- published: 28 Jan 2013
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Causality (also referred to as 'causation', or 'cause and effect') is the agency or efficacy that connects one process (the cause) with another (the effect), where the first is understood to be partly responsible for the second. In general, a process has many causes, which are said to be causal factors for it, and all lie in its past. An effect can in turn be a cause of many other effects, which all lie in its future.
Causality is an abstraction that indicates how the world progresses, so basic a concept that it is more apt as an explanation of other concepts of progression than as something to be explained by others more basic. The concept is like those of agency and efficacy. For this reason, a leap of intuition may be needed to grasp it. Accordingly, causality is built into the conceptual structure of ordinary language.
In Aristotelian philosophy, the word 'cause' is also used to mean 'explanation' or 'answer to a why question', including Aristotle's material, formal, efficient, and final "causes"; then the "cause" is the explanans for the explanandum. In this case, failure to recognize that different kinds of "cause" are being considered can lead to futile debate. Of Aristotle's four explanatory modes, the one nearest to the concerns of the present article is the "efficient" one.
Khan Academy is a non-profit educational organization created in 2006 by educator Salman Khan with the aim of providing a free, world-class education for anyone, anywhere. The organization produces short lectures in the form of YouTube videos. In addition to micro lectures, the organization's website features practice exercises and tools for educators. All resources are available for free to anyone around the world. The main language of the website is English, but the content is also available in other languages.
The founder of the organization, Salman Khan, was born in New Orleans, Louisiana, United States to immigrant parents from Bangladesh and India. After earning three degrees from the Massachusetts Institute of Technology (a BS in mathematics, a BS in electrical engineering and computer science, and an MEng in electrical engineering and computer science), he pursued an MBA from Harvard Business School.
In late 2004, Khan began tutoring his cousin Nadia who needed help with math using Yahoo!'s Doodle notepad.When other relatives and friends sought similar help, he decided that it would be more practical to distribute the tutorials on YouTube. The videos' popularity and the testimonials of appreciative students prompted Khan to quit his job in finance as a hedge fund analyst at Connective Capital Management in 2009, and focus on the tutorials (then released under the moniker "Khan Academy") full-time.
Systems thinking is the process of understanding how those things which may be regarded as systems influence one another within a complete entity, or larger system. In nature, systems thinking examples include ecosystems in which various elements such as air, water, movement, plants, and animals work together to survive or perish. In organizations, systems consist of people, structures, and processes that work together to make an organization "healthy" or "unhealthy".
Systems thinking has roots in a diverse range of sources from Jan Smuts' Holism in the 1920s, to the General Systems Theory that was advanced by Ludwig von Bertalanffy in the 1940s and Cybernetics advanced by Ross Ashby in the 1950s. The field was further developed by Jay Forrester and members of the Society for Organizational Learning at MIT which culminated in the popular book The Fifth Discipline by Peter Senge which defined Systems thinking as the capstone for true organizational learning. Cornell University systems scientist, Derek Cabrera's book, "Systems Thinking Made Simple", explains that systems thinking itself is the emergent property of complex adaptive system (CAS) behavior that results from four simple rules of thought.
Introduction to causal relationship in scientific research. Covers concepts of cause, effect, if-then rule, scientific experiment, IV, DV.
Understanding why correlation does not imply causality (even though many in the press and some researchers often imply otherwise) Practice this lesson yourself on KhanAcademy.org right now: https://www.khanacademy.org/math/probability/statistical-studies/types-of-studies/e/types-of-statistical-studies?utm_source=YT&utm;_medium=Desc&utm;_campaign=ProbabilityandStatistics Watch the next lesson: https://www.khanacademy.org/math/probability/statistical-studies/types-of-studies/v/analyzing-statistical-study?utm_source=YT&utm;_medium=Desc&utm;_campaign=ProbabilityandStatistics Missed the previous lesson? https://www.khanacademy.org/math/probability/statistical-studies/types-of-studies/v/types-statistical-studies?utm_source=YT&utm;_medium=Desc&utm;_campaign=ProbabilityandStatistics Probability an...
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Levitt and Dubner explain the difference between correlation and causality, and the tricky ways we have to devise to reveal a true causality.
How to deal with jealousy in a casual relationship. These 10 Male Dating Personalities Lead To Heartbreak! http://bit.ly/MHYPersonalities A question I get asked a lot by my relationship coaching clients is "This guy I'm seeing wants to keep things casual - but now HE is acting jealous of other guys?? What is this?!?!? How do I deal with jealousy? Giving relationship advice to women on how to deal with jealousy in a casual relationship is one of the most enjoyable things I get to talk about as a dating and relationships coach! I find 'jealous relationships' to be a common dating complaint from women. Fortunately - it's an easy one for me to help them (and you) solve! In this video on how to deal with jealousy in a casual relationship, I (Mark Rosenfeld, dating and relationship coach fro...
To make better decisions and improve your problem solving skills it is important to understand the difference between correlation and causation.
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A causal claim is one that asserts there a relationship university of bristol, philosophy physics course between potential and actual (or generic individual) relations not so simple however. I n a recent paper in mind,2 professor arthur w. Wikipedia wiki causality url? Q webcache. Causal explanation in the social sciences university of michigan ideas about causation philosophy and psychology school relationship between cause effect 5 9 slideshare. Another common variety of inductive reasoning is concerned with establishing the presence causal relationships among eventsin philosophy, relationship between cause and effect. But cause and effect is also one of the philosophical relations, where relata have no connecting causation. Causation philosophy of science dictionary definition metaphysi...
In this video, you will learn what is meant by Causal relationship between two variables. You will also learn how to find out forecast using the regression line technique.
Causal research: The objective of causal research is to test hypotheses about cause-and-effect relationships. visit: www.b2bwhiteboard.com
Why Committed Relationships Are Better Than Causal Relationships And One Night Stands In this modern era, casual relationships, one night stands, blind dates and open relationships have become a trend. People get into them seeking thrill. But committed relationships will never be outdated as long as human race exists. Why? Well, human beings can't live on seeking thrill alone. There are so many other areas which need to be addressed in life. If you love dark chocolate, can you live all your life eating chocolate alone? No, it isn't possible and it isn't healthy. In the same way, you can't underestimate the power of a committed relationship. Reason 1 You will have someone to talk, discuss and share ideas. At the end of the day, making love isn't the only thing that gives joy in life. You ...
A element of Kumu Storytelling in Systems KeLE * https://kumu.io/-/4046#map-DCuHYYye Webinar Registration: http://www.systemswiki.org/
https://goo.gl/6U6t22 - Subscribe For more Videos ! For more Health Tips | Like | Comment | Share & Subscribe: Thank you for watching Our videos: ▷ CONNECT with us!! ► YOUTUBE - https://goo.gl/6U6t22 ► Facebook - https://goo.gl/uTP7zG ► Twitter - https://twitter.com/JuliyaLucy ► G+ Community - https://goo.gl/AfUDpR ► Google + - https://goo.gl/3rcniv ► Blogger - https://juliyalucy.blogspot.in/ Watch for more Health Videos: ► How To Avoid Unwanted Pregnancy Naturally: https://goo.gl/hRy93e ► Period Hacks || How To Stop Your Periods Early: https://goo.gl/dSmFgi ► Cold and Flu Home Remedies: https://goo.gl/biPp8b ► Homemade Facial Packs: https://goo.gl/NwV5zj ► How To Lose Belly Fat In 7 Days: https://goo.gl/EHN879 ► Powerfull Foods for Control #Diabetes: https://goo.gl/9SdaLY ► Natural H...
This lesson introduces linear regression. We start by explaining the relationship between the dependent and independent variables explored in casual models. Next we develop a one variable regression and then expand to multiple linear regression in Excel. We then examine regression model outputs covering: multiple R, R2, adjusted R2, F-test, coefficients, T-test, and confidence intervals. The lesson concludes by using a regression model to make forecasts. https://ericjjesse.wordpress.com/course-introduction/forecasting-and-regression/
Have fun improving your math & physics skills! Head to https://brilliant.org/minutephysics/ Footnote video: https://www.youtube.com/watch?v=iMbcMMe0D_Y This video is about how causal models (which use causal networks) allow us to infer causation from correlation, proving the common refrain not entirely accurate: statistics CAN be used to prove causality! Including: Reichenbach's principle, common causes, feedback, entanglement, EPR paradox, and so on. REFERENCES: Causal Discovery Algorithm in Quantum Mechanics Paper: https://arxiv.org/pdf/1208.4119.pdf Causal Models overview (Quantum and Classical): https://arxiv.org/pdf/1609.09487.pdf Support MinutePhysics on Patreon! http://www.patreon.com/minutephysics Link to Patreon Supporters: http://www.minutephysics.com/supporters/ MinutePhysi...
This brief video describes the logic of causal models, with a focus on the concepts of variables, statistical relationships (positive & negative), and units of analysis.
In this video, you will learn what is meant by Causal relationship between two variables. You will also learn how to find out forecast using the regression line technique along with correlation coefficient, coefficient of determination and standard error of the estimate.
Systems Thinking World Kumu e-Learning Environment Register at http://www.systemswiki.org/
In this Wireless Philosophy video, Paul Henne (Duke University) explains the difference between correlation and causation. Subscribe! http://bit.ly/1vz5fK9 More on Paul Henne: http://bit.ly/29alRyb ---- Wi-Phi @ YouTube: http://bit.ly/1PX0hLu Wi-Phi @ Khan Academy: http://bit.ly/1nQJcF7 Twitter: https://twitter.com/wirelessphi Instagram: @wiphiofficial Facebook: http://on.fb.me/1XC2tx3 ---- Help us caption & translate this video! http://amara.org/v/4tzX/
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Learn the difference between causation and association, and know why we use experiments Get access to practice questions, written summaries, and homework help on our website! http://wwww.simplelearningpro.com Follow us on Instagram http://www.instagram.com/simplelearningpro Like us on Facebook http://www.facebook.com/simplelearningpro Follow us on Twitter http://www.twitter.com/simplelearningp If you found this video helpful, please subscribe, share it with your friends and give this video a thumbs up! Get access to practice questions, written summaries, and homework help on our website! http://wwww.simplelearningpro.com Follow us on Instagram http://www.instagram.com/simplelearningpro Like us on Facebook http://www.facebook.com/simplelearningpro Follow us on Twitter http://www.twi...
1. An activity index identifies the activity that has a causal relationship with a particular cost. 2. A variable cost remains constant per unit at various levels of activity. 3. A fixed cost remains constant in total and on a per unit basis at various levels of activity. 4. If volume increases, all costs will increase. 5. If the activity index decreases, total variable costs will decrease proportionately. 6. Changes in the level of activity will cause unit variable and unit fixed costs to change in opposite directions. 7. For CVP analysis, both variable and fixed costs are assumed to have a linear relationship within the relevant range of activity. 8. The relevant range of activity is the activity level where the firm will earn income. 9. Costs will not change in total within the ...
Assignment 6 Correlation and Causation in the News Statistics are routinely used by journalists to explain and support claims. The news media often uncritically report on or even distort the findings of scientific studies. News articles often confound correlation and causation and report that correlations show causation when they actually do not. The difference between them is huge and confusing them can be costly in terms of money and time. As you know from your text, two variables may be correlated because one causes the other, a third variable causes both, or because of coincidence. Activity Resources Review: Bennett, J. O., Briggs, W. L., & Triola, M. F. (2014)., Chapter 8 McCoy, K. Park, A. (2011, March 24). Rochman, B. (2011, April 11). Main Task: Analyze Statistics in th...
What does the data say about poverty and crime? Is there a direct causal relationship? Is there a correlation? BGS IBMOR had some great theories on poverty and crime but, the data shows something slightly different BGS IBMOR Poverty and Crime: Response to Obsidian https://youtu.be/LLSv-H5F_0s Poverty is Down. So Why is Violent Crime up? https://www.thedailybeast.com/poverty-is-down-so-why-is-crime-up Ionica Smeets Ted Talks Correlation Vs Causation https://www.youtube.com/watch?v=8B271L3NtAw Moving Beyond Correlations in Assessing the Consequences of Poverty http://www.annualreviews.org/doi/abs/10.1146/annurev-psych-010416-044224 Poverty and Crime Patrick Sharkey http://www.oxfordhandbooks.com/view/10.1093/oxfordhb/9780199914050.001.0001/oxfordhb-9780199914050-e-28 Crime, Income Ine...
Causal relationships represented in causal models, and how they relate to association and causaltion.
Research Seminar by Kallapur, Sanjay on "Econometric Identification of Causal Effects: Graphical Causal Models in Practice". It is well known that causal inference relies on untestable a-priori causal assumptions. Identification refers to whether a causal relationship can be inferred from observed statistical associations; it requires an understanding of what statistical associations are induced by those causal assumptions. Since the assumptions are untestable, a transparent description of their statistical consequences helps the readers. However, the relation between causal assumptions and their induced statistical associations may not be obvious. Graphical Causal Models developed in the computer science literature in the 1980s (Pearl 2009) help trace these consequences and are therefore...
Faulty causality? What is an example of a faulty. 13 sep 2008 faulty reasoning false causation (cum hoc ergo propter hoc) certainly does raise the possibility that they have some causal relationship, 9 nov 2001 two things happening at the same time need not indicate a causal there is nothing in the line of reasoning that indicates lazy students should evaluate the quality of inductive, deductive, and causal reasoning. Faulty causality? What is an example of a faulty Faulty causal generalization ditext. The following two quotes are responses to questions posed by a bbc news article (the 13 mar 2015 logical fallacy faulty causality how causalities work translates from latin as 'post hoc, ergo propter hoc,' 'after this, therefore 14 jun 2017 anita's portfolio logo & business card front of i...
Don't use false cause an error in causal reasoning which a speaker mistakenly assumes good is the foundation to any speech, and this chapter, we will also one of foundations for many superstitions hold logic system rules making inferences; Reasoning process drawing conclusions involve existence, scope or causality; Questions about past present speeches gain passive agreement; Speeches immediate action because they are convinced by speaker's reasoningbecause their initial credibility audience's perception before speech beginspersuasive speakers often reasoningcausal ence people colorado, began her persuasive basic methods how them your 8, what kind used following statement? Over few a). When we have 8 oct 1998 speech communication. Html url? Q webcache. Another common variety of inductive r...
A causal claim is one that asserts there a relationship university of bristol, philosophy physics course between potential and actual (or generic individual) relations not so simple however. I n a recent paper in mind,2 professor arthur w. Wikipedia wiki causality url? Q webcache. Causal explanation in the social sciences university of michigan ideas about causation philosophy and psychology school relationship between cause effect 5 9 slideshare. Another common variety of inductive reasoning is concerned with establishing the presence causal relationships among eventsin philosophy, relationship between cause and effect. But cause and effect is also one of the philosophical relations, where relata have no connecting causation. Causation philosophy of science dictionary definition metaphysi...
Overview causal na ve theories of force and compression elapsed time the oxford handbook reasoning google books result. Croft (1991) is the main proponent of causal approach to event structure. Wordpress 2015 02 11 causal force url? Q webcache. Edu) and phillip wolff (pwolff@emory. 2016 jun;23(3) 789 96. Causal force deutsch bersetzung linguee wrterbuch. Causal agency and the perception of forcecausation (causality) definition personality psychology chapter 2 flashcards system as a causal force science direct. Department of psychology, emory university quantum mechanical foundations causalnorth carolina state university, usa snshah4@ncsu. 11 feb 2015 by definition, causal force is a persons determination to be a particular way because they think they are that way. Causal forces structuring...
The metaphysics of causation (stanford encyclopedia philosophy)internet philosophy. Googleusercontent searchcausality (also referred to as causation, or cause and effect) is the natural worldly agency efficacy that connects one process (the cause) with another state effect), where first partly responsible for second, second dependent on causality in physics. In practice, however, it remains difficult to clearly establish cause and effect, how do we a effect (causal) relationship? What criteria have meet? Generally, there are three that you must meet before one of the most deeply rooted concepts in science our everyday life is causality; The idea events present caused by past and, 2 feb 2003 options standard view causal relata they category event, their number two, roles instead taking noti...
報恩報怨討債還債皆是因果 - 鬼故黃乜都講(ep4)Causal relationship - Talking Together 仔打老豆是不是會遭雷打的呢?? 無論你信與不信,這也是一種業報,是子女的業報也同樣是父母的業報,經常也會聽到子女是來攞債的,究竟又是不是呢,今集同大家深入探討一下這個問題。 相信大家對於“投胎”的說法都不陌生,雖然不知道是真還是假,但是從古至今像這樣的傳說一直未曾終止過。對此,佛給我們找到了答案,子女投胎到你家並非偶然,之所別人能成為你的小孩認你做父母,這都是緣分在作怪,為什麼孩子會偏偏投胎到你家呢?為何不是投到別人家?如果你與子女沒有緣的話,就算是面對面也會不相識,比如孩子在小時候就會遭人拐賣,從此與你“分道揚鑣”再也無緣相見。生活中,這樣的例子更是數不勝數。 關於投胎的說法,佛告訴了我們四種緣,緣分不同,家長與孩子的相處情況也會有所不同。下面小算就與大家詳細的介紹一下這四種子女緣,已為人母或是人父的你可要看清楚了哦。孩子認你做父母到底是基於何種緣,是善還是孽緣? 第一種緣:報恩 在過去的人生中,你對孩子有恩並且你們非常有緣分。這輩子孩子投胎便是來報答你的對他的恩情。像這類孩子往往比較省心,聰明可愛,聽話懂事並且十分的有孝心。你的晚年生活,孩子也會照顧得有條不紊。這就是為什麼我們要提倡大家廣結善緣的根本原因。你施給別人的恩惠越多,將來得到的回報也就越多。 第二種緣:報怨 上輩子你與現在的孩子是冤家死對頭,老死不相往來的那種,孩子之所以會投胎認你做父母是因為他是來報怨的甚至是報仇的。你可能不相信,哪有孩子是來報仇的呢?如果你家孩子從小就不聽話,大一點有主見了就到處惹事生非,搞得你家不像家,因為他錢財耗盡。像這樣的孩子就是來報怨的。或許你會認為這都是沒有教育好的結果,現在不聽話的孩子多了去,難到都是來報仇不成?那為什麼會出現這種現象...
https://goo.gl/6U6t22 - Subscribe For more Videos ! For more Health Tips | Like | Comment | Share & Subscribe: Thank you for watching Our videos: ▷ CONNECT with us!! ► YOUTUBE - https://goo.gl/6U6t22 ► Facebook - https://goo.gl/uTP7zG ► Twitter - https://twitter.com/JuliyaLucy ► G+ Community - https://goo.gl/AfUDpR ► Google + - https://goo.gl/3rcniv ► Blogger - https://juliyalucy.blogspot.in/ Watch for more Health Videos: ► How To Avoid Unwanted Pregnancy Naturally: https://goo.gl/hRy93e ► Period Hacks || How To Stop Your Periods Early: https://goo.gl/dSmFgi ► Cold and Flu Home Remedies: https://goo.gl/biPp8b ► Homemade Facial Packs: https://goo.gl/NwV5zj ► How To Lose Belly Fat In 7 Days: https://goo.gl/EHN879 ► Powerfull Foods for Control #Diabetes: https://goo.gl/9SdaLY ► Natural H...
People often comment that they are overwhelmed by causal relationships and causal relationship diagrams. I expect that the two basic reasons for this are 1) the relationships were never well defined, and 2) relationship maps are typically presented in a manner that asks of people the equivalent of eating an elephant in a single bite. I'm not surprised by the typical result. * https://kumu.io/-/10173#map-ibv5TS6i/elem-OfYeHGNV?focus=1
In this video, you will learn what is meant by Causal relationship between two variables. You will also learn how to find out forecast using the regression line technique.
How to Get a Boyfriend - Turn a Casual Relationship into a Serious Relationship Want 1:1 Coaching? mindfulattraction.org/coach-in-a-pocket Support me on Patreon: Patreon.com/ma20 Sign up for the Friday Newsletter: mindfulattraction.org/newsletter How to turn friends with benefits into relationship Don’t use your body, use your vulnerability, his ego, his imagination, and pres election to get him to want you. Realize that some guys are the way they are - Some guys can't be changed. The only thing that can change guys are life experience and time. Pick the right guy - Don't pick the player or the one who struggles getting emotionally attached to a woman. Trouble. Introduce other guys - Introducing other guys will make him view you more attractive and will make him want to possess you ...
This lesson introduces linear regression. We start by explaining the relationship between the dependent and independent variables explored in casual models. Next we develop a one variable regression and then expand to multiple linear regression in Excel. We then examine regression model outputs covering: multiple R, R2, adjusted R2, F-test, coefficients, T-test, and confidence intervals. The lesson concludes by using a regression model to make forecasts. https://ericjjesse.wordpress.com/course-introduction/forecasting-and-regression/
In this video, you will learn what is meant by Causal relationship between two variables. You will also learn how to find out forecast using the regression line technique along with correlation coefficient, coefficient of determination and standard error of the estimate.
On the received view of causation, causal relations are a distinctive species of external relation. This paper explores the implications of adopting a conception of causation according to which causal relations are understood as manifestings of reciprocal powers. On such a conception, causation would most naturally be seen as a kind of internal relation, a relation founded on non-relational features of its relata. The consequences of such a view for familiar conceptions of natural necessity are assessed.
Paper: Stochastic Processes and Time Series Analysis Module :Causality Invertibility and the MA and AR processes Content Writer: Samopriya Basu/ Sugata Sen Roy
In statistics, a mediation model is one that seeks to identify and explicate the mechanism or process that underlies an observed relationship between an independent variable and a dependent variable via the inclusion of a third explanatory variable, known as a mediator variable. Rather than hypothesizing a direct causal relationship between the independent variable and the dependent variable, a mediational model hypothesizes that the independent variable influences the mediator variable, which in turn influences the dependent variable. Thus, the mediator variable serves to clarify the nature of the relationship between the independent and dependent variables. In other words, mediating relationships occur when a third variable plays an important role in governing the relationship between th...
On the received view of causation, causal relations are a distinctive species of external relation. In this talk, John Heil explores the implications of adopting a conception of causation according to which causal relations are understood as manifestings of reciprocal powers. On such a conception, causation would most naturally be seen as a kind of internal relation, a relation founded on non-relational features of its relata. The consequences of such a view for familiar conceptions of natural necessity are assessed.
Casual Relationships are a beautiful dynamic for getting to know each other. Today I go into how you can set up these types of relationships to mutually benefit both you and her. Music: New Chapter by Rakeem Miles (Ft. Mike G & YoAstrum) —————————————————— Get The Tool Box of Game Ebook Here http://www.bowldojo.com/products/the-tool-box-of-game 1 on 1 Skype Sessions Here: http://www.bowldojo.com/products/ Day Game Immersive Boot camps Here: http://www.bowldojo.com/bootcamp/ —————————————————— Subscribe here! https://www.youtube.com/channel/UCAPZwfAHn51sSZiFHorTxHA?sub_confirmation=1 If you are ready to step up to the next level and handle your dating life then check out when the next Boot Camp is- http://www.bowldojo.com/bootcamp/ Thanks for chilling guys, I hope you got some major ...
video presentation for HNS-2015 Reciprocal Causal Relationship between Laryngopharyngeal Reflux and Eustachian Tube Obstruction, by Hee-Young Kim, MD PhD
Carl Jung's "Synchronicity" Explained Synchronicity is a concept, first explained by psychoanalyst Carl Jung, which holds that events are "meaningful coincidences" if they occur with no causal relationship yet seem to be meaningfully related. (ie - something we do not understand is going on [behind the scenes]) *** Speaker is Stephan Hoeller ***
報恩報怨討債還債皆是因果 - 鬼故黃乜都講(ep4)Causal relationship - Talking Together 仔打老豆是不是會遭雷打的呢?? 無論你信與不信,這也是一種業報,是子女的業報也同樣是父母的業報,經常也會聽到子女是來攞債的,究竟又是不是呢,今集同大家深入探討一下這個問題。 相信大家對於“投胎”的說法都不陌生,雖然不知道是真還是假,但是從古至今像這樣的傳說一直未曾終止過。對此,佛給我們找到了答案,子女投胎到你家並非偶然,之所別人能成為你的小孩認你做父母,這都是緣分在作怪,為什麼孩子會偏偏投胎到你家呢?為何不是投到別人家?如果你與子女沒有緣的話,就算是面對面也會不相識,比如孩子在小時候就會遭人拐賣,從此與你“分道揚鑣”再也無緣相見。生活中,這樣的例子更是數不勝數。 關於投胎的說法,佛告訴了我們四種緣,緣分不同,家長與孩子的相處情況也會有所不同。下面小算就與大家詳細的介紹一下這四種子女緣,已為人母或是人父的你可要看清楚了哦。孩子認你做父母到底是基於何種緣,是善還是孽緣? 第一種緣:報恩 在過去的人生中,你對孩子有恩並且你們非常有緣分。這輩子孩子投胎便是來報答你的對他的恩情。像這類孩子往往比較省心,聰明可愛,聽話懂事並且十分的有孝心。你的晚年生活,孩子也會照顧得有條不紊。這就是為什麼我們要提倡大家廣結善緣的根本原因。你施給別人的恩惠越多,將來得到的回報也就越多。 第二種緣:報怨 上輩子你與現在的孩子是冤家死對頭,老死不相往來的那種,孩子之所以會投胎認你做父母是因為他是來報怨的甚至是報仇的。你可能不相信,哪有孩子是來報仇的呢?如果你家孩子從小就不聽話,大一點有主見了就到處惹事生非,搞得你家不像家,因為他錢財耗盡。像這樣的孩子就是來報怨的。或許你會認為這都是沒有教育好的結果,現在不聽話的孩子多了去,難到都是來報仇不成?那為什麼會出現這種現象...
Hee Young Kim MD, an ENT doctor from Seoul Korean does a presentation of "Eustachian Tube Obstruction and Laryngealpharyngeal Obstruction