In contrast, analyzing a sample means you are working with a subset drawn from a larger population, and any statements made about the larger group from which your sample was drawn are probabilistic rather than absolute. Analyzing a population means your data set is the complete population of interest, so you are performing your calculations on all members of the group of interest to you and can make direct statements about the characteristics of that group. For instance, the final exam grades of the students in a class are a population if the purpose of the analysis is to describe the distribution of scores in that class, but they are a sample if the purpose of the analysis is to make some inference from those scores to the scores of other students (perhaps students in different classes or different schools). The same data set may be considered as either a population or a sample, depending on the reason for its collection and analysis. For instance, a business might want to monitor sales volumes for different locations or different sales personnel and wish to present that information using graphics, without any desire to use that information to make inferences (for instance, about other locations or other years) using the data collected. Descriptive statistics and graphic displays can also be the final product of a statistical analysis. You can never be too familiar with your data, and time spent examining it is nearly always time well spent. In particular, it is a common practice to begin an analysis by examining graphical displays of a data set and to compute some basic descriptive statistics to get a better sense of the data to be analyzed. Nearly everyone involved in statistical work works with both types of statistics, and often, computing descriptive statistics is a preliminary step in what will ultimately be an inferential statistical analysis. However, another type of statistics is the concern of this chapter: descriptive statistics, meaning the use of statistical and graphic techniques to present information about the data set being studied. Most of this book, as is the case with most statistics books, is concerned with statistical inference, meaning the practice of drawing conclusions about a population by using statistics calculated on a sample. Draw the leaves as 1's place value.Chapter 4. Descriptive Statistics and Graphic Displays Step 4 - Draw the stem numbers as 10's place-value digits 5, 6, 7, 8 and 9 (each number is representing 10 units). Step 3 - Group the numbers based on stem value. Step 2 - Choose step as largest place value. Step 1 - Sort the numbers in ascending order. ![]() ExampleÄraw Stemplot diagram for the following data points. Stems and leaves may be labelled as - millions, thousands, ones, tenths, etc. In figure above, the stems are tens (here 5 represents 50, 6 represents 60, and so on) and the leaves are actual values. ![]() In a stemplot, left side entries are called stems and the right side entries are called leaves. A Stemplot is used to draw quantitative data with fewer than 50 observations. Stemplots are also called stem and leaves plot as there is one step with largest place value digits on the left and at leaf(ves) to the right. Stemplots are similar to histogram with the difference that in histogram, bars are used to compare data and in case of stemplots leaves represents actual numbers to be compared.
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