Listings for Female Realtors Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Sum Count 7.238095 2.051602 5 0 9.401621 88.39048 12.4364 3.234869 44 0 44 152 21 Listings for Male Realtors Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Su Count 12.31579 3.765846 10 6 16.41494 269.4503 14.09694 3.54549 76 0 76 234 19

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Please help me check my answers on the shape of the data and the outliers sections. I am pretty lost and not sure if I did it correctly… thank you so much
Listings for Female Realtors
Mean
Standard Error
Median
Mode
Standard Deviation
Sample Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
7.238095
2.051602
5
0
9.401621
88.39048
12.4364
3.234869
44
0
44
152
21
Listings for Male Realtors
Mean
Standard Error
Median
Mode
Standard Deviation
Sample Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Su
Count
12.31579
3.765846
06
10
16.41494
269.4503
14.09694
3.54549
76
60
76
234
19
Transcribed Image Text:Listings for Female Realtors Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Sum Count 7.238095 2.051602 5 0 9.401621 88.39048 12.4364 3.234869 44 0 44 152 21 Listings for Male Realtors Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Su Count 12.31579 3.765846 06 10 16.41494 269.4503 14.09694 3.54549 76 60 76 234 19
Describe the shape of the male listings data and the shape of the female listings data, to the extent
you can with the numbers provided (put your answer in the table below):
Realtor group
Shape of the data
Explain your answer
Male realtors
Skewed right (positive)
The mean (12.3) is bigger than the median (10)
Female realtors
Skewed right (positive) The mean (7.2) is bigger than the median (5)
Are there outliers? (put your answer in the table below)
Are there outliers?
Explain your answer
Yes (upper end) and no I took the mean (12.3) and added it to the standard
(lower end)
deviation three times (16.4) and got 61.5. The maximum
in the data set was 76, which means there definitely is
an outlier on the upper end because it did not equal
greater than the max.
Realtor group
Male realtors
Female realtors
Yes (upper end) and no
(lower end)
When I subtracted 16.4 from 12.3 three times, the
number was less than the minimum 0, so there was no
outlier.
I took the mean (7.2) and added it to the standard
deviation three times (9.4) and got 35.4. The maximum
in the data set was 44, which means there definitely is
an outlier or the upper end because it did not equal
greater than the max.
When I subtracted 7.2 from 9.4 three times, the number
was less than the minimum 0, so there was no outlier.
Transcribed Image Text:Describe the shape of the male listings data and the shape of the female listings data, to the extent you can with the numbers provided (put your answer in the table below): Realtor group Shape of the data Explain your answer Male realtors Skewed right (positive) The mean (12.3) is bigger than the median (10) Female realtors Skewed right (positive) The mean (7.2) is bigger than the median (5) Are there outliers? (put your answer in the table below) Are there outliers? Explain your answer Yes (upper end) and no I took the mean (12.3) and added it to the standard (lower end) deviation three times (16.4) and got 61.5. The maximum in the data set was 76, which means there definitely is an outlier on the upper end because it did not equal greater than the max. Realtor group Male realtors Female realtors Yes (upper end) and no (lower end) When I subtracted 16.4 from 12.3 three times, the number was less than the minimum 0, so there was no outlier. I took the mean (7.2) and added it to the standard deviation three times (9.4) and got 35.4. The maximum in the data set was 44, which means there definitely is an outlier or the upper end because it did not equal greater than the max. When I subtracted 7.2 from 9.4 three times, the number was less than the minimum 0, so there was no outlier.
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