How many mins are in 4 and a half hours.
1 answer:
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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.
Answer:
Explained below.
Step-by-step explanation:
The data provided is for the dying time of four different types of paint.
One-way ANOVA can be used to determine whether all the four paints have the same drying time.
Use Excel to perform the one-way ANOVA.
Go to Data → Data Analysis → Anova: Single Factor
A dialog box will open.
Select the data.
Select "Grouping" as Columns.
Press OK.
The output is attached below.
The required values are as follows:
(1)
Sum of Squares of Treatment (Between Subjects):
SST = 330
(2)
Sum of Squares of Error (Within Subjects):
SSE = 692
(3)
Mean Squares Treatment (Between Subjects):
MST = 110
(4)
Mean Squares Error (Within Subjects):
MSE = 43.25
