Đang chuẩn bị liên kết để tải về tài liệu:
INTRODUCTION TO STATISTICS THROUGH RESAMPLING METHODS AND MICROSOFT OFFICE EXCEL phần 10

và một hay nhiều biến khác, thường được gọi là yếu tố dự đoán. Chúng tôi bắt đầu văn bản của chúng tôi với một tham chiếu đến pháp luật Boyle cho hành vi của các chất khí hoàn hảo, V = KT / P. Trong phiên bản này của pháp luật Boyle, V (khối lượng của khí) là biến phụ thuộc; | CHAPTER 8 REPORTING YOUR FINDINGS 203 The purpose of your inquiry must be kept in mind. Orders in from a machinery plant ranked by size may be quite skewed with a few large orders. The median order size might be of interest in describing sales the mean order size would be of interest in estimating revenues and profits. Are the results expressed in appropriate units For example are parts per thousand more natural in a specific case than percentages Have we rounded off to the correct degree of precision taking account of what we know about the variability of the results and considering whether the reader will use them perhaps by multiplying by a constant factor or another variable Whether you report a mean or a median be sure to report only a sensible number of decimal places. Most statistical packages including R can give you nine or 10. Don t use them. If your observations were to the nearest integer your report on the mean should include only a single decimal place. Limit tabulated values to no more than two effective changing digits. Readers can distinguish 354691 and 354634 at a glance but will be confused by 354691 and 357634. 8.3.2. Dispersion The standard error of a summary is a useful measure of uncertainty if the observations come from a normal or Gaussian distribution. Then in 95 of the samples we would expect the sample mean to lie within two standard errors of the population mean. But if the obser vations come from any of the following A nonsymmetric distribution like an exponential or a Poisson A truncated distribution like the uniform A mixture of populations we cannot draw any such inference. For such a distribution the probability that a future observation would lie between plus and minus one standard error of the mean might be anywhere from 40 to 100 . Recall that the standard error of the mean equals the standard deviation of a single observation divided by the square root of the sample size. As the standard error depends on the squares of .