Question
Answer the following with the help of the data.
4,5,6,6,6,7,7,7,7,7,7,8,8,8,9,10
What is the shape of the data set?
- Skewed
- Symmetric
- Left skewed
- Both a and c
The correct answer is: Symmetric
Related Questions to study
Answer the following with the help of the data.
4,5,6,6,6,7,7,7,7,7,7,8,8,8,9,10
What is the median of the data set?
Answer the following with the help of the data.
4,5,6,6,6,7,7,7,7,7,7,8,8,8,9,10
What is the median of the data set?
Answer the following with the help of the data.
4,5,6,6,6,7,7,7,7,7,7,8,8,8,9,10
What is the mean of the data set?
Answer the following with the help of the data.
4,5,6,6,6,7,7,7,7,7,7,8,8,8,9,10
What is the mean of the data set?
Answer the following with the help of data (18 to 20).
5, 15, 20, 10, 25, 35, 30, 40, 55, 60
What is the shape of the histogram of the data set?
Answer the following with the help of data (18 to 20).
5, 15, 20, 10, 25, 35, 30, 40, 55, 60
What is the shape of the histogram of the data set?
Answer the following with the help of data (18 to 20).
5, 15, 20, 10, 25, 35, 30, 40, 55, 60
What is the median of the given data?
Answer the following with the help of data (18 to 20).
5, 15, 20, 10, 25, 35, 30, 40, 55, 60
What is the median of the given data?
Answer the following with the help of data (18 to 20).
5, 15, 20, 10, 25, 35, 30, 40, 55, 60
What is the mean of the data?
Answer the following with the help of data (18 to 20).
5, 15, 20, 10, 25, 35, 30, 40, 55, 60
What is the mean of the data?
Answer the following with the help of the dot plots.
The data for the survey for………….. is more symmetrical than the data for the survey for………….
Answer the following with the help of the dot plots.
The data for the survey for………….. is more symmetrical than the data for the survey for………….
Answer the following with the help of the dot plots.
The mode of the data for the survey results for teachers is ………….than the mode of the data for the survey results for students.
Answer the following with the help of the dot plots.
The mode of the data for the survey results for teachers is ………….than the mode of the data for the survey results for students.
Answer the following with the help of the dot plots.
The range of the data for the survey results for teachers is ………….than the range of the data for the survey results for students.
Answer the following with the help of the dot plots.
The range of the data for the survey results for teachers is ………….than the range of the data for the survey results for students.
What is the upper limit of modal class?
What is the upper limit of modal class?
What is the upper limit of modal class?
What is the upper limit of modal class?
What is the modal class of the given data?
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)
What is the modal class of the given data?
![](data:image/png;base64,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)
Determine the shape of histogram of the given data.
25, 75, 50, 20, 15
Determine the shape of histogram of the given data.
25, 75, 50, 20, 15
Find the mean and median of the following data.
10, 10, 20, 40, 70, 80
The mean (average) of a data set is found by adding all numbers in the data set and then dividing by the number of values in the set. The median is the middle value when a data set is ordered from least to greatest.
Find the mean and median of the following data.
10, 10, 20, 40, 70, 80
The mean (average) of a data set is found by adding all numbers in the data set and then dividing by the number of values in the set. The median is the middle value when a data set is ordered from least to greatest.