The following table shows the number of players who passed and failed, based on the program they used.In other words, the odds that a player passes the test are actually lowered by 40.1 by using the new program.
![]() Master the basics of statistics in a single weekend using Statistics Made Easy. Again, lets just jump right in and learn the formula for the prediction interval. Note again that the t -multiplier has n -2 (not n -1) degrees of freedom, because the prediction interval uses the mean square error ( MSE ) whose denominator is n -2. The standard error of the prediction just has an extra MSE term added that the standard error of the fit does not. More on this a bit later.). ![]() Excel 95 Confidence Interval Skin Cancer MortalityLets look at the prediction interval for our example with skin cancer mortality as the response and latitude as the predictor ( Skin Cancer data ). We can be 95 confident that the skin cancer mortality rate at an individual location at 40 degrees north is between 111.235 and 188.933 deaths per 10 million people. Again, (xh) does not have to be one of the actual x values in the data set. Unlike the case for the formula for the confidence interval, the formula for the prediction interval depends strongly on the condition that the error terms are normally distributed. Because the formulas are so similar, it turns out that the factors affecting the width of the prediction interval are identical to the factors affecting the width of the confidence interval. Think about how we could predict a new response (ynew) at a particular (xh) if the mean of the responses (muY) at (xh) were known. That is, suppose it were known that the mean skin cancer mortality at (xh 40o) N is 150 deaths per million (with variance 400) What is the predicted skin cancer mortality in Columbus, Ohio. That is, it says that 95 of the measurements are in the interval sandwiched by. The problem is that our calculation used (muY) and (sigma), population values that we would typically not know. The logical thing to do is estimate it with the predicted response (haty). The cost of using (haty) to estimate (muY) is the variance of (haty). That is, different samples would yield different predictions (haty), and so we have to take into account this variance of (haty). Because the prediction interval has the extra MSE term, the prediction intervals standard error cannot get close to 0. Furthermore, both intervals are narrowest at the mean of the predictor values (about 39.5). Odit molestiae mollitia laudantium assumenda nam eaque, excepturi, soluta, perspiciatis cupiditate sapiente, adipisci quaerat odio voluptates consectetur nulla eveniet iure vitae quibusdam Excepturi aliquam in iure, repellat, fugiat illum voluptate repellendus blanditiis veritatis ducimus ad ipsa quisquam, commodi vel necessitatibus, harum quos a dignissimos.
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |