Step 1: The Poisson distribution formula is: \[ P(X = k) = \frac{\lambda^k e^{-\lambda}}{k!}, \] where \( \lambda \) is the average rate of occurrence, \( k \) is the number of occurrences, and \( e \) is the base of the natural logarithm.
Step 2: Mean expectation (\( \lambda \)): The average number of floods in 10 years is \( \lambda = 2 \).
Step 3: Probability of 3 or fewer overflows (\( P(X \leq 3) \)): \[ P(X \leq 3) = P(X = 0) + P(X = 1) + P(X = 2) + P(X = 3). \] Using the Poisson formula: - For \( P(X = 0) \): \[ P(X = 0) = \frac{2^0 e^{-2}}{0!} = \frac{1 \cdot 0.13534}{1} = 0.13534. \] - For \( P(X = 1) \): \[ P(X = 1) = \frac{2^1 e^{-2}}{1!} = \frac{2 \cdot 0.13534}{1} = 0.27068. \] - For \( P(X = 2) \): \[ P(X = 2) = \frac{2^2 e^{-2}}{2!} = \frac{4 \cdot 0.13534}{2} = 0.27068. \] - For \( P(X = 3) \): \[ P(X = 3) = \frac{2^3 e^{-2}}{3!} = \frac{8 \cdot 0.13534}{6} = 0.18045. \]
Step 4: Add the probabilities: \[ P(X \leq 3) = P(X = 0) + P(X = 1) + P(X = 2) + P(X = 3). \] Substitute the values: \[ P(X \leq 3) = 0.13534 + 0.27068 + 0.27068 + 0.18045 = 0.85715. \]
Final Answers: - Mean expectation: \( \lambda = 2 \). - Probability of 3 or fewer overflows: \( P(X \leq 3) = 0.85715 \) or approximately \( 85.72\% \).
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:
\[ \begin{array}{|c|c|c|c|c|c|c|} \hline \textbf{Year} & 1996 & 1997 & 1998 & 1999 & 2000 & 2001 \\ \hline \textbf{Sales (in lakh ₹)} & 6.5 & 5.3 & 4.3 & 6.1 & 5.6 & 7.8 \\ \hline \end{array} \]