By D. J., Chambers, Joan Sybil Crawshaw

This identify is absolutely revised and up-to-date for entire assurance of data at complex point. using a moment colour highlights key components and formulation. Summaries are incorporated to supply consolidation of studying and knowing. extra perform sections of questions are provided for thorough examination instruction and perform. New ICT help for this key zone is equipped all through.

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**Extra info for A Concise Course in Advanced Level Statistics: With Worked Examples, Fourth Edition**

**Example text**

C. Once a random sample is chosen, we can use statistical inference to draw conclusions about the entire population by studying the elements of the sample. 1 Stratiﬁed Random Sampling A more sophisticated approach to sampling than simple random sampling is the stratiﬁed random sampling approach. This approach, which requires more initial information about the population than does simple random sampling, can be explained as follows. Consider a high school that contains 300 students in ∗ The asterisk signiﬁes optional material not used in the sequel.

4. Draw adjacent bars having heights determined by the frequencies in step 3. The importance of a histogram is that it enables us to organize and present data graphically so as to draw attention to certain important features of the data. For instance, a histogram can often indicate 1. 2. 3. 4. 5. 7. 8 Characteristics of data detected by histograms. (a) symmetry, (b) degree of spread and where values are concentrated, and (c) gaps in data and data far from others. For instance, the histogram presented in Fig.

15 = 15 ﬁrst-year students in the sample. 30 = 30 third-year and 30 fourth-year students. Then one selects students from each class at random. In other words, in this type of sample, ﬁrst the population is stratiﬁed into subpopulations, and then the correct number of elements is randomly chosen from each of the subpopulations. As a result, the proportions of the sample members that belong to each of the subpopulations are exactly the same as the proportions for the total population. Stratiﬁcation is particularly effective for learning about the “average” member of the entire population when there are inherent differences between the subpopulations with respect to the question of interest.