
Find the arithmetic mean of the following data by shortcut method.
Class Interval 0 – 10 10 – 20 20 – 30 30 – 40 40 – 50 50 – 60 Frequency 4 8 13 20 12 8
(a) 21
(b) 22
(c) 30
(d) 33
| Class Interval | 0 – 10 | 10 – 20 | 20 – 30 | 30 – 40 | 40 – 50 | 50 – 60 |
| Frequency | 4 | 8 | 13 | 20 | 12 | 8 |
Answer
579.3k+ views
Hint: To solve this question, we will first all assume a mean after finding \[{{x}_{i}}\] by using \[{{x}_{i}}=\] upper limit of the class interval + lower limit of class interval and dividing by 2. And after assuming the mean, we will draw the table and use the formula \[\text{Mean}=\overline{x}=A+\left[ \dfrac{1}{N}\sum{{{f}_{i}}{{d}_{i}}} \right]\] to calculate the mean.
Complete step by step answer:
We have to find the mean using the shortcut method. It can be given as stated and explained below. If \[{{x}_{1}},{{x}_{2}},...{{x}_{n}}\] are the observations given with respective frequencies \[{{f}_{1}},{{f}_{2}},....{{f}_{n}}.\] Let the deviation A take at any point, we have, \[{{d}_{i}}={{x}_{i}}-A\] where i = 1, 2, …n. So, we can say, \[{{f}_{i}}{{d}_{i}}={{f}_{i}}\left( {{x}_{i}}-A \right)\] where i = 1, 2, … n. Then the mean \[\overline{x}\] is given by the formula,
\[\overline{x}=\dfrac{\sum{{{x}_{i}}{{f}_{i}}}}{\sum{{{f}_{i}}}}\]
We have our data as,
Let us assume A = 13, then we will calculate \[{{d}_{i}}={{x}_{i}}-A,\] i = 1, 2, …6 and \[{{f}_{i}}{{d}_{i}}.\] But before doing so, we need \[{{x}_{i}},i=1,2,.....6\] as here class interval is given as \[{{x}_{i}}=\text{mid point of class interval}\text{.}\] This gives our new table as
Finally, we will use the mean formula given as
\[\text{Mean}=\overline{x}=A+\left[ \dfrac{1}{N}\sum{{{f}_{i}}{{d}_{i}}} \right]\]
\[\Rightarrow \text{Mean}=\overline{x}=25+\dfrac{520}{65}\]
\[\Rightarrow \text{Mean}=\overline{x}=25+8\]
\[\Rightarrow \text{Mean}=\overline{x}=33\]
Hence, the mean of the given data is \[\overline{x}=33.\]
So, the correct answer is “Option D”.
Note: Another method is to solve this question that can be directed by using the formula, \[\text{Mean}=\dfrac{\sum{{{f}_{i}}{{x}_{i}}}}{\sum{{{f}_{i}}}}\] and without assuming any mean although assuming a mean and solving gives a more precise value.
Complete step by step answer:
We have to find the mean using the shortcut method. It can be given as stated and explained below. If \[{{x}_{1}},{{x}_{2}},...{{x}_{n}}\] are the observations given with respective frequencies \[{{f}_{1}},{{f}_{2}},....{{f}_{n}}.\] Let the deviation A take at any point, we have, \[{{d}_{i}}={{x}_{i}}-A\] where i = 1, 2, …n. So, we can say, \[{{f}_{i}}{{d}_{i}}={{f}_{i}}\left( {{x}_{i}}-A \right)\] where i = 1, 2, … n. Then the mean \[\overline{x}\] is given by the formula,
\[\overline{x}=\dfrac{\sum{{{x}_{i}}{{f}_{i}}}}{\sum{{{f}_{i}}}}\]
We have our data as,
| Class Interval | Frequency |
| 0 – 10 | 4 |
| 10 – 20 | 8 |
| 20 – 30 | 13 |
| 30 – 40 | 20 |
| 40 – 50 | 12 |
| 50 – 60 | 8 |
Let us assume A = 13, then we will calculate \[{{d}_{i}}={{x}_{i}}-A,\] i = 1, 2, …6 and \[{{f}_{i}}{{d}_{i}}.\] But before doing so, we need \[{{x}_{i}},i=1,2,.....6\] as here class interval is given as \[{{x}_{i}}=\text{mid point of class interval}\text{.}\] This gives our new table as
| Class Interval | \[{{x}_{i}}\] | \[{{f}_{i}}\] | \[{{d}_{i}}={{x}_{i}}-A\] | \[{{f}_{i}}{{d}_{i}}\] |
| 0 – 10 | 5 | 4 | – 20 | – 80 |
| 10 – 20 | 15 | 8 | – 10 | – 80 |
| 20 – 30 | 25 = A | 13 | 0 | 0 |
| 30 – 40 | 35 | 20 | 10 | 200 |
| 40 – 50 | 45 | 12 | 20 | 240 |
| 50 – 60 | 55 | 8 | 30 | 240 |
| N = 65 | 520 |
Finally, we will use the mean formula given as
\[\text{Mean}=\overline{x}=A+\left[ \dfrac{1}{N}\sum{{{f}_{i}}{{d}_{i}}} \right]\]
\[\Rightarrow \text{Mean}=\overline{x}=25+\dfrac{520}{65}\]
\[\Rightarrow \text{Mean}=\overline{x}=25+8\]
\[\Rightarrow \text{Mean}=\overline{x}=33\]
Hence, the mean of the given data is \[\overline{x}=33.\]
So, the correct answer is “Option D”.
Note: Another method is to solve this question that can be directed by using the formula, \[\text{Mean}=\dfrac{\sum{{{f}_{i}}{{x}_{i}}}}{\sum{{{f}_{i}}}}\] and without assuming any mean although assuming a mean and solving gives a more precise value.
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