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A series of questions and answers related to the interpretation of histograms, focusing on data distribution analysis. It covers key concepts such as percentage calculations, shape identification (skewness and outliers), and the impact of outliers on statistical measures like mean and median. The document also addresses the modal number in a dataset. It is designed to enhance understanding of statistical data representation and analysis, particularly in the context of trauma-related mortality data. The questions are based on a histogram showing the distribution of ages at death due to trauma observed in a hospital during a week. Detailed explanations for each answer, making it a valuable resource for students learning about descriptive statistics and data interpretation.
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The histogram below displays the distribution of 50 ages at death due to trauma (accidents and homicides) that were observed in a certain hospital during a week. What percentage of deaths were individuals younger than 35? - ANSWER 68% From the histogram we can find that 3 + 18 + 13 = 34 out of the 50 observations fall below age 35 (those within the first three bars), and since the question asks about the percentage of observations, the answer is (34/50) * 100 = 68%. Here again is the histogram showing the distribution of 50 ages at death due to trauma (accidents and homicides) that were observed in a certain hospital during a week. Which of the following best describes the shape of the histogram? - ANSWER Right- skewed with a possible outlier The histogram displays a tail to the right (i.e., it is skewed right) with one isolated observation at around 90 years, which is possibly an outlier. Here again is the histogram showing the distribution of 50 ages at death due to trauma (accidents and homicides) that were observed in a certain hospital during a week. Assume that the largest observation in this dataset is 90. If this observation were wrongly recorded as 900, then: - ANSWER The mean will increase, but the median won't change. The mean will increase, because the mean tends to be pulled by an outlier. So moving the largest value, 90, farther to the right, to 900, would tend to pull the mean to the right, making it larger. The median wouldn't be affected, because moving 90 to 900 wouldn't change the 25th or the 26th value in the data. The following data, on the number of children ever born per 1,000 women, are from the Current Population Reports - Fertility of American Women: 2008 For the data described by the above histogram, - ANSWER (B) the median will be smaller than the mean Remember that we'd expect the mean and the median to be nearly the same only if the distribution were nearly symmetric. But notice this distribution is skewed to the right (the tail is towards the right) and we also see a suspected outlier on the right side. Remember that both of these shape features will tend to pull the mean more than they would pull the median.
Here again is the histogram showing the distribution of 50 ages at death due to trauma (accidents and homicides) that occurred in a certain hospital during a week. A possible value of the median in this example is: - ANSWER 33 There are n=50 people in the data and therefore the median is the average of the 25th and 26th ranked observations. Note from the histogram that these two observations fall in the third age interval (from the left) which is [25, 35). The median must therefore also be an age between 25 and 35. The only possible answer in that interval is 33. Here again is the histogram showing the distribution of 50 ages at death due to trauma (accidents and homicides) that were observed in a certain hospital during a week. What is the modal number of children ever born to American women, in 2008? - ANSWER 0 The most commonly occurring value is zero; therefore, zero is the modal number of children born to American women, in 2008.