An unbiased coin is tossed $n$ times. The probability of getting at least one tail is $p$ and the probability of at least two tails is $q$ and $p-q = \frac{5}{32}$.
The problem involves calculating probabilities related to tossing an unbiased coin \(n\) times. We are given definitions for two probabilities:
To find \(p\), we consider the complementary event: getting no tails at all, which means getting all heads. Since the coin is unbiased, the probability of getting heads on a single toss is \(\frac{1}{2}\). For \(n\) tosses, the probability of getting all heads is \((\frac{1}{2})^n\).
Therefore, the probability of getting at least one tail, \(p\), is:
\(p = 1 - P(\text{all heads}) = 1 - (\frac{1}{2})^n\)
To find \(q\), the probability of getting at least two tails, we consider the complementary event: getting zero tails or exactly one tail.
The probability of getting zero tails (\(P(T_0)\)) is the same as getting all heads, which is \((\frac{1}{2})^n\).
The probability of getting exactly one tail (\(P(T_1)\)) can be calculated using the binomial probability formula. There are \(\binom{n}{1}\) ways to choose the position for the single tail, and the probability for each specific sequence (like T H H...) is \((\frac{1}{2})^n\).
\(P(T_1) = \binom{n}{1} (\frac{1}{2})^n = n (\frac{1}{2})^n\)
So, the probability \(q\) is:
\(q = 1 - [P(\text{zero tails}) + P(\text{one tail})]\) \(q = 1 - [(\frac{1}{2})^n + n (\frac{1}{2})^n]\)
Factoring out \((\frac{1}{2})^n\):
\(q = 1 - (\frac{1}{2})^n (1+n)\)
We are given the equation \(p - q = \frac{5}{32}\). Substitute the derived expressions for \(p\) and \(q\):
\([1 - (\frac{1}{2})^n] - [1 - (\frac{1}{2})^n (1+n)] = \frac{5}{32}\)
Now, simplify the equation by removing the brackets and combining terms:
\(1 - (\frac{1}{2})^n - 1 + (\frac{1}{2})^n (1+n) = \frac{5}{32}\)
The '1's cancel out:
\((\frac{1}{2})^n (1+n) - (\frac{1}{2})^n = \frac{5}{32}\)
Factor out the common term \((\frac{1}{2})^n\):
\((\frac{1}{2})^n [(1+n) - 1] = \frac{5}{32}\)
Simplify the expression inside the brackets:
\((\frac{1}{2})^n [n] = \frac{5}{32}\)
This can be written as:
\(\frac{n}{2^n} = \frac{5}{32}\)
To find the value of \(n\), we can test small positive integer values since \(n\) represents the number of tosses:
So, the number of coin tosses is \(n=5\).
With \(n=5\), we can now calculate the specific values for \(p\) and \(q\):
\(p = 1 - (\frac{1}{2})^5 = 1 - \frac{1}{32} = \frac{31}{32}\)
\(q = 1 - (\frac{1}{2})^5 (1+5) = 1 - (\frac{1}{32})(6) = 1 - \frac{6}{32} = \frac{26}{32} = \frac{13}{16}\)
The question asks for the value of \(p + q\).
\(p + q = \frac{31}{32} + \frac{13}{16}\)
To add these fractions, we need a common denominator, which is 32. Convert \(\frac{13}{16}\) to an equivalent fraction with a denominator of 32:
\(\frac{13}{16} = \frac{13 \times 2}{16 \times 2} = \frac{26}{32}\)
Now, add the fractions:
\(p + q = \frac{31}{32} + \frac{26}{32} = \frac{31 + 26}{32}\)
\(p + q = \frac{57}{32}\)
Therefore, the value of \(p + q\) is \(\frac{57}{32}\).
What is the median height of the class?
What is the total number of students whose height is less than or equal to 165 cm?
The height which occurs most frequently in the class is
| LIST-I | LIST-II | |
| A. Sampling error | I. | Values that describe the characteristics of a population |
| B. Parameters | II. | The standard deviation of the sampling distribution |
| C. Sampling fraction | III. | Variation among sample statistics due to change |
| D. Standard error | IV. | Proportion of the population contained in a sample |
Given below are two statements one is labelled as Assertion (A) and the other is labelled as
Reason (R).
Assertion (A): Additive model considers the total of various components resulting in the given
values of the over all time series and it is stated as:
$Y=T+C+S+I$
Reason (R): Multiplicative model assumes that the various components interact in a
multiplicative manner to produce the given values of the over all time series and
can be stated as $Y=T \times C \times S \times I$
In the light of the above statements, choose the most appropriate answer from the options given
below:
Match List - I with List - II.
| List - I | List - II |
| A. Sample proportion P = x/n | I. $\frac{\sigma}{\sqrt{n}}$ |
| B. Sample variance $S_1^2 = \frac{1}{n-1} \sum_{i=1}^{n}(xi-x̄)^2$ | II. $\sigma^2 \sqrt{\frac{2}{n}}$ |
| C. Sample variance $S^2 = \frac{1}{n} \sum_{i=1}^{n}(xi-x̄)^2$ | III. $\sigma^2 \sqrt{\frac{2}{(n-1)}}$ |
| D. Sample mean $\bar{x} = \frac{1}{n} \sum_{i=1}^{n}xi$ | IV. $\sqrt{\frac{\pi(1-\pi)}{n}}$ |
Choose the correct answer from the options given below :