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CLEVELAND -- James Schamus makes his directing debut with "Indignation," but he's hardly a newbie in the independent film world.

As a screenwriter and producer, he has collaborated since 1992 with director Ang Lee on films as varied as "Eat Drink Man Woman," "Brokeback Mountain" and "Crouching Tiger, Hidden Dragon." As the longtime head of Focus Features, he championed the likes of Sofia Coppola, Todd Haynes and Mira Nair.

All of that experience informs his work writing and directing "Indignation," a finely calibrated, if a bit deliberate, adaptation of Philip Roth's 2008 coming-of-age novel.

It is 1951, and Marcus Messner (Logan Lerman) -- one of Roth's many literary alter egos -- is fleeing both the war in Korea and his overbearing Jewish parents in Newark, New Jersey, by heading to Ohio for college.

"Ohio?" says a neighbor, in a tone that suggests Marcus is planning to study with the pope himself at the Vatican. "How will you keep kosher?"

That's the least of Marcus's concerns once he arrives at Winesburg College (Sherwood Anderson reference intended) and discovers that he's one of just 80 Jews on a campus of 1,400, and that his presence will be required at weekly chapel for prayers and Christian teachings.

Before he leaves, his anxious father (Danny Burstein) is already in mourning, wailing, "If anything should happen to you! It's about the tiniest mistake that can happen to you!"

Which states, in blinking neon, the core theme of Roth's novel: Death is going to happen to you, like it or not, and you will make the decisions - or mistakes -- that cause it.

What mistakes does Marcus make at Winesburg? First, he falls for Olivia Hutton (Sarah Gadon), a dazzling, sexually liberated shiksa who turns out to be a deeply troubled girl. Then, he mixes it up in two extended arguments with the pompous Dean Caudwell (Tracy Letts), who admires Marcus's intellectual acumen but wants him to conform to the stultifying culture of Winesburg.

The first of these debates forms the centerpiece of "Indignation," a gambit that works far better in print than on screen. Though both actors handle the heated argument with agility and even a touch of wry humor, it's still 15 minutes of two people talking in an office, and it grows tiresome.

At a crisis point, Marcus's mother (Linda Emond) arrives from Newark on a rescue mission. In one of Roth's signature ironies, though, her meddling is that "tiniest mistake" in a chain of mistakes that lead to disaster.

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Quartiles are useful, but they are also somewhat limited because they do not take into account every score in our group of data. To get a more representative idea of spread we need to take into account the actual values of each score in a data set. The absolute deviation, variance and top air jordans 2013
are such measures.

The absolute and mean absolute deviation show the amount of deviation (variation) that occurs around the mean score. To find the total variability in our group of data, we simply add up the deviation of each score from the mean. The average deviation of a score can then be calculated by dividing this total by the number of scores. How we calculate the deviation of a score from the mean depends on our choice of statistic, whether we use absolute deviation, variance or nike roshe run amazon mens
.

Perhaps the simplest way of calculating the deviation of a score from the mean is to take each score and minus the mean score. For example, the mean score for the group of 100 students we used earlier was 58.75 out of 100. Therefore, if we took a student that scored 60 out of 100, the deviation of a score from the mean is 60 - 58.75 = 1.25. It is important to note that scores above the mean have positive deviations (as demonstrated above), whilst scores below the mean will have negative deviations.

To find out the total variability in our data set, we would perform this calculation for all of the 100 students' scores. However, the problem is that because we have both positive and minus signs, when we add up all of these deviations, they cancel each other out, giving us a total deviation of zero. Since we are only interested in the deviations of the scores and not whether they are above or below the mean score, we can ignore the minus sign and take only the absolute value, giving us the absolute deviation . Adding up all of these absolute deviations and dividing them by the total number of scores then gives us the mean absolute deviation (see below). Therefore, for our 100 students the mean absolute deviation is 12.81, as shown below:

absolute deviation

Another method for calculating the deviation of a group of scores from the mean, such as the 100 students we used earlier, is to use the variance. Unlike the absolute deviation, which uses the absolute value of the deviation in order to "rid itself" of the negative values, the variance achieves positive values by squaring each of the deviations instead. Adding up these squared deviations gives us the sum of squares, which we can then divide by the total number of scores in our group of data (in other words, 100 because there are 100 students) to find the variance (see below). Therefore, for our 100 students, the variance is 211.89, as shown below:

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