![]() Navigate to STAT ( MENU, then hit 2) and enter the data into a list. We take the median in this case to be the average of the two middle observations: \((6,768+7,012)/2 = 6,890\text\)Įnter the data to be graphed as described previously.ĭown arrow and then right arrow three times to select box plot with outliers.ĭown arrow again and make Xlist: L1 and Freq: 1.Ĭhoose ZOOM and then 9:ZoomStat to get a good viewing window.Ĭasio fx-9750GII: Drawing a box plot and 1-variable statistics There are 50 character counts in the email50 data set (an even number) so the data are perfectly split into two groups of 25. The median splits an ordered data set in half. The median provides another measure of center. However, we have provided an online supplement on weighted means for interested readers: Had we computed the simple mean of per capita income across counties, the result would have been just $22,504.70!Įxample 2.2.5 used what is called a weighted mean, which will not be a key topic in this textbook. What is the median of the exam scores To find the median, we need to identify the vertical line inside the box. If we completed these steps with the county data, we would find that the per capita income for the US is $27,348.43. The following box plot shows the distribution of scores on a certain college exam. Box and whisker plots, sometimes known as box plots, are a great chart to use when showing the distribution of data points across a selected measure. Instead, we should compute the total income for each county, add up all the counties' totals, and then divide by the number of people in all the counties. If we were to simply average across the income variable, we would be treating counties with 5,000 and 5,000,000 residents equally in the calculations. The county data set is special in that each county actually represents many individual people. Step 2: Determine the first, second, and third quartiles of our data set. These will be the ends of the 'whiskers' of our plot. Inference for the slope of a regression line Step 1: Determine the lowest and highest values of our data set.Fitting a line by least squares regression.Line fitting, residuals, and correlation.Comparing many means with ANOVA (special topic). ![]()
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