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In statistics you have an experiment which will consist of one or more measurements. These measurements are converted to some statistic: it could be the sample mean, variance, maximum or something else. If you repeated the experiment, the value of this statistic would also change.If your hypothesis is true - whether in terms of the distribution or its parameters - and you repeated the experiment many times, you should expect the statistic to fall within the confidence interval (CI) in 95% of your trials. Even if the hypothesis is true, you should expect random variations to cause your statistic to lie outside the CI in 5% of cases.If you have a result that falls outside the 95% CI, it could be because you were unlucky and hit upon one of the 5% of rogue cases or that your hypothesis was incorrect. In this case you play the odds and conclude that your [null] hypothesis was incorrect.In statistics you have an experiment which will consist of one or more measurements. These measurements are converted to some statistic: it could be the sample mean, variance, maximum or something else. If you repeated the experiment, the value of this statistic would also change.If your hypothesis is true - whether in terms of the distribution or its parameters - and you repeated the experiment many times, you should expect the statistic to fall within the confidence interval (CI) in 95% of your trials. Even if the hypothesis is true, you should expect random variations to cause your statistic to lie outside the CI in 5% of cases.If you have a result that falls outside the 95% CI, it could be because you were unlucky and hit upon one of the 5% of rogue cases or that your hypothesis was incorrect. In this case you play the odds and conclude that your [null] hypothesis was incorrect.In statistics you have an experiment which will consist of one or more measurements. These measurements are converted to some statistic: it could be the sample mean, variance, maximum or something else. If you repeated the experiment, the value of this statistic would also change.If your hypothesis is true - whether in terms of the distribution or its parameters - and you repeated the experiment many times, you should expect the statistic to fall within the confidence interval (CI) in 95% of your trials. Even if the hypothesis is true, you should expect random variations to cause your statistic to lie outside the CI in 5% of cases.If you have a result that falls outside the 95% CI, it could be because you were unlucky and hit upon one of the 5% of rogue cases or that your hypothesis was incorrect. In this case you play the odds and conclude that your [null] hypothesis was incorrect.In statistics you have an experiment which will consist of one or more measurements. These measurements are converted to some statistic: it could be the sample mean, variance, maximum or something else. If you repeated the experiment, the value of this statistic would also change.If your hypothesis is true - whether in terms of the distribution or its parameters - and you repeated the experiment many times, you should expect the statistic to fall within the confidence interval (CI) in 95% of your trials. Even if the hypothesis is true, you should expect random variations to cause your statistic to lie outside the CI in 5% of cases.If you have a result that falls outside the 95% CI, it could be because you were unlucky and hit upon one of the 5% of rogue cases or that your hypothesis was incorrect. In this case you play the odds and conclude that your [null] hypothesis was incorrect.
I assume this is a trick question, and the answer is "everything". If you expect it, it is your expectation and if it is your expectation, you expect it.
When do you anticipate his arrival- When do you expect him-
You will expect to find that you do not have enough information.
A chi-square test is often used as a "goodness-of-fit" test. You have a null hypothesis under which you expect some results. You carry out observations and get a set of results. The expected and observed results are used to calculate the chi-square statistic. This statistic is used to test how well the observations match the values expected under the null hypothesis. In other words, how good the fit between observed and expected values is.
When you pass by value you essentially pass a temporary copy of the value. If the value's parameter is declared const, then copying the value could be costly, especially if the value is a large and complex structure or object. If the value's parameter is non-const, then it has to be assumed the function intends to alter the value in some way. If you pass by value, only the copy will be affected, not the original value. When a parameter is declared constant, passing by reference is generally the way to go. When it is non-const, pass by reference if you fully expect any changes to be reflected in the original value, otherwise pass by value.
Generally a contractor who builds a house for a customer can expect to receive 15% profit and 15% overhead. Generally that is on the high end though and can vary by region. Generally a contractor who builds a house for a customer can expect to receive 15% profit and 15% overhead. Generally that is on the high end though and can vary by region.
We can expect that prices are higher, output is less, and profits are high er.
True
b
Generally, a woman can expect a period by about 6 weeks after giving birth.
Generally you can expect to have better blood pressure, less risk for a variety of diesases or conditions and more stamina and energy.
It Depends on the guitar, however they are not generally much bigger than you would expect.
Generally you can expect 10 years, after that the repair bills outweigh the value of the vehicle.
One should expect a good amount of RAM, but a generally amount than one could expect from obtaining a desktop or laptop. It should have around 4 gigabytes of RAM.
Generally you should expect 10% non operator. If you plan on being owner/operator you can expect 10-20% or even higher based on your knowledge and dedication.
500 miles