# Super Levitra

By B. Dimitar. Montana Tech. 2019.

You need this information to understand both how the class performed and how you performed relative to everyone else generic super levitra 80 mg otc. But it is difficult to do this by looking at the individual scores or at the frequency distribution order super levitra cheap online. Instead trusted 80mg super levitra, it is much better if you know something like the class average; an average on the exam of 80 versus 30 is very understandable. Therefore, in virtually all research, we first com- pute a statistic that shrinks the data down into one number that summarizes everyone’s score. To understand central tendency, first change your perspective of what a score indicates. For example, if I am 70 inches tall, don’t think of this as indicating that I have 70 inches of height. Instead, think of any variable as an infinite continuum—a straight line—and think of a score as indicating a participant’s location on that line. If my brother is 60 inches tall, then he is located at the point marked 60 on the height variable. The idea is not so much that he is 10 inches shorter than I am, but rather that we are separated by a distance of 10 units— in this case, 10 “inch” units. In statistics, scores are locations, and the difference between any two scores is the distance between them. In our parking lot view of the normal curve, partici- pants’ scores determine where they stand. A high score puts them on the right side of the lot, a low score puts them on the left side, and a middle score puts them in a crowd in the middle. Further, if we have two distributions containing different scores, then the distributions have different locations on the variable. Thus, a measure of central tendency is a number that is a summary that you can think of as indicating where on the variable most scores are located; or the score that everyone scored around; or the typical score; or the score that serves as the address for the distribution as a whole. Notice that the above example again illustrates how to use descriptive statistics to envision the important aspects of the distribution without looking at every individual score. If a researcher told you only that one normal distribution is centered at 60 and the other is centered at 70, you could envision Figure 4. Thus, although you’ll see other statistics that add to this mental picture, measures of central tendency are at the core of sum- marizing data. The trick is to com- pute the correct one so that you accurately envision where most scores in the data are located. The scale of measurement used so that the summary makes sense given the nature of the scores. The shape of the frequency distribution the scores produce so that the measure accurately summarizes the distribution. In the following sections, we first discuss the mode, then the median, and finally the mean. The score of 4 is the mode because it occurs more frequently than any other score. Also, notice that the scores form a roughly normal curve, with the highest point at the mode. When a polygon has one hump, such as on the normal curve, the distribution is called unimodal, indicating that one score qualifies as the mode. For example, consider the scores 2, 3, 4, 5, 5, 5, 6, 7, 8, 9, 9, 9, 10, 11, and 12. Describing this distribution as bimodal and identifying the two modes does summarize where most of the scores tend to be located—most are either around 5 or around 9. The way to summarize such data would be to indicate the most frequently occurring category: Reporting that the mode was a preference for “Goopy Chocolate” is very in- formative. Also, you have the option of reporting the mode along with other measures of central tendency when describing other scales of measurement because it’s always informative to know the “modal score. First, the distribution may contain many scores that are all tied at the same highest frequency. In the most extreme case, we might obtain a rectangular distribution such as 4, 4, 5, 5, 6, 6, 7, and 7. A sec- ond problem is that the mode does not take into account any scores other than the most frequent score(s), so it may not accurately summarize where most scores in the distri- bution are located. For example, say that we obtain the skewed distribution containing 7, 7, 7, 20, 20, 21, 22, 22, 23, and 24. Because of these limitations, we usually rely on one of the other measures of central tendency when we have ordinal, interval, or ratio scores. Recall that 50% of a distribution is at or below the score at the 50th percentile. As we discussed in the previous chapter, when researchers are dealing with a large distribution they may ignore the relatively few scores at a percentile, so they may say that 50% of the scores are below the median and 50% are above it. To visualize this, re- call that a score’s percentile equals the proportion of the area under the curve that is to the left of—below—the score. Therefore, the 50th percentile is the score that separates the lower 50% of the distribution from the upper 50%. Because 50% of the area under the curve is to the left of the line, the score at the line is the 50th percentile, so that score is the median. In fact, the median is the score below which 50% of the area of any polygon is lo- cated. When scores form a perfect normal distribution, the median is also the most frequent score, so it is the same score as the mode. When scores are approximately normally distributed, the median will be close to the mode. When data are not at all normally distributed, however, there is no easy way to deter- mine the point below which. Also, recall that using the area under the curve is not accurate with a small sample. With an odd number of scores, the score in the middle position is the ap- proximate median. For example, for the nine scores 1, 2, 3, 3, 4, 7, 9, 10, and 11, the score in the middle position is the fifth score, so the median is the score of 4. On the other hand, if N is an even number, the average of the two scores in the middle is the approximate median. For example, for the ten scores 3, 8, 11, 11, 12, 13, 24, 35, 46, and 48, the middle scores are at position 5 (the score of 12) and position 6 (the score of 13). To precisely calculate the median, consult an advanced textbook for the formula, or as in Appendix B. High scores scores The Mean 65 Uses of the Median The median is not used to describe nominal data: To say, for example, that 50% of our participants preferred “Goopy Chocolate” or below is more confusing than informa- tive. On the other hand, the median is the preferred measure of central tendency when the data are ordinal scores.