A device used to transfer electric energy from one circuit to another, especially a pair of multiply wound, inductively coupled wire coils that effect such a transfer with a change in voltage, current, phase, or other electric characteristic.

In more complex definition you can say that

A transformer is a device that transfers electrical energy from one circuit to another through inductively coupled conductors—the transformer's coils. A varying current in the first or primary winding creates a varying magnetic flux in the transformer's core and thus a varying magnetic field through the secondary winding. This varying magnetic field induces a varying electromotive force (EMF) or "voltage" in the secondary winding. This effect is called mutual induction.

If a load is connected to the secondary, an electric current will flow in the secondary winding and electrical energy will be transferred from the primary circuit through the transformer to the load. In an ideal transformer, the induced voltage in the secondary winding (V_s) is in proportion to the primary voltage (V_p), and is given by the ratio of the number of turns in the secondary (N_s) to the number of turns in the primary (N_p) as follows:

By appropriate selection of the ratio of turns, a transformer thus allows an alternating current (AC) voltage to be "stepped up" by making N_s greater than N_p, or "stepped down" by making N_s less than N_p.

resonance..do u mean the coaching centre for JEE..well if yes & facing prob in understanding then leave it...its better that u dont waste time..! try Brilliant's Correspondence course..it works..hell yeah!

There are 3 types of tear gas

• CS (chlorobenzylidenemalononitrile)
• CN (chloroacetophenone) - often sold as Mace
• Pepper spray - made from chili peppers mixed with a vehicle like corn oil

CS is stronger than CN but wears off more quickly.

Maximum distance is infinity & minimum is 25 cm...its the ability of eye lens to focus on objects @ different distances

The Chandrasekhar limit is an upper bound on the mass of bodies made from electron-degenerate matter, a dense form of matter which consists of nuclei immersed in a gas of electrons. The limit is the maximum nonrotating mass which can be supported against gravitational collapse by electron degeneracy pressure. It is named after the Indian astrophysicist Subrahmanyan Chandrasekhar, and is commonly given as being about 1.4 solar masses. As white dwarfs are composed of electron-degenerate matter, no nonrotating white dwarf can be heavier than the Chandrasekhar limit. The Chandrasekhar limit is analogous to the Tolman–Oppenheimer–Volkoff limit for neutron stars.

Its a) 5 -> 2

in my opinion u shud go for NCERT....immensly important for concept building n for JEE prep also!

The two yr tym is quiet crucial..the person who takes this tym seriuosly cn band it off..!

listen up..if u wanna give JEE dis yr..forget ur past..IQ has nuthing to do wid JEE..i have seen a guy who failed in his 10th board exams esp in phy & math & den he wrked really hard for 2 yrs & crakced JEE wid a massive AIR of 164...

u cnt choose any particular topic..question cn cum frm any portion bt as tym is very less..pick up dose topic whic u think u need to focus more on...sum imp topic are mechanics..esp..projectile & prob related to inclined plane..thermodynamics, chemical kinetics..equilibrium..& calculus + conic & complex number..these are sum topics frm wch u gonna have a question..for sure..! dont worry..kick out d tension..! else u wont be able to focus..just wrk hard supremely..! GOD WILL  HELP U..IF U REALLY HAVE THE DESIRE TO GO IN IIT!

The answer is simple n short . the thing is that atomic nucleus or we can say that the atomic radius of the electron is bigger than the atomic radius of the nucleus itself. like a bigger thing can not fit into a smaller area like wise is the case with electrons .hence they rotate outside the nucleus. however there is an other answer according to Heisenberg uncertainty principle ....that @anwesha answered....

also whose gonna stop the repulsion between nucleus & electron..if they are pushed closer to each other!

@akhil Good joke eh!..ppl including u also na..i mean the 5th one.. :) plz pardon..jus kidding!

Motion of a body on a circular path with a fixed radius is called circular motion...one more thing important here is that the direction of velocity is always tangential to the direction of motion..thats imp one..!

cool one @nugorama

Liquids contained in a vessel exerts thrust at all points below their free surface, you have to use this property!

In simple word...mass is the measure ment of inertia & weight is the force that earth exerts on our body.

Hii kid...knowing is not important....whats ur depth in topic is main factor..if u are having prob in kinematics..start wid basics..like eqn of motion..cuz its an imp topic in phy!

Relative to the Earth, the Moon makes one rotation every 29.5 days. That happens to also be the time it takes for the Moon to complete one revolution around the Earth. This might seem like a coincidence, but it's not.

In the past, the Moon used to rotate much faster than it does now. But over millions of years, the effect of the Earth's gravity has slowed down the Moon's rotation until it became gravitationally locked to the Earth. This is why we always see the same side of the Moon.

Molar concentration, also called molarity, amount concentration or substance concentration, is a measure of the concentration of a solute in a solution, or of any chemical species in terms of amount of substance in a given volume. A commonly used unit for molar concentration used in chemistry is mol/L. A solution of concentration 1 mol/L is also denoted as 1 molar (1 M).

Molality (mol/kg, molal, or m) denotes the number of moles of solute per kilogram of solvent (not solution).

This can be calculated by the formula for range of a projectile which is u² Sin 2 0/ g ( 0 - theta), so 1000x1000 or 10⁶/9.8 or 10 for easy calculation = approximately 100 km...so none of the options are correct.

It was nice helping u!

In probability theory and applications, Bayes' theorem shows the relation between two conditional probabilities which are the reverse of each other. This theorem is named for Thomas Bayes and often called Bayes' law or Bayes' rule. Bayes' theorem expresses the conditional probability, or "posterior probability", of a hypothesis H (i.e. its probability after evidence E is observed) in terms of the "prior probability" of H, the prior probability of E, and the conditional probability of E given H. It implies that evidence has a stronger confirming effect if it was more unlikely before being observed. Bayes' theorem is valid in all common interpretations of probability, and it is commonly applied in science and engineering. However, there is disagreement among statisticians regarding its proper implementation.

The key idea is that the probability of an event A given an event B (e.g., the probability that one has breast cancer given that one has tested positive in a mammogram) depends not only on the relationship between events A and B (i.e., the accuracy of mammograms) but on the marginal probability (or "simple probability") of occurrence of each event. For instance, if mammograms are known to be 95% accurate, this could be due to 5.0% false positives, 5.0% false negatives (missed cases), or a mix of false positives and false negatives. Bayes' theorem allows one to calculate the conditional probability of having breast cancer, given a positive mammogram, for any of these three cases. The probability of a positive mammogram will be different for each of these cases. In the example at hand, there is a point of great practical importance that is worth noting: if the prevalence of mammograms resulting positive for cancer is, say, 5.0%, then the conditional probability that an individual with a positive result actually does have cancer is rather small, since the marginal probability of this type of cancer is closer to 1.0%. The probability of a positive result is therefore five times more likely than the probability of the cancer itself. This shows the value of correctly understanding and applying Bayes' mathematical theorem.

Thomas Bayes addressed both the case of discrete probability distributions of data and the more complicated case of continuous probability distributions. In the discrete case, Bayes' theorem relates the conditional and marginal probabilities of events A and B, provided that the probability of B does not equal zero:

In Bayes' theorem, each probability has a conventional name:

• P(A) is the prior probability (or "unconditional" or "marginal" probability) of A. It is "prior" in the sense that it does not take into account any information about B; however, the event B need not occur after event A. In the nineteenth century, the unconditional probability P(A) in Bayes's rule was called the "antecedent" probability; in deductive logic, the antecedent set of propositions and the inference rule imply consequences. The unconditional probability P(A) was called "a priori" by Ronald A. Fisher.
• P(A|B) is the conditional probability of A, given B. It is also called the posterior probability because it is derived from or depends upon the specified value of B.
• P(B|A) is the conditional probability of B given A. It is also called the likelihood.
• P(B) is the prior or marginal probability of B, and acts as a normalizing constant.

Bayes' theorem in this form gives a mathematical representation of how the conditional probability of event A given B is related to the converse conditional probability of B given A.

Bayes' theorem with continuous prior and posterior distributions

Suppose a continuous probability distribution with probability density function ƒ_Θ is assigned to an uncertain quantity Θ. (In the conventional language of mathematical probability theory Θ would be a "random variable.") The probability that the event B will be the outcome of an experiment depends on Θ; it is P(B | Θ). As a function of Θ this is the likelihood function:

Then the posterior probability distribution of Θ, i.e. the conditional probability distribution of Θ given the observed data B, has probability density function

where the "constant" is a normalizing constant so chosen as to make the integral of the function equal to 1, so that it is indeed a probability density function. This is the form of Bayes' theorem actually considered by Thomas Bayes.

In other words, Bayes' theorem says:

To get the posterior probability distribution, multiply the prior probability distribution by the likelihood function and then normalize.

More generally still, the new data B may be the value of an observed continuously distributed random variable X. The probability that it has any particular value is therefore 0. In such a case, the likelihood function is the value of a probability density function of X given Θ, rather than a probability of B given Θ:

A simple example of Bayes' theorem

Suppose there is a school with 60% boys and 40% girls as its students. The female students wear trousers or skirts in equal numbers; the boys all wear trousers. An observer sees a (random) student from a distance, and what the observer can see is that this student is wearing trousers. What is the probability this student is a girl? The correct answer can be computed using Bayes' theorem.

The event A is that the student observed is a girl, and the event B is that the student observed is wearing trousers. To compute P(A|B), we first need to know:

• P(B|A), or the probability of the student wearing trousers given that the student is a girl. Since girls are as likely to wear skirts as trousers, this is 0.5.
• P(A), or the probability that the student is a girl regardless of any other information. Since the observer sees a random student, meaning that all students have the same probability of being observed, and the fraction of girls among the students is 40%, this probability equals 0.4.
• P(B), or the probability of a (randomly selected) student wearing trousers regardless of any other information. Since half of the girls and all of the boys are wearing trousers, this is 0.5×0.4 + 1.0×0.6 = 0.8.

Given all this information, the probability of the observer having spotted a girl given that the observed student is wearing trousers can be computed by substituting these values in the formula:

Another, essentially equivalent way of obtaining the same result is as follows. Assume, for concreteness, that there are 100 students, 60 boys and 40 girls. Among these, 60 boys and 20 girls wear trousers. All together there are 80 trouser-wearers, of which 20 are girls. Therefore the chance that a random trouser-wearer is a girl equals 20/80 = 0.25. Put in terms of Bayes´ theorem, the probability of a student being a girl is 40/100, the probability that any given girl will wear trousers is 1/2. The product of these two is 20/100, but we know the student is wearing trousers, so one deducts the 20 students not wearing trousers, and then calculate a probability of (20/100)/(80/100), or 20/80.

It is often helpful when calculating conditional probabilities to create a simple table containing the number of occurrences of each outcome, or the relative frequencies of each outcome, for each of the independent variables. The table below illustrates the use of this method for the above girl-or-boy example