms of oral expression
that leave a record for future generations.
Relics are any objects whose physical or visual characteristics
can provide some information about the
43. Primary vs. Secondary Sources
A primary sourceis one prepared by an individual who was a
participant in or a direct witness to the event being described.
A secondary sourceis a document prepared by an individual
who was not a direct witness to an event, but who obtained a
description of the event from someone else
44. Internal criticism Accuracy, trustworthiness and veracity of
materials
Is the source the result of pressure, bias or vanity?
External criticism
Authenticity and genuineness of data
Is the source a forgery, a counterfeit or a hoax?
45. Data Analysis in Historical Research
Historical researchers use the following methods to make sense
out of large amounts of data:
Theoretical model leading to a content analysis
Use of patterns or themes
Coding system
Quantitative data to validate interpretations
46. Advantages
Permits investigation of topics and questions that can be studied
in no other fashion
Can make use of more categories of evidence than most other
methods (with the exception of case studies and ethnographic
studies)
Disadvantages
Cannot control for threats to internal validity
Limitations are imposed due to the content analysis
Researchers cannot ensure representation of the sample