Rien - Idiomatic French Expressions

Rien - Idiomatic French Expressions The French pronoun rien usually means nothing, and is also used in many expressions. Learn how to say for no reason, not a chance, worthless, and more with this list of expressions with rien. Possible Meanings of Rien nothinganythinglove (tennis)nil, zero (sports) le rien - nothingnessun rien - a mere nothingdes riens - trivia Expressions with Rien (faire qqchose) comme un rien(to do something) with no trouble, like nothing at allun coup pour riena free gode rienyoure welcomedeux fois riennext to nothingjamais rien / As-tu jamais rien vu de plus bizarre  ?anything / Have you ever seen anything stranger?ne ___ en rien / Il ne ressemble en rien son pà ¨re.not at all, nothing like / He looks nothing like his father.ne... riennothing___ ne risque rien___ will be okay, nothing can happen to ___pour rienfor nothing, for a songpour un rienfor no reason, at the drop of a hatrien dà ©clarer ( la douane)nothing to declare (at customs)rien signalernothing to reportrien voirnothing to do withrien au mondenothing in the worldrien dautrenothing elseun rien dea splash, touch, hint of somethingrien de gravenothing seriousrien de moinsnothing lessrien de neufnothing newrien de plusnothing else, nothing morerien de plus facile(theres) nothing easier, nothing could be simplerrien de plus, rien de moinsnothing more or lessrien de rien (inf ormal)absolutely nothingrien de tel quenothing likerien du toutnothing at allrien partout (sports)nil all, love allrien queonlyrien que à §a (ironic)thats all, no lessrien qui vaillenothing useful, nothing worthwhile___ sinon rien___ or nothingtrois fois riennext to nothingcomprendre rien riento not have a cluenavoir rien voir avec/dansto have nothing to do withnavoir rien contre (quelquun)to have nothing against (someone)navoir rien de (quelquun)to having nothing in common with (someone)nà ªtre riento be a nobody/nothing, to be worthlessÇa ne compte pour rien dansThat has nothing to do withÇa ne fait rien.It doesnt matter, Never mind. Ça ne me dit rienI dont feel like itÇa ne risque pas !Not a chance!Ça ne vaut rienIts worthless, its no goodÇa ne veut dire rienThat doesnt mean a thingCela na rien voir avec...That has nothing to do with...Cela na rien dimpossible.That is perfectly possible.Cela ne rime rienThat makes no senseCe que tu fais ou rien !Dont bother!Cest à §a ou rienTake it or leave itCest mieux que rienIts better than nothingCest rien de le dire. (informal)Thats an understatement.Cest tout ou rien.Its all or nothing.Cest un(e) rien du toutHe (She) is a nobody, no goodCe nest pas rien.Its not nothing, Its no picnic.Ce nest rien.Its nothing, Never mind.Cà ©tait un coup pour rien.It was all for nothing.Il nen est rien.Its nothing like that, Thats not it at all.Il ny a rien faireTheres nothing we can do, Its hopelessJe nai rien dire surI have nothing to say about, I cant complain aboutJe ny peux rienTheres nothing I can do about it.Je ny suis pour rienIve got nothing to do with it. On na rien pour rienEverything has a price.Qui ne risque rien na rien (proverb)Nothing ventured, nothing gainedRien faire !Its no good!Rien ne dit que ...Theres nothing to say that ...Rien ne va plusNo more betsRien ny faitNothing is any goodTu nas rien dire !Youre in no position to comment! You cant complain!La và ©rità ©, rien que la và ©rità ©.The truth and nothing but the truth.Y a-t-il rien de plus ___ ?Is there anything more ____?

Examples of Confidence intervals for means

Examples of Confidence intervals for means One of the major parts of inferential statistics is the development of ways to calculate confidence intervals. Confidence intervals provide us with a way to estimate a population parameter. Rather than say that the parameter is equal to an exact value, we say that the parameter falls within a range of values.   This range of values is typically an estimate, along with a margin of error that we add and subtract from the estimate. Attached to every interval is a level of confidence. The level of confidence gives a measurement of how often, in the long run, the method used to obtain our confidence interval captures the true population parameter. It is helpful when learning about statistics to see some examples worked out. Below we will look at several examples of confidence intervals about a population mean. We will see that the method we use to construct a confidence interval about a mean depends on further information about our population. Specifically, the approach that we take depends on whether or not we know the population standard deviation or not. Statement of Problems We start with a simple random sample of 25 a particular species of newts and measure their tails. The mean tail length of our sample is 5 cm. If we know that 0.2 cm is the standard deviation of the tail lengths of all newts in the population, then what is a 90% confidence interval for the mean tail length of all newts in the population?If we know that 0.2 cm is the standard deviation of the tail lengths of all newts in the population, then what is a 95% confidence interval for the mean tail length of all newts in the population?If we find that that 0.2 cm is the standard deviation of the tail lengths of the newts in our sample the population, then what is a 90% confidence interval for the mean tail length of all newts in the population?If we find that that 0.2 cm is the standard deviation of the tail lengths of the newts in our sample the population, then what is a 95% confidence interval for the mean tail length of all newts in the population? Discussion of the Problems We begin by analyzing each of these problems. In the first two problems we know the value of the population standard deviation. The difference between these two problems is that the level of confidence is greater in #2 than what it is for #1. In the second two problems the population standard deviation is unknown. For these two problems we will estimate this parameter with the sample standard deviation. As we saw in the first two problems, here we also have different levels of confidence. Solutions We will calculate solutions for each of the above problems. Since we know the population standard deviation, we will use a table of z-scores. The value of z that corresponds to a 90% confidence interval is 1.645. By using the formula for the margin of error we have a confidence interval of 5 – 1.645(0.2/5) to 5 1.645(0.2/5). (The 5 in the denominator here is because we have taken the square root of 25). After carrying out the arithmetic we have 4.934 cm to 5.066 cm as a confidence interval for the population mean.Since we know the population standard deviation, we will use a table of z-scores. The value of z that corresponds to a 95% confidence interval is 1.96. By using the formula for the margin of error we have a confidence interval of 5 – 1.96(0.2/5) to 5 1.96(0.2/5). After carrying out the arithmetic we have 4.922 cm to 5.078 cm as a confidence interval for the population mean.Here we do not know the population standard deviation, only the sample standard deviation. Thus we will use a table of t-scores. When we use a tabl e of t scores we need to know how many degrees of freedom we have. In this case there are 24 degrees of freedom, which is one less than sample size of 25. The value of t that corresponds to a 90% confidence interval is 1.71. By using the formula for the margin of error we have a confidence interval of 5 – 1.71(0.2/5) to 5 1.71(0.2/5). After carrying out the arithmetic we have 4.932 cm to 5.068 cm as a confidence interval for the population mean. Here we do not know the population standard deviation, only the sample standard deviation. Thus we will again use a table of t-scores. There are 24 degrees of freedom, which is one less than sample size of 25. The value of t that corresponds to a 95% confidence interval is 2.06. By using the formula for the margin of error we have a confidence interval of 5 – 2.06(0.2/5) to 5 2.06(0.2/5). After carrying out the arithmetic we have 4.912 cm to 5.082 cm as a confidence interval for the population mean. Discussion of the Solutions There are a few things to note in comparing these solutions. The first is that in each case as our level of confidence increased, the greater the value of z or t that we ended up with. The reason for this is that in order to be more confident that we did indeed capture the population mean in our confidence interval, we need a wider interval. The other feature to note is that for a particular confidence interval, those that use t are wider than those with z. The reason for this is that a t distribution has greater variability in its tails than a standard normal distribution. The key to correct solutions of these types of problems is that if we know the population standard deviation we use a table of z-scores. If we do not know the population standard deviation then we use a table of t scores.