Answer:
Largest Median: Same
Largest Range: Castro
Largest IQR: Castro
Step-by-step explanation:
With a box-and-whisker plot, the box represents the upper and lower quartiles, the vertical line inside the box represents the median, and the lines on either side of the box show the high and low of the range.
Largest Median: Medians are the same because the verticle line inside the boxes is at 7 for both
Largest Range: Ms Castro's Class- the lines on either side of the box for Ms Castro go from 1-10 while the other class only goes from 4-10.
Largest IQR (interquartile range) Ms. Castro's class: their IQR goes from 5-8 while the other class only goes from 6-8
Answer: $ 2.20
Step-by-step explanation: 31.80-18.60= 13.20
13.20 divided by 6= 2.20
——————-
2.20 x 6= 13.2
13.20+ 18.60= 31.80
70/2 = 35
The car gets 35 miles per gallons.
You can also find that out by looking at the slope of the equation. y = 35x. 35 is the slope here.
For your graph, plot the points:
(0,0)
(1,35)
(2,70)
(3,105)
(4,140)
(5,175)
(6,210)
(7,245)
(8,280)
And then draw a line through the points.
Answer:
A.The mean would increase.
Step-by-step explanation:
Outliers are numerical values in a data set that are very different from the other values. These values are either too large or too small compared to the others.
Presence of outliers effect the measures of central tendency.
The measures of central tendency are mean, median and mode.
The mean of a data set is a a single numerical value that describes the data set. The median is a numerical values that is the mid-value of the data set. The mode of a data set is the value with the highest frequency.
Effect of outliers on mean, median and mode:
- Mean: If the outlier is a very large value then the mean of the data increases and if it is a small value then the mean decreases.
- Median: The presence of outliers in a data set has a very mild effect on the median of the data.
- Mode: The presence of outliers does not have any effect on the mode.
The mean of the test scores without the outlier is:
*Here <em>n</em> is the number of observations.
So, with the outlier the mean is 86 and without the outlier the mean is 86.9333.
The mean increased.
Since the median cannot be computed without the actual data, no conclusion can be drawn about the median.
Conclusion:
After removing the outlier value of 72 the mean of the test scores increased from 86 to 86.9333.
Thus, the the truer statement will be that when the outlier is removed the mean of the data set increases.