2.1

Bell-Shaped Histogram

Histogram with single mode the near center of the data, and are approximately symmetric.

uniform histogram

A histogram where the bars are roughly the same height.

skewed left histogram

A histogram with a longer tail on the left side

skewed right histogram

A histogram with a longer tail on the right side

A data set has values ranging from a low of 10 to a high of 50. The class width is to be 10. What's wrong with using the class limits 10-20, 21-31, 32-42, 43-53 for a frequency table with a class width of 10? A)The classes listed have a class width of 11. B)The classes overlap so that some data values fall within two classes. C)There is nothing wrong with using these class limits for a frequency table with a class width of 10. D)Each data value must fall into one class. Some data values do not have a class.

A)The classes listed have a class width of 11.

A data set has values ranging from a low of 10 to a high of 52. What's wrong with using the class limits 10-19, 20-29, 30-39, 40-49 for a frequency table? A)There is nothing wrong with using these class limits for the data set. B)Each data value must fall into one class. The data values of 50 and above do not have a class. C)The class widths are too wide and not evenly spread out over the data range of 10 to 52. D)Each data value must fall into only one class. The classes have overlapping values.

B)Each data value must fall into one class. The data values of 50 and above do not have a class.

A data set has values ranging from a low of 10 to a high of 50. What's wrong with using the class limits 10-20, 20-30, 30-40, 40-50 for a frequency table? A)There is nothing wrong with using these class limits for the data set. B)The classes overlap so that some data values, such as 20, fall within two classes. C)Each data value must fall into one class. Some data values do not have a class. D)The class widths are too wide for the data range of 10 to 50.

B)The classes overlap so that some data values, such as 20, fall within two classes

class width

largest score - smalled score -------------------------------- number of classes

class limits

must know class width. start with the lowest score example 51 students, the beginning limit will be 50. the class width is 20 so add 20 to 50 to set the beginning limits such as 50-69 70-89 90-109 110-129 130-149 150-169 170-189

class boundaries

take the ending limit of one class and add it to the upper limit of another class and divide by two example 69+70 /2 = 69.5 50-69 70-89 90-109 110-129 130-149 150-169 170-189 boundaries 49.5-69.5 69.5-89.5 89.5-109.5

class frequency

the number of observations in the data set falling into a particular class

bimodal histogram

two peaks

You are manager of a specialty coffee shop and collect data throughout a full day regarding waiting time for customers from the time they enter the shop until the time they pick up their order. What if the distribution for waiting times were bimodal? What might be some explanations? A)A bimodal distribution for waiting times might exist if almost all orders are filled in approximately the same amount of time. B)A bimodal distribution for waiting times might exist if almost all orders take a long time to fill and only a couple orders are filled very quickly. C)A bimodal distribution for waiting times might exist if orders are filled at different rates during busy and slow periods. D)A bimodal distribution for waiting times might exist if almost all orders are filled very quickly and only a couple orders get lost throughout the day.

C)A bimodal distribution for waiting times might exist if orders are filled at different rates during busy and slow periods.

What is the difference between a class boundary and a class limit? (Select all that apply.)

Class boundaries are values halfway between the upper class limit of one class and the lower class limit of the next. Class boundaries are not possible data values. Class limits specify the span of data values that fall within a class. Class limits are possible data values.

You are manager of a specialty coffee shop and collect data throughout a full day regarding waiting time for customers from the time they enter the shop until the time they pick up their order. What type of distribution do you think would be most desirable for the waiting times: skewed right, skewed left, mound-shaped symmetric? Explain. A)A skewed left distribution would be the most desirable because this would mean there are never any long waiting times. B)A skewed right distribution would be the most desirable because this would mean there are never any long waiting times. C)A mound shaped symmetric distribution would be the most desirable distribution because this would mean are a lot of short waiting times and only a few long waiting times. D)A skewed right distribution would be the most desirable because this would mean there are a lot of short waiting times and only a few long waiting times. E)A skewed left distribution would be the most desirable because this would mean there are a lot of short waiting times and only a few long waiting times.

D)A skewed right distribution would be the most desirable because this would mean there are a lot of short waiting times and only a few long waiting times.