Arithmetic Mean from Frequency Table
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Q. The production of soyabean per acre, in quintal obtained by 27 farmers in Raipur, is given below:
9, 6, 7, 7, 5, 4.5, 8.5, 4, 5, 4, 9, 9, 8.5, 7, 7, 7, 6, 5, 5, 5, 5, 4.5, 4.5, 4.5, 4, 4, 4
From the given data, prepare a frequency table and find the average production per acre of soyabean.
9, 6, 7, 7, 5, 4.5, 8.5, 4, 5, 4, 9, 9, 8.5, 7, 7, 7, 6, 5, 5, 5, 5, 4.5, 4.5, 4.5, 4, 4, 4
From the given data, prepare a frequency table and find the average production per acre of soyabean.
- 5.1 quintal
- 5.2 quintal
- 5.0 quintal
- 5.9 quintal
Q. Calculate the arithmetic mean of the following frequency table.
xifi1202531545
xifi1202531545
- 3.11
- 2
- 2.11
- 3
Q. ∑fi and ∑fixi of the given data are respectively_____ and ______.
Number of runs(xi)Tally marks1|||2||||||4|6||||
Number of runs(xi)Tally marks1|||2||||||4|6||||
- 20, 50
- 45, 15
- 15, 45
- 10, 25
Q. The mean of the following frequency table is 10. Find the value of a.
xi | fi |
10 | 10 |
12 | 5 |
14 | a |
15 | 2 |
5 | 8 |
10
7
8
5
Q. ∑ (sigma) indicates the ____.
- sum
- subtraction
- multiplication
- division
Q. ![](https://search-static.byjusweb.com/question-images/byjus/infinitestudent-images/ckeditor_assets/pictures/389147/original_g5.png)
![](https://search-static.byjusweb.com/question-images/byjus/infinitestudent-images/ckeditor_assets/pictures/389147/original_g5.png)
- 3.2
- 3.3
- 3.4
- 3.1
Q. The marks obtained by 22 students of std 8 in a test of 80 marks are given below. Find the mean of the data.
![](https://search-static.byjusweb.com/question-images/byjus/infinitestudent-images/ckeditor_assets/pictures/393785/original_n1.png)
![](https://search-static.byjusweb.com/question-images/byjus/infinitestudent-images/ckeditor_assets/pictures/393785/original_n1.png)
- 54.22
- 54.23
- 54
- 54.11
Q. If values of x of the following frequency table is increased by 1 then what happens to its mean?
x | f |
1 | 6 |
2 | 4 |
3 | 2 |
4 | 5 |
5 | 3 |
- It is increased by 1
- It is decreased by 1
- It is doubled
- It stays the same
Q. Find the value of ∑fixi when the mean = 6 and N = 9.
- 44
- 40
- 42
- 54
Q. The following table shows the monthly income of 20 families. How many families have Rs 50000 as their monthly income.
Monthly income(Rs) | Number of families(frequency) |
20, 000 | 2 |
50, 000 | 4 |
1, 00, 000 | 5 |
75, 000 | 6 |
60, 000 | 3 |
5
4
2
3
Q. The number of members in the 22 families in Bhilar are as follows:
1, 1, 2, 2, 4, 1, 1, 1, 2, 2, 2, 2, 3, 4, 3, 3, 3, 5, 5, 6, 7, 6. Prepare the frequency table.
1, 1, 2, 2, 4, 1, 1, 1, 2, 2, 2, 2, 3, 4, 3, 3, 3, 5, 5, 6, 7, 6. Prepare the frequency table.
Q. The following table shows the monthly income of 20 families.Find the mean monthly income of those families.
Monthly income (xi) |
Number of families (fi) |
fixi |
20000 | 2 | 40000 |
50000 | 4 | 200000 |
100000 | 5 | 500000 |
75000 | 6 | 450000 |
60000 | 3 | 180000 |
Q.
The mean of the following distribution is:
xi10131619fi2576
15.2
15.35
15.55
16
Q. The sum of the frequencies of the runs 2 and 6 is _____.
Number of runs(xi)Tally marksFrequency(fi)fixi1|||332||||||7144|146||||424
Number of runs(xi)Tally marksFrequency(fi)fixi1|||332||||||7144|146||||424
- 11
- 7
- 15
- 10
Q.
Find the missing number.
Q.
Find the missing number.
Q. ![](https://search-static.byjusweb.com/question-images/byjus/infinitestudent-images/ckeditor_assets/pictures/390010/original_p4.png)
Find the value of mean.
![](https://search-static.byjusweb.com/question-images/byjus/infinitestudent-images/ckeditor_assets/pictures/390010/original_p4.png)
Find the value of mean.
- 6.5
- 6.2
- 6.3
- 6.1
Q.
The average value of a group of observations is 25. If the observations are 29, 32, 16, 7, x and x+4, then find the value of x.
31
53
21
43
Q. ![](https://search-static.byjusweb.com/question-images/byjus/infinitestudent-images/ckeditor_assets/pictures/389145/original_g4.png)
![](https://search-static.byjusweb.com/question-images/byjus/infinitestudent-images/ckeditor_assets/pictures/389145/original_g4.png)
- 4.5
- 4.6
- 4.1
- 4.2
Q. ![](https://search-static.byjusweb.com/question-images/byjus/infinitestudent-images/ckeditor_assets/pictures/389141/original_g3.png)
![](https://search-static.byjusweb.com/question-images/byjus/infinitestudent-images/ckeditor_assets/pictures/389141/original_g3.png)
- 5
- 5.2
- 5.3
- 5.1
Q. The following table shows the monthly income of 20 families.Find the mean monthly income of those families.
Monthly income (xi) |
Number of families (fi) |
fixi |
20000 | 2 | 40000 |
50000 | 4 | 200000 |
100000 | 5 | 500000 |
75000 | 6 | 450000 |
60000 | 3 | 180000 |
Q. Consider the following frequency distribution;
Monthy income in Rs
More than or equal to 10000
More than or equal to 13000
More than or equal to 16000
More than or equal to 19000
More than or equal to 22000
More than or equal to 25000 No of families
100
85
69
50
33
15
Find the number of families having income range from Rs 16000 to Rs 19000
More than or equal to 10000
More than or equal to 13000
More than or equal to 16000
More than or equal to 19000
More than or equal to 22000
More than or equal to 25000 No of families
100
85
69
50
33
15
Find the number of families having income range from Rs 16000 to Rs 19000
Q.
Find the missing number.
Q. The mean of the following frequency table is 10. Find the value of a.
(2 marks)
xi | fi |
10 | 10 |
12 | 5 |
14 | a |
15 | 2 |
5 | 8 |
(2 marks)
Q.
Find the missing number.
Q. The weekly pocket expenses (in rupees) of 30 students of a class are given below:
62, 80, 110, 75, 84, 73, 60, 62, 100, 87, 78, 94, 117, 86, 65, 68, 90, 80, 118, 72, 95, 72, 103, 96, 64, 94, 87, 85, 105, 115.
Construct a frequency table with class intervals 60−70 (where 70 is not included), 70−80, 80−90, etc.
62, 80, 110, 75, 84, 73, 60, 62, 100, 87, 78, 94, 117, 86, 65, 68, 90, 80, 118, 72, 95, 72, 103, 96, 64, 94, 87, 85, 105, 115.
Construct a frequency table with class intervals 60−70 (where 70 is not included), 70−80, 80−90, etc.
Q.
Find the missing number.
Q.
Find the missing number
Q. What is the average of 13 and 16?
- 14
- 29
- 23
- 19
Q. The total score made by the men of groups G1 and G3 is
![](data:image/png;base64, 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)
- 45
- 60
- 55
- 50