Here is a small part of an EESEE data set, "Nutrition and Breakfast Cereals," that describes the

Step 1: **Cold/Hot** is a **dichotomous** variable because it has only two possible values: "C"

Here is a small part of an EESEE data set, "Nutrition and...

Here is a small part of an EESEE data set, "Nutrition and Breakfast Cereals," that describes the nutritional content per serving of 77 brands of breakfast cereal: Brand name Manufacturer Cold/Hot Calories (Cal) Sugar (grams) Fiber (grams) All Bran K C 70 5 9 All Bran with K C 50 0 14 Extra Fiber Almond Delight R C 110 8 1 Apple Cinnamon G C 110 10 2 Cheerios Apple Jacks K C 110 14 1 Question 2 16 / 20 points For each variable in the "Nutrition and Breakfast Cereals," indicate which of the four variable types (dichotomous, polytomous, discrete, continuous) is most appropriate. 1 Cold/Hot 3 Calories (Cal) 4 Sugar (grams) 2 Manufacturer 4 Fiber (grams) 1. Dichotomous 2. Polytomous 3. Discrete 4. Continuous

Transcript

00:01 So for this problem, in part a to begin, we have that the mode is the value that occurs most frequently.

00:06 We can see the only calorie count that occurs more than once is 100.

00:11 So that would be our mode.

00:13 For determining the range, we simply take the maximum value minus the minimum value.

00:19 So that would be 200 minus 80, giving a range of 120.

00:25 The median, we would find, in this case, i'll note that we have, since n is equal to 7, we have 7 individual values, we'd find the rank of the median by taking n plus 1 over 2, or pardon me, n plus 1 over 2.

00:46 So the rank here would be 8 over 2, suggesting that the rank of the median is going to be the fourth value in order.

00:53 So we can see that the fourth value, when everything is put in order, which, that is the case here, would be 110.

01:01 So that is going to be our median.

01:03 Then to find the average, we'll add up all the individual values and then divide by the number of values.

01:16 So we'd have 80 plus 100 plus 100, plus 110, plus 130, plus 190, plus 200, plus 200, divided by 7.

01:27 So the average is 130.

01:30 To find the standard deviation, i'll note that this is a sample standard deviation, that's equal to the square root of 1 over n minus 1.

01:40 Times the sum of the difference between each measurement and the mean value squared.

01:46 All right, so off to the side here, you can see that in my software i've put in the data, then i've subtracted 130 from each data point.

01:57 So 80 minus 130 gives negative 50, 100 minus 130 gives negative 30, and so on.

02:03 Then we want to square each one of those values.

02:08 So negative 50 squared gives 2 ,500, negative 30 squared.

02:12 Gives 900 and so on...

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