This document introduces key concepts in probability including:
- Random events have uncertain outcomes but a regular distribution appears with large numbers of trials.
- Probability is the proportion of times an outcome would occur with many trials.
- Set theory concepts like unions, intersections, and complements are used to define sample spaces and calculate probabilities.
- The three basic probability rules are that probabilities lie between 0 and 1, the probabilities of all outcomes sum to 1, and the probability of an event's complement is 1 minus the probability of the event.
This document defines key probability terms and provides 6 general probability rules. The rules state that: 1) A probability is between 0 and 1, 2) The probabilities of all possible outcomes sum to 1, 3) The probability of events A or B is the sum of their individual probabilities minus their intersection, 4) The probability of the complement of an event is 1 minus the original probability, 5) The probability of independent events occurring together is the product of their individual probabilities, and 6) The probability of disjoint events is the sum of their individual probabilities.
1. Statistical efficiency compares two unbiased estimators by calculating their relative variance. The estimator with the lower variance is more efficient.
2. The Cramer-Rao inequality provides a lower bound (CRLB) for the variance of an unbiased estimator. It states that the variance of an estimator must be greater than or equal to the inverse of the expected information.
3. An estimator that achieves the Cramer-Rao lower bound is considered statistically efficient, as its variance reaches the theoretical minimum. In the example given, the maximum likelihood estimator of the mean of a normal distribution is shown to be efficient.
Hypothesis Testing techniques in social research.pptSolomonkiplimo
油
1) This document discusses hypothesis testing and comparing populations. It covers developing null and alternative hypotheses, types of errors, significance levels, and approaches using p-values and critical values.
2) Key steps in hypothesis testing include specifying the null and alternative hypotheses, choosing a significance level, calculating a test statistic, and determining whether to reject the null based on the p-value or critical value.
3) Comparing two populations involves testing whether their means are equal or different. The standard deviations play a role in determining if sample means are close enough to indicate the true population means are probably the same or different.
Hypothesis Test _One-sample t-test, Z-test, Proportion Z-testRavindra Nath Shukla
油
This document discusses hypothesis testing concepts including the null and alternative hypotheses, type I and II errors, and the hypothesis testing process. It provides examples of hypothesis testing for a mean where the population standard deviation is known (z-test) and unknown (t-test). The document outlines the 6 steps in hypothesis testing and provides examples using both the critical value approach and p-value approach. It discusses the relationship between hypothesis testing and confidence intervals.
The document discusses hypothesis testing and outlines the steps:
1) Define the population, hypotheses, significance level, and select a sample
2) State the null and alternative hypotheses
3) Calculate the test value and compare to the critical value to determine whether to reject the null hypothesis
Some key points include defining type I and type II errors, setting the significance level which determines the critical value, and identifying one-tailed or two-tailed tests. Examples demonstrate applying the steps to test claims about population means using z-tests with large sample sizes.
1. The document discusses the basic principles of hypothesis testing, including stating the null and alternative hypotheses, selecting a significance level, choosing a test statistic, determining critical values, and making a decision to reject or fail to reject the null hypothesis.
2. It outlines the five steps of hypothesis testing: state hypotheses, select significance level, select test statistic, determine critical value, and make a decision.
3. Key terms discussed include type I and type II errors, significance levels, critical values, test statistics such as z and t, and the decision to reject or fail to reject the null hypothesis.
1Copyright 息 2015 The McGraw-Hill Companies, Inc. Permission r.docxfelicidaddinwoodie
油
1
Copyright 息 2015 The McGraw-Hill Companies, Inc. Permission required for reproduction or display.
C H A P T E R E I G H T
Hypothesis Testing
1.1
Descriptive and Inferential Statistics
際際滷 2
Copyright 息 2012 The McGraw-Hill Companies, Inc. 8-1Steps in Hypothesis TestingTraditional Method8-2z Test for a Mean8-3t Test for a Mean8-4z Test for a Proportion8-52 Test for a Variance or Standard Deviation8-6Additional Topics Regarding Hypothesis
Testing
Hypothesis Testing
CHAPTER
8
Outline
Copyright 息 2015 The McGraw-Hill Companies, Inc. Permission required for reproduction or display.
1.1
Descriptive and inferential statistics
1Understand the definitions used in hypothesis
testing.2State the null and alternative hypotheses.3Find critical values for the z test.4State the five steps used in hypothesis testing.5Test means when is known, using the z test.6Test means when is unknown, using the t test.7Test proportions, using the z test.
Learning Objectives
Copyright 息 2015 The McGraw-Hill Companies, Inc. Permission required for reproduction or display.
1.1
Descriptive and inferential statistics
8Test variances or standard deviations, using the chi-square test.9Test hypotheses, using confidence intervals.10Explain the relationship between type I and type II errors and the power of a test.
Learning Objectives
Copyright 息 2015 The McGraw-Hill Companies, Inc. Permission required for reproduction or display.
Hypothesis Testing
Three methods used to test hypotheses:
1. The traditional method
2. The P-value method
3. The confidence interval method
5
Bluman Chapter 8
Copyright 息 2015 The McGraw-Hill Companies, Inc. Permission required for reproduction or display.
8.1 Steps in Hypothesis Testing-Traditional Method
A statistical hypothesis is a conjecture about a population parameter. This conjecture may or may not be true.
The null hypothesis, symbolized by H0, is a statistical hypothesis that states that there is no difference between a parameter and a specific value, or that there is no difference between two parameters.
6
Bluman Chapter 8
Copyright 息 2015 The McGraw-Hill Companies, Inc. Permission required for reproduction or display.
Steps in Hypothesis Testing-Traditional Method
The alternative hypothesis, symbolized by H1, is a statistical hypothesis that states the existence of a difference between a parameter and a specific value, or states that there is a difference between two parameters.
7
Bluman Chapter 8
Copyright 息 2015 The McGraw-Hill Companies, Inc. Permission required for reproduction or display.
Situation A
A medical researcher is interested in finding out whether a new medication will have any undesirable side effects. The researcher is particularly concerned with the pulse rate of the patients who take the medication. Will the pulse rate increase, decrease, or remain unchanged after a patient takes the medication? The researcher knows that the mean pulse rate for the population un ...
This document provides an introduction to hypothesis testing, including:
1. Defining hypotheses as claims about population parameters and the distinction between the null and alternative hypotheses.
2. Explaining the hypothesis testing process, including specifying the significance level, determining the rejection region, calculating test statistics, and making a decision.
3. Providing examples of one-sample z-tests and t-tests for the mean when the population standard deviation is known and unknown.
4. Discussing type I and type II errors and how significance levels influence the probability of each.
This document discusses hypothesis testing without statistics using a criminal trial as an example. It explains that in a trial, the jury must decide between a null hypothesis (H0) that the defendant is innocent, and an alternative hypothesis (H1) that the defendant is guilty based on the presented evidence. There are two possible errors - a Type I error of convicting an innocent person, and a Type II error of acquitting a guilty person. The probability of each error is inversely related to the sample size. The document provides examples to illustrate hypothesis testing concepts like rejection regions, test statistics, and interpreting p-values.
This document contains a presentation on hypothesis testing given by Dr. Mohammed Nasir Uddin. The presentation covers topics such as the definition of a hypothesis, null and alternative hypotheses, one-tailed and two-tailed tests, types of errors in hypothesis testing, p-values, and the steps to conduct hypothesis testing. Examples are provided to illustrate key concepts like computing rejection regions, concluding whether to reject or fail to reject the null hypothesis based on test statistics, and testing hypotheses about population means.
THIS POWERPOINT EXPLAINS ABOUT HYPOTHESIS AND ITS TYPES, ROLE OF HYPOTHESIS,TEST OF SIGNIFICANCE AND PROCEDURE FOR TESTING A HYPOTHESIS, TYPE I AND TYPE ii ERROR
This document provides an overview of hypothesis testing. It defines key terms like the null hypothesis, alternative hypothesis, and level of significance. It also distinguishes between parametric and non-parametric tests, giving examples of each. Finally, it walks through examples of conducting hypothesis tests, including calculating test statistics and determining whether to reject the null hypothesis. The overall purpose is to introduce students to different forms of hypothesis testing and how to apply appropriate statistical tests in quantitative research.
This document provides an overview of hypothesis testing, including:
- The two types of statistical hypotheses: the null hypothesis (H0) and alternative hypothesis (H1)
- How to define the hypotheses for different study designs, such as one-tailed, two-tailed, or directional alternatives
- How to determine critical values and critical regions based on the significance level (留)
- How to calculate a test statistic and make a decision to reject or not reject the null hypothesis
- Examples of hypothesis testing for different research scenarios involving means
Hypothesis testing involves 4 steps: 1) stating the null and alternative hypotheses, 2) setting the significance level criteria, 3) computing a test statistic to evaluate the hypotheses, and 4) making a decision to either reject or fail to reject the null hypothesis based on the significance level and test statistic. The goal is to correctly identify true null hypotheses while minimizing errors like falsely rejecting a true null hypothesis (Type I error) or retaining a false null hypothesis (Type II error).
The Rudimentary Tenets of Formulation and Testing HypothesisJJ.pptDrJosephJames
油
This document discusses key concepts in research methodology, including hypotheses, variables, and hypothesis testing. It defines hypotheses as tentative, testable statements made to help solve a research problem. Variables are factors that can be measured in a study, and can be categorical, ordinal, interval or ratio. Hypothesis testing involves stating a null hypothesis, setting a significance level, calculating a test statistic, and making a decision to reject or fail to reject the null hypothesis based on the probability of obtaining the sample results. Type I and Type II errors in hypothesis testing are also addressed.
This document discusses hypothesis testing. It defines hypothesis as an assumption or statement that may or may not be true. The key points covered include:
- Types of hypotheses include null and alternative hypotheses
- Errors in hypothesis testing include Type I errors of rejecting a true null hypothesis and Type II errors of failing to reject a false null hypothesis
- The hypothesis testing process involves setting hypotheses, significance level, determining test statistics, critical regions, computing test statistics, and making a decision to accept or reject the null hypothesis
This lecture discusses hypothesis testing. It begins by reviewing confidence intervals and introducing the concepts of the null hypothesis (H0) and alternative hypothesis (H1). Hypothesis testing involves collecting sample data and using it to decide whether to accept or reject the null hypothesis. Type I and type II errors are defined. Common steps in hypothesis testing are outlined, including specifying the significance level, determining the rejection region, calculating the test statistic, and making a decision. Examples demonstrate one-tailed and two-tailed hypothesis tests using z-tests and t-tests. P-values are also introduced as another method for drawing conclusions in hypothesis testing.
This document discusses hypothesis testing methodology and limitations. It provides a brief history of hypothesis testing, describing early examples and the development of modern approaches. The key approaches discussed are Fisher's significance tests using p-values and the Neyman-Pearson formulation, which specifies both the null and alternative hypotheses. The debate between these perspectives is outlined. While a hybrid approach combining elements of both is now common, the document notes this glosses over underlying philosophical differences in how hypothesis testing is conceptualized and limitations of the methodology.
Chapter8 Introduction to Estimation Hypothesis Testing.pdfmekkimekki5
油
1. AT&T argues its rates are similar to competitors, with a mean of $17.09. It sampled 100 customers and recalculated bills based on competitors' rates.
2. The null hypothesis is that the mean is equal to AT&T's $17.09. The alternative hypothesis is that the mean is not equal to $17.09.
3. Using a two-tailed test at a 5% significance level, if the calculated p-value is less than 0.05 we would reject the null hypothesis, concluding the mean is likely not equal to $17.09.
This document provides an overview of statistical inference and hypothesis testing. It discusses key concepts such as the null and alternative hypotheses, type I and type II errors, one-tailed and two-tailed tests, test statistics, p-values, confidence intervals, and parametric vs non-parametric tests. Specific statistical tests covered include the t-test, z-test, ANOVA, chi-square test, and correlation analyses. The document also addresses how sample size affects test power and significance.
This document provides an overview of hypothesis testing concepts. It defines key terms like population, sample, parameter, statistic, null hypothesis, alternative hypothesis, test statistic, critical region, type I and type II errors, level of significance, p-value, degrees of freedom, one-sided and two-sided tests, power of a test, and common test methods. It also provides examples of hypothesis tests for single means, paired means, and differences between means. The document is intended as lecture material to introduce students to the basic process and terminology of hypothesis testing.
Quantitative Methods in Business - Lecture (5)Mohamed Ramadan
油
- The document discusses hypothesis testing through a series of examples. It introduces key terminology like the null hypothesis (H0), alternative hypothesis (H1), significance level (留), calculated value, and tabulated value.
- An example tests whether the mean savings of VIP bank customers is greater than LE 5.6 million based on a sample. The calculated z-value is compared to the critical value to reject the null hypothesis.
- A second example tests whether the population mean is different than LE 6 million, calculating the z-value and comparing it to the critical value for a two-tailed test.
1Copyright 息 2015 The McGraw-Hill Companies, Inc. Permission r.docxfelicidaddinwoodie
油
1
Copyright 息 2015 The McGraw-Hill Companies, Inc. Permission required for reproduction or display.
C H A P T E R E I G H T
Hypothesis Testing
1.1
Descriptive and Inferential Statistics
際際滷 2
Copyright 息 2012 The McGraw-Hill Companies, Inc. 8-1Steps in Hypothesis TestingTraditional Method8-2z Test for a Mean8-3t Test for a Mean8-4z Test for a Proportion8-52 Test for a Variance or Standard Deviation8-6Additional Topics Regarding Hypothesis
Testing
Hypothesis Testing
CHAPTER
8
Outline
Copyright 息 2015 The McGraw-Hill Companies, Inc. Permission required for reproduction or display.
1.1
Descriptive and inferential statistics
1Understand the definitions used in hypothesis
testing.2State the null and alternative hypotheses.3Find critical values for the z test.4State the five steps used in hypothesis testing.5Test means when is known, using the z test.6Test means when is unknown, using the t test.7Test proportions, using the z test.
Learning Objectives
Copyright 息 2015 The McGraw-Hill Companies, Inc. Permission required for reproduction or display.
1.1
Descriptive and inferential statistics
8Test variances or standard deviations, using the chi-square test.9Test hypotheses, using confidence intervals.10Explain the relationship between type I and type II errors and the power of a test.
Learning Objectives
Copyright 息 2015 The McGraw-Hill Companies, Inc. Permission required for reproduction or display.
Hypothesis Testing
Three methods used to test hypotheses:
1. The traditional method
2. The P-value method
3. The confidence interval method
5
Bluman Chapter 8
Copyright 息 2015 The McGraw-Hill Companies, Inc. Permission required for reproduction or display.
8.1 Steps in Hypothesis Testing-Traditional Method
A statistical hypothesis is a conjecture about a population parameter. This conjecture may or may not be true.
The null hypothesis, symbolized by H0, is a statistical hypothesis that states that there is no difference between a parameter and a specific value, or that there is no difference between two parameters.
6
Bluman Chapter 8
Copyright 息 2015 The McGraw-Hill Companies, Inc. Permission required for reproduction or display.
Steps in Hypothesis Testing-Traditional Method
The alternative hypothesis, symbolized by H1, is a statistical hypothesis that states the existence of a difference between a parameter and a specific value, or states that there is a difference between two parameters.
7
Bluman Chapter 8
Copyright 息 2015 The McGraw-Hill Companies, Inc. Permission required for reproduction or display.
Situation A
A medical researcher is interested in finding out whether a new medication will have any undesirable side effects. The researcher is particularly concerned with the pulse rate of the patients who take the medication. Will the pulse rate increase, decrease, or remain unchanged after a patient takes the medication? The researcher knows that the mean pulse rate for the population un ...
This document provides an introduction to hypothesis testing, including:
1. Defining hypotheses as claims about population parameters and the distinction between the null and alternative hypotheses.
2. Explaining the hypothesis testing process, including specifying the significance level, determining the rejection region, calculating test statistics, and making a decision.
3. Providing examples of one-sample z-tests and t-tests for the mean when the population standard deviation is known and unknown.
4. Discussing type I and type II errors and how significance levels influence the probability of each.
This document discusses hypothesis testing without statistics using a criminal trial as an example. It explains that in a trial, the jury must decide between a null hypothesis (H0) that the defendant is innocent, and an alternative hypothesis (H1) that the defendant is guilty based on the presented evidence. There are two possible errors - a Type I error of convicting an innocent person, and a Type II error of acquitting a guilty person. The probability of each error is inversely related to the sample size. The document provides examples to illustrate hypothesis testing concepts like rejection regions, test statistics, and interpreting p-values.
This document contains a presentation on hypothesis testing given by Dr. Mohammed Nasir Uddin. The presentation covers topics such as the definition of a hypothesis, null and alternative hypotheses, one-tailed and two-tailed tests, types of errors in hypothesis testing, p-values, and the steps to conduct hypothesis testing. Examples are provided to illustrate key concepts like computing rejection regions, concluding whether to reject or fail to reject the null hypothesis based on test statistics, and testing hypotheses about population means.
THIS POWERPOINT EXPLAINS ABOUT HYPOTHESIS AND ITS TYPES, ROLE OF HYPOTHESIS,TEST OF SIGNIFICANCE AND PROCEDURE FOR TESTING A HYPOTHESIS, TYPE I AND TYPE ii ERROR
This document provides an overview of hypothesis testing. It defines key terms like the null hypothesis, alternative hypothesis, and level of significance. It also distinguishes between parametric and non-parametric tests, giving examples of each. Finally, it walks through examples of conducting hypothesis tests, including calculating test statistics and determining whether to reject the null hypothesis. The overall purpose is to introduce students to different forms of hypothesis testing and how to apply appropriate statistical tests in quantitative research.
This document provides an overview of hypothesis testing, including:
- The two types of statistical hypotheses: the null hypothesis (H0) and alternative hypothesis (H1)
- How to define the hypotheses for different study designs, such as one-tailed, two-tailed, or directional alternatives
- How to determine critical values and critical regions based on the significance level (留)
- How to calculate a test statistic and make a decision to reject or not reject the null hypothesis
- Examples of hypothesis testing for different research scenarios involving means
Hypothesis testing involves 4 steps: 1) stating the null and alternative hypotheses, 2) setting the significance level criteria, 3) computing a test statistic to evaluate the hypotheses, and 4) making a decision to either reject or fail to reject the null hypothesis based on the significance level and test statistic. The goal is to correctly identify true null hypotheses while minimizing errors like falsely rejecting a true null hypothesis (Type I error) or retaining a false null hypothesis (Type II error).
The Rudimentary Tenets of Formulation and Testing HypothesisJJ.pptDrJosephJames
油
This document discusses key concepts in research methodology, including hypotheses, variables, and hypothesis testing. It defines hypotheses as tentative, testable statements made to help solve a research problem. Variables are factors that can be measured in a study, and can be categorical, ordinal, interval or ratio. Hypothesis testing involves stating a null hypothesis, setting a significance level, calculating a test statistic, and making a decision to reject or fail to reject the null hypothesis based on the probability of obtaining the sample results. Type I and Type II errors in hypothesis testing are also addressed.
This document discusses hypothesis testing. It defines hypothesis as an assumption or statement that may or may not be true. The key points covered include:
- Types of hypotheses include null and alternative hypotheses
- Errors in hypothesis testing include Type I errors of rejecting a true null hypothesis and Type II errors of failing to reject a false null hypothesis
- The hypothesis testing process involves setting hypotheses, significance level, determining test statistics, critical regions, computing test statistics, and making a decision to accept or reject the null hypothesis
This lecture discusses hypothesis testing. It begins by reviewing confidence intervals and introducing the concepts of the null hypothesis (H0) and alternative hypothesis (H1). Hypothesis testing involves collecting sample data and using it to decide whether to accept or reject the null hypothesis. Type I and type II errors are defined. Common steps in hypothesis testing are outlined, including specifying the significance level, determining the rejection region, calculating the test statistic, and making a decision. Examples demonstrate one-tailed and two-tailed hypothesis tests using z-tests and t-tests. P-values are also introduced as another method for drawing conclusions in hypothesis testing.
This document discusses hypothesis testing methodology and limitations. It provides a brief history of hypothesis testing, describing early examples and the development of modern approaches. The key approaches discussed are Fisher's significance tests using p-values and the Neyman-Pearson formulation, which specifies both the null and alternative hypotheses. The debate between these perspectives is outlined. While a hybrid approach combining elements of both is now common, the document notes this glosses over underlying philosophical differences in how hypothesis testing is conceptualized and limitations of the methodology.
Chapter8 Introduction to Estimation Hypothesis Testing.pdfmekkimekki5
油
1. AT&T argues its rates are similar to competitors, with a mean of $17.09. It sampled 100 customers and recalculated bills based on competitors' rates.
2. The null hypothesis is that the mean is equal to AT&T's $17.09. The alternative hypothesis is that the mean is not equal to $17.09.
3. Using a two-tailed test at a 5% significance level, if the calculated p-value is less than 0.05 we would reject the null hypothesis, concluding the mean is likely not equal to $17.09.
This document provides an overview of statistical inference and hypothesis testing. It discusses key concepts such as the null and alternative hypotheses, type I and type II errors, one-tailed and two-tailed tests, test statistics, p-values, confidence intervals, and parametric vs non-parametric tests. Specific statistical tests covered include the t-test, z-test, ANOVA, chi-square test, and correlation analyses. The document also addresses how sample size affects test power and significance.
This document provides an overview of hypothesis testing concepts. It defines key terms like population, sample, parameter, statistic, null hypothesis, alternative hypothesis, test statistic, critical region, type I and type II errors, level of significance, p-value, degrees of freedom, one-sided and two-sided tests, power of a test, and common test methods. It also provides examples of hypothesis tests for single means, paired means, and differences between means. The document is intended as lecture material to introduce students to the basic process and terminology of hypothesis testing.
Quantitative Methods in Business - Lecture (5)Mohamed Ramadan
油
- The document discusses hypothesis testing through a series of examples. It introduces key terminology like the null hypothesis (H0), alternative hypothesis (H1), significance level (留), calculated value, and tabulated value.
- An example tests whether the mean savings of VIP bank customers is greater than LE 5.6 million based on a sample. The calculated z-value is compared to the critical value to reject the null hypothesis.
- A second example tests whether the population mean is different than LE 6 million, calculating the z-value and comparing it to the critical value for a two-tailed test.
Analytical Framework of Egyptian Labour Market Information LessonsMohamed Ramadan
油
This presentation introduce the analytical framework of the Egyptian LMIS in Arabic language. The presentation was executed in a series of capacity development workshops, which carried-out by TEVT-2 project. This material introduces the main key information items that should be included in ELMIS to answer different stakeholders needs at the national and local levels.
Egypt on the road to achieve SDG-2 "Zero Hunger"Mohamed Ramadan
油
This is an Arabic version from the presentation that anatomize the challenges facing Egypt to achieve SDG 2. The presentation was the main topic of the carried activities by WFP country office in the planning process to the Country Strategy Program for 2018-2022.
How Gender Biased are Female-Headed-Households Transfers in Egypt?Mohamed Ramadan
油
In this paper, we claim that the policy of targeting female-headed households (FHHs) may generate bias against women in male-headed households (MHHs) who may be more poverty-constrained. Targeting FHHs may have the merit of clear targeting, however, it doesnt address the feminization phenomenon of poverty; instead, it presents unequal opportunities for women in other families by less favoring them. We argue that proper targeting could be derived based on the number of women in families. The study applied a Gender-Based Poverty Detection Model to provide a good detection of household poverty and show that the vulnerable characteristics of females could be more influenced by the general households poverty than females headed households. Model results showed that not all FHHs are poor, and that some de jure MHHs include a large number of poor females. This means that targeting only de jure FHHs might result in resource leakage to the non-poor and under-coverage of poor de facto FHHs and poor females in MHHs. The analysis asserts that female headship is not always a correlate of poverty in Egypt. An important correlate, however, is the share of female members in the household. This raises questions about the effectiveness of social assistance and poverty alleviation programs in Egypt in targeting female poverty.
This document discusses the development of a labor market information system (LMIS) in Egypt. It defines what labor market information (LMI) and LMIS are. It also explores what was learned from international experiences with LMIS and how LMI can help address policy questions. Finally, it outlines the practical approach and data sources that will be used to build the LMIS.
Egy-GeoInfo, 1st Egyptian Geospatial Information PortalMohamed Ramadan
油
This presentation was executed as a keynote-speaker in 2017 conference of Africa GIS. The presentation introduced the conceptual framework of the first Egyptian Geospatial Information Portal "Egy-GeoInfo", which launched for the first time in November 2016. Moreover, the presentation give a brief overview regarding the second generation of the portal, which will present the full resulted statistics by the first Egyptian e-census 2017, as well as the new geo-analytics that will exclusively be introduced in this version.
This presentation is the Arabic version from the results of the first e-census in Egypt, which published in September 2017, with a notable attendance by H.E. Abdel Fattah el-Sisi, the President of Egypt, as well as the Egyptian Cabinet, and key society elites and distinguishable international representatives.
Patrick Dwyer resides in Miami, Florida where he proudly founded the Dwyer Family Foundation. Formerly with Merrill Lynch, Patrick has a long, successful career as a wealth advisor for high-net-worth clients.
COPY & PASTE LINK https://pcsoftsfull.org/dl
Letasoft Sound Booster Crack Free Download is an impressive application that will amplify the volume of the entire operating system. It was developed to ensure you get the most out of your PCs features by increasing your audio volume to a maximum of 500% and making smaller speakers sound louder.
The Glass Communities SSE Addendum document discusses the strategic developments and impacts of glass communities, focusing on new portals, licensing, and economic implications.
Introduction
The document builds on previous work regarding "Glass Communities SSE" and introduces the "Glass World Report." It highlights the need for new governance structures and the importance of avoiding conflicts of interest.
V1 Pre-deployment Strategy
Discusses a covenant-based approach for proprietary access and evaluates the Pittsburgh Portal. Monte Carlo evaluations indicate a 90% risk-free investment potential for SSE glass projects until 2038-2042.
Key strategies include multi-agency building, supply chain portals, and early participation benefits.
V2 Glass World Reports
Highlights significant changes in the glass industry, including the establishment of a new portal in Spain and BAKO's licensing. Engages audience questions regarding the impact of new portals and the certification of home build divisions.
Discusses the potential economic impact of the new portal on European economies, estimating a GDP increase.
V3 Impact of SSE Communities
Explores the long-term vision for glass communities and their role in the global economy.
/slideshow/comments-on-glass-communities-sse-addendum-pdf/279523383
OwnAir - Your Cinema Everywhere | Business PlanAlessandro Masi
油
Own Air is a film distributor specializing in tailored digital and day-and-date releases for quality independent and festival-driven content. This is a strategic deck for potential partnerships. This is a business plan for potential investors primarily. Copyright 2012. All rights reserved.
In simple terms, a business is an organization or activity that aims to make money by producing or selling goods or services. It can be a commercial venture, industrial enterprise, or professional practice. Essentially, businesses provide a means to create economic value
The Mo You Know Heads to LA for the 2025 BET ExperiencePRnews2
油
Fayetteville, NC ShaDonna Mo McPhaul, the powerhouse behind The Mo You Know brand, is heading back to Los Angeles June 59 to soak up the energy, connections, and culture of the 2025 BET Experience and BET Awards Weekend!
A combat veteran turned public relations strategist, media relations maven, and micro-influencer, Mo is no stranger to high-profile moments and celebrity-driven platforms. Her journey into the entertainment industry has been years in the makingand this trip could be the breakthrough moment shes been building toward.
Mos track record speaks for itself:
In May 2019, she was empowered by the Steve Harvey Vault Conference in LA, where she made the decision to JUMP into entrepreneurship.
In February 2024, she hit the red carpet as credentialed press at the 25th Super Bowl Soulful Celebration, televised on CBS.
In November 2024, she covered the Blue HBCU Honors taped at Howard University and aired on BET.
Since retiring from the U.S. Air Force in 2016, Mo has turned her passion into purposeamplifying stories, creating connections, and serving as a dynamic bridge between brands and audiences. Her dream has always been to work in the sports and entertainment industry, and with this next trip to LA, shes ready to bet on herself once again.
This year, Mo will be attending:
BETX Casting Call
BETX Fan Fest (Saturday + Sunday)
Networking events and influencer
BET Awards
Im not just showing upIm showing out. This is what Ive trained for20 years in the military taught me discipline, but purpose gave me vision, says McPhaul.
To collaborate with Mo during BET Weekend or to schedule interviews, influencer coverage, or partnership opportunities, please contact her directly at shadonnamack@me.com or visit https://calendly.com/themoyouknow/discovery-call
_________
Follow the Journey:
Instagram/Facebook: @TheMoYouKnow214
Business Plan Review Presentation v1712 aniamation.pptxNguyenThanhKiet4
油
This file describes the steps to create a business plan, and is also a sample business report, which you can customize to create similar business reports for yourself #businessplan #businesspresentation #pptsample
Why adaptiveness is difficult for (select from <<agile, product management, DevOps, DevSecOps, lean/agile, lean>> without something like Beyond Budgeting. Start with the Viable Map.
Cynefin presents the case for adaptiveness. John shows how Plan Do Study Act (PDSA) can be used at all levels.
John Coleman recounted my journey from Scrum through people & change, Kanban, and product management to adaptiveness and ambidexterity. He showed how Roger L. Martin style strategy and strategy deployment might work. But without balanced flow, it's all for nothing.
Enter Beyond Budgeting and the Viable Map.
Learn more at https://evolved.institute.
Prakash Hinduja, a prominent figure in the esteemed Hinduja family, serves as the Chairman and Managing Director of Jaihind Projects Limitedone of Indias foremost companies in oil and gas infrastructure. Since joining the family enterprise in June 1980, Prakash Hinduja has guided Jaihind Projects with a steadfast vision, transforming it into a national leader in pipeline construction, turnkey solutions, and integrated infrastructure services.
Hailing from the respected Hinduja family business lineage, Prakash Hinduja brought with him a deep-rooted ethos of innovation, accountability, and excellence. His leadership reflects the legacy of the Prakash Hinduja familyblending tradition with progressive strategies that have redefined industry benchmarks. Based in Ahmedabad, he took on the challenge of expanding Jaihind Projects from a regional operation into a national infrastructure giant, now employing over 5,000 skilled professionals and executing critical projects across India.
Over the decades, Prakash Hinduja has exemplified strategic leadership, combining deep industry knowledge with a strong focus on execution and partnerships. His emphasis on adopting advanced technologies, maintaining stringent safety protocols, and ensuring environmental sustainability has earned Jaihind Projects consistent recognition for quality and reliability.
Beyond business, Prakash Hinduja is driven by a vision to create lasting impact. Inspired by the values of the Hinduja family business, he champions initiatives that contribute to national development, empower local communities, and foster innovation within the organization. His inclusive leadership style nurtures talent, encourages growth, and reinforces the companys reputation as a trusted infrastructure partner.
Prakash Hinduja continues to uphold the rich legacy of the Hinduja family while leading Jaihind Projects into the future with resilience, ambition, and a commitment to excellence. His journey is a testament to visionary leadership grounded in values and fueled by purpose.
ICV Assessments is a trusted provider of ISO certification and compliance solutions for businesses across diverse industries.
Our core services include ISO certification, GMP, HACCP, and other international standards to ensure quality, safety, and operational excellence.
We specialize in guiding organizations through audits, documentation, and implementation processes for ISO 9001, ISO 14001, ISO 45001, ISO 27001, ISO 22000,
and more. With a client-centric approach, we help businesses improve efficiency, meet regulatory requirements, and gain competitive advantage.
Our experienced team offers end-to-end support, from initial consultation to successful certification.
Partner with ICV Assessments for credible, timely, and globally recognized certification services.
Overview: The document discusses advancements in car and home integration, focusing on glass technology, internships, and media hosting.
Part I Industry Focus
Future designs emphasize the integration of glass technology in car and home development.
The Model O stabilizes vehicle functions and enhances road handling through innovative systems refined by various renditions of model compositions
Pull systems leverage renewable energy, contrasting with traditional push systems that rely on physical labor and fuel injection.
Rotational internships train participants in portal projects, with 14,322 participants receiving certification for development of city portals.
Priming Tables
Intern rotations involve a structured process of testing, reviewing, and redesigning models over 24 months.
The table outlines the progression from beta models to final production books for both cars and homes.
Media Hosting
Media hosting addresses simulation problems and enhances task delivery for advancing models.
Foiling is necessary for controlling vehicle dynamics and ensuring a healthy driving environment.
Industrial Redevelopment
Industrial redevelopment is crucial for media streaming and involves a significant number of participants in the internship program.
The document highlights the importance of collaboration and training in the glass community for future developments.
/slideshow/summary-of-comments-on-conference-2-notes-for-car-and-home-show-pdf/279864505
4. Null
Hypothesis
Alternative
Hypothesis
Lecture (4) Introduction to Hypothesis Testing
The Idea ... Concept and Terminologies
= 基 咋
!
"
#
$
The Decision Maker (Prime-
Minister) introduced the following
two hypotheses to be tested:
H0: The Population Mean of
Monthly Salary = 23,100
H1: The Population Mean of
Monthly Salary < 23,100
袖
5. Lecture (4) Introduction to Hypothesis Testing
The Idea ... Concept and Terminologies
= 基 咋
!
"
#
$
The Decision Maker (Prime-
Minister) introduced the following
two hypotheses to be tested:
H0: The Population Mean of
Monthly Salary = 23,100
H1: The Population Mean of
Monthly Salary < 23,100
袖
Decision H0 is True H0 is False
! Reject H0
# Reject H0
$ Reject H0
" Accept H0
6. 留
= ($ )
= ($ 腫 )
Lecture (4) Introduction to Hypothesis Testing
Decision H0 is True H0 is False
Reject H0
Dont Reject H0
Types of Errors:
Type I error
Type II error
Decision H0 is True
Innocent
H0 is False
Guilty
Reject H0
Dont Reject H0
If you are a Judge:
H0: The defendant is innocent
H1: The defendant is guilty
Type II error
Occurs when a null hypothesis
Type I error
Occurs when we a null hypothesis
Types of Errors
7. Lecture (4) Introduction to Hypothesis Testing
The Concept of Testing of Hypotheses
1. There are two hypotheses, the null and the alternative
hypotheses.
2. The procedure begins with the assumption that the null
hypothesis is true.
3. The goal is to determine whether there is enough evidence to
infer that the alternative hypothesis is true.
4. There are two possible decisions:
皃 Conclude that there is enough evidence to support the
alternative hypothesis. [We reject the null hypothesis]
皃 Conclude that there is not enough evidence to support the
alternative hypothesis. [We couldnt reject the null hypothesis]
Hypotheses Testing