Step 1: For a binomial distribution, the mean is given by: \[ \mu = np = 200 \times 0.04 = 8. \] Since Poisson approximation is used, the mean of the Poisson distribution is also 8. Thus, Assertion (A) is true.
Step 2: The probability mass function of a Poisson distribution is: \[ P(X = k) = \frac{e^{-\mu} \cdot \mu^k}{k!}. \] Substituting \( \mu = 8 \) and \( k = 4 \): \[ P(X = 4) = \frac{e^{-8} \cdot 8^4}{4!} = \frac{512}{3e^8}. \] Since the given expression matches this calculation, Reason (R) is also true.
Step 3: However, Reason (R) does not directly explain why the mean of the Poisson distribution is 8. The mean of a Poisson distribution is derived from the binomial approximation (\( \lambda = np \)), not from the probability calculation of \( P(X = 4) \). Thus, Assertion (A) and Reason (R) are both true, but Reason (R) is not the correct explanation of Assertion (A).
Fit a straight-line trend by the method of least squares for the following data:
\[ \begin{array}{|c|c|c|c|c|c|c|c|} \hline \textbf{Year} & 2004 & 2005 & 2006 & 2007 & 2008 & 2009 & 2010 \\ \hline \textbf{Profit (₹ 000)} & 114 & 130 & 126 & 144 & 138 & 156 & 164 \\ \hline \end{array} \]When observed over a long period of time, a time series data can predict trends that can forecast increase, decrease, or stagnation of a variable under consideration. The table below shows the sale of an item in a district during 1996–2001:
\[ \begin{array}{|c|c|c|c|c|c|c|} \hline \textbf{Year} & 1996 & 1997 & 1998 & 1999 & 2000 & 2001 \\ \hline \textbf{Sales (in lakh ₹)} & 6.5 & 5.3 & 4.3 & 6.1 & 5.6 & 7.8 \\ \hline \end{array} \]