1. A statistical hypothesis represents the mathematical relationship between two or more population parameters. It can be directional, specifying the exact relationship, or nondirectional, anticipating a difference but not specifying the direction.
2. The null hypothesis predicts no difference between population parameters, while the alternative or experimental hypothesis predicts a difference. Through statistical testing, we can either reject the null hypothesis in favor of the alternative, or accept the null hypothesis.
3. Significance testing uses statistical tests and probabilities to determine if sample data can be used to reject the null hypothesis involving population parameters. If the difference is unlikely to have occurred by chance when the null hypothesis is true, it is considered statistically significant.
Statistical tests of significance and Student`s T-TestVasundhraKakkar
Statistical tests of significance is explained along with steps involve in Statistical tests of significance and types of significance test are also mentioned. Student`s T-Test is explained
Statistical tests of significance and Student`s T-TestVasundhraKakkar
Statistical tests of significance is explained along with steps involve in Statistical tests of significance and types of significance test are also mentioned. Student`s T-Test is explained
01 parametric and non parametric statisticsVasant Kothari
Definition of Parametric and Non-parametric Statistics
Assumptions of Parametric and Non-parametric Statistics
Assumptions of Parametric Statistics
Assumptions of Non-parametric Statistics
Advantages of Non-parametric Statistics
Disadvantages of Non-parametric Statistical Tests
Parametric Statistical Tests for Different Samples
Parametric Statistical Measures for Calculating the Difference Between Means
Significance of Difference Between the Means of Two Independent Large and
Small Samples
Significance of the Difference Between the Means of Two Dependent Samples
Significance of the Difference Between the Means of Three or More Samples
Parametric Statistics Measures Related to Pearson’s ‘r’
Non-parametric Tests Used for Inference
Parametric and non parametric test in biostatistics Mero Eye
This ppt will helpful for optometrist where and when to use biostatistic formula along with different examples
- it contains all test on parametric or non-parametric test
Today’s overwhelming number of techniques applicable to data analysis makes it extremely difficult to define the most beneficial approach while considering all the significant variables.
The analysis of variance has been studied from several approaches, the most common of which uses a linear model that relates the response to the treatments and blocks. Note that the model is linear in parameters but may be nonlinear across factor levels. Interpretation is easy when data is balanced across factors but much deeper understanding is needed for unbalanced data.
Analysis of variance (ANOVA) is a collection of statistical models and their associated estimation procedures (such as the "variation" among and between groups) used to analyze the differences among means. ANOVA was developed by the statistician Ronald Fisher. ANOVA is based on the law of total variance, where the observed variance in a particular variable is partitioned into components attributable to different sources of variation. In its simplest form, ANOVA provides a statistical test of whether two or more population means are equal, and therefore generalizes the t-test beyond two means. In other words, the ANOVA is used to test the difference between two or more means.Analysis of variance (ANOVA) is an analysis tool used in statistics that splits an observed aggregate variability found inside a data set into two parts: systematic factors and random factors. The systematic factors have a statistical influence on the given data set, while the random factors do not. Analysts use the ANOVA test to determine the influence that independent variables have on the dependent variable in a regression study.
Sir Ronald Fisher pioneered the development of ANOVA for analyzing results of agricultural experiments.1 Today, ANOVA is included in almost every statistical package, which makes it accessible to investigators in all experimental sciences. It is easy to input a data set and run a simple ANOVA, but it is challenging to choose the appropriate ANOVA for different experimental designs, to examine whether data adhere to the modeling assumptions, and to interpret the results correctly. The purpose of this report, together with the next 2 articles in the Statistical Primer for Cardiovascular Research series, is to enhance understanding of ANVOA and to promote its successful use in experimental cardiovascular research. My colleagues and I attempt to accomplish those goals through examples and explanation, while keeping within reason the burden of notation, technical jargon, and mathematical equations.
Through this ppt you could learn what is Wilcoxon Signed Ranked Test. This will teach you the condition and criteria where it can be run and the way to use the test.
01 parametric and non parametric statisticsVasant Kothari
Definition of Parametric and Non-parametric Statistics
Assumptions of Parametric and Non-parametric Statistics
Assumptions of Parametric Statistics
Assumptions of Non-parametric Statistics
Advantages of Non-parametric Statistics
Disadvantages of Non-parametric Statistical Tests
Parametric Statistical Tests for Different Samples
Parametric Statistical Measures for Calculating the Difference Between Means
Significance of Difference Between the Means of Two Independent Large and
Small Samples
Significance of the Difference Between the Means of Two Dependent Samples
Significance of the Difference Between the Means of Three or More Samples
Parametric Statistics Measures Related to Pearson’s ‘r’
Non-parametric Tests Used for Inference
Parametric and non parametric test in biostatistics Mero Eye
This ppt will helpful for optometrist where and when to use biostatistic formula along with different examples
- it contains all test on parametric or non-parametric test
Today’s overwhelming number of techniques applicable to data analysis makes it extremely difficult to define the most beneficial approach while considering all the significant variables.
The analysis of variance has been studied from several approaches, the most common of which uses a linear model that relates the response to the treatments and blocks. Note that the model is linear in parameters but may be nonlinear across factor levels. Interpretation is easy when data is balanced across factors but much deeper understanding is needed for unbalanced data.
Analysis of variance (ANOVA) is a collection of statistical models and their associated estimation procedures (such as the "variation" among and between groups) used to analyze the differences among means. ANOVA was developed by the statistician Ronald Fisher. ANOVA is based on the law of total variance, where the observed variance in a particular variable is partitioned into components attributable to different sources of variation. In its simplest form, ANOVA provides a statistical test of whether two or more population means are equal, and therefore generalizes the t-test beyond two means. In other words, the ANOVA is used to test the difference between two or more means.Analysis of variance (ANOVA) is an analysis tool used in statistics that splits an observed aggregate variability found inside a data set into two parts: systematic factors and random factors. The systematic factors have a statistical influence on the given data set, while the random factors do not. Analysts use the ANOVA test to determine the influence that independent variables have on the dependent variable in a regression study.
Sir Ronald Fisher pioneered the development of ANOVA for analyzing results of agricultural experiments.1 Today, ANOVA is included in almost every statistical package, which makes it accessible to investigators in all experimental sciences. It is easy to input a data set and run a simple ANOVA, but it is challenging to choose the appropriate ANOVA for different experimental designs, to examine whether data adhere to the modeling assumptions, and to interpret the results correctly. The purpose of this report, together with the next 2 articles in the Statistical Primer for Cardiovascular Research series, is to enhance understanding of ANVOA and to promote its successful use in experimental cardiovascular research. My colleagues and I attempt to accomplish those goals through examples and explanation, while keeping within reason the burden of notation, technical jargon, and mathematical equations.
Through this ppt you could learn what is Wilcoxon Signed Ranked Test. This will teach you the condition and criteria where it can be run and the way to use the test.
This is me Sonia Azam from university of the Punjab. this presentation is knowledgeable for all those students whose field related to Research, engineering or business. So enjoy my Presentation Dear fellows!
God Bless you All !!!!
Chapter 9 and 10 from book Understanding Research in SLL by James Dean Brown.
Chapter 9: Statistical Logic
Chapter 10: Correlation
Identifying a problem; Operationalizing Variables; Research Hypotheses
Choosing the correct statistic
Statistical hypotheses
Alpha decision level
Observed statistics
Assumptions:
1: Independence
2: Normal Distribution
3: Interval Scales
4: Linear Relationship
Degrees of Freedom
Critical Values
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Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
The Art Pastor's Guide to Sabbath | Steve ThomasonSteve Thomason
What is the purpose of the Sabbath Law in the Torah. It is interesting to compare how the context of the law shifts from Exodus to Deuteronomy. Who gets to rest, and why?
2. The Null and Experimental Hypothesis
• A Statistical hypothesis represents the mathematical
relationship presumed to exist between two or more
population parameters.
• A. Symbolic form (𝝁c ˃ 𝝁e)
• B. Written form
• (The average number of correctly recognized changes
for the control group (𝝁c) is anticipated to be higher
than changes noted by the experimental group (𝝁e) )
•
3. Directional and Nondirectional
Hypothesis
• Relationship existing between the population
parameters identified in any statistical
hypothesis can be one or two types.
• 1. Directional
• 2. Nondirectional
4. Directional hypothesis
• A directional hypothesis specifies the exact
nature or direction of the relationship
between population parameters.
• (𝝁c ˃ 𝝁e) or (𝝁c < 𝝁e)
• ≤ or ≥ less than or equal to or greater than or
equal to can also be used.
5. Nondirectional hypothesis
• A nondirectional hypothesis anticipates a
difference will exist between population
parameters but does not specify the nature,
or direction, of that difference.
• Symbolically (𝝁c ≠ 𝝁e)
• Note: the representative magnitudes of the
population means are not specified by this
relationship
6. Null hypothesis and Experimental
hypothesis
• Logicians, philosophers, scientists, and
statisticians all agree that:
• Empirical statements can never be proved to
be true, but they can be demonstrated to be
false.
• Take dogs barking as example. "They all bark"
7. Null hypothesis
• The null hypothesis traditionally indicates all
the population in an experiment, which are
represented by sample statistics, are equal.
The null hypothesis predicts that a given
independent variable or other intervention
will not cause in some dependent measure.
• It is symbolically represented by H0
• H0 : as 𝝁c = 𝝁e
•
8. Alternative hypothesis
• The alternative hypothesis or experimental
hypothesis specifies that a difference exists
between the population parameters identified
by the null hypothesis. The alternative
hypothesis predicts that a given independent
variable or other intervention will cause in
some change in some dependent measure.
• It is represented as H1
• H1 :𝝁c ˃ 𝝁e
9. Rejection and Acceptance
• Anticipated relationship is found.
• Statisticians say we reject the null hypothesis
as false and accept the alternative hypothesis
as an adequate explanation for the time
being.
• An hypothesized relation H1 is not found
• We can accept or retain the null hypothesis.
10. A statistical Conclusion
• Any statistical conclusion is based exclusively
on the null hypothesis, which is either
accepted or rejected.
11. SIGNIFICANT TESTING
• A significant difference between group means
or averages, for example, is unlikely to occur
when population means are actually equal to
one another. Any significant difference, then,
suggests that each sample mean represents a
distinct and different population.
• Inference statistics rely heavily on this form of
significance testing
12. Significance testing
• Significance testing entails using statistical
tests and probabilities to determine whether
sample data can be used to accept or reject a
null hypothesis involving population
parameters.
• Statistical test is not synonymous to scientific
importance or the strength or size of a result.
A significant result is one that is reliable and
detectable through statistical means.
13. Probability or P Values
• An alternative term that is frequently used to
denote p value or significance level is alpha
level.
• Symbolized by Greek letter α
14. P Value
• The term p value, significance level, level of
significance, and alpha α level are generally
interchangeable.
• They all refer to some predetermined
probability level (e.g., .05, .01, .001) used to
assess the null hypothesis.