Excel 95 Confidence Interval Software Such As
Excel 95 Confidence Interval Skin Cancer Mortality

Data Analysis Add-in The data used are in carsdata.xls We then create a new variable in cells C2:C6, cubed household size.

Note, however, that the regressors need to be in contiguous columns.

If this is not the case in the original data, then columns need to be.

For example, to find 99 confidence intervals: in the Regression dialog.

Analysis Add-in), check the Confidence Level box and set the level to 99.

Excel 95 Confidence Interval Software Such As

More specialized software such as STATA, EVIEWS, SAS, LIMDEP, PC-TSP.

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.

Excel 95 Confidence Interval Skin Cancer Mortality

Lets 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.

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).

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Excel For Mac 95 Confidence Interval