![]() ![]() lower whisker smallest observation greater than or equal to lower hinger - 1.5 IQR. statboxplot () provides the following variables, some of which depend on the orientation: width of boxplot. Navigate to STAT ( MENU, then hit 2) and enter the data into a list. These are calculated by the stat part of layers and can be accessed with delayed evaluation. 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. If we completed these steps with the county data, we would find that the per capita income for the US is $27,348.43. 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. A box plot is constructed from five values: the minimum value, the first quartile, the median, the third quartile, and. They also show how far the extreme values are from most of the data. The county data set is special in that each county actually represents many individual people. Box plots (also called box-and-whisker plots or box-whisker plots) give a good graphical image of the concentration of the data. Inference for the slope of a regression line.Fitting a line by least squares regression.Line fitting, residuals, and correlation.Comparing many means with ANOVA (special topic).Difference of two means using the \(t\)-distribution.Inference for a single mean with the \(t\)-distribution.Homogeneity and independence in two-way tables.Testing for goodness of fit using chi-square.Sampling distribution of a sample proportion.Case study: gender discrimination (special topic).Observational studies and sampling strategies.Case study: using stents to prevent strokes.OpenIntro, online resources, and getting involved. ![]()
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