Questions number 19 and 20 are Assertion and Reason-based questions. Two statements are given, one labelled Assertion (A) and the other labelled Reason (R). Select the correct answer from the codes (A), (B), (C), and (D) as given below.
(A) Both Assertion (A) and Reason (R) are true, and Reason (R) is the correct explanation of the Assertion (A).
(B) Both Assertion (A) and Reason (R) are true, but Reason (R) is not the correct explanation of the Assertion (A).
(C) Assertion (A) is true, but Reason (R) is false.
(D) Assertion (A) is false, but Reason (R) is true.
Step 1: Compute the derivative of \( f(x) = x^2 - x + 1 \): \[ f'(x) = 2x - 1. \]
Step 2: Analyze the sign of \( f'(x) \) on \((-1, 1)\): - At \( x = \frac{1}{2} \), \( f'(x) = 0 \). - For \( x<\frac{1}{2} \), \( f'(x)<0 \), meaning \( f(x) \) is decreasing. - For \( x>\frac{1}{2} \), \( f'(x)>0 \), meaning \( f(x) \) is increasing.
Step 3: Since \( f(x) \) is not strictly increasing throughout \((-1,1)\), Assertion (A) is false.
Step 4: Reason (R) states a correct mathematical theorem, so it is true. Thus, the correct answer is that Assertion (A) is false, but Reason (R) is true.
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).
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:
\[ \begin{array}{|c|c|c|c|c|c|c|} \hline \textbf{Year} & 1996 & 1997 & 1998 & 1999 & 2000 & 2001 \\ \hline \textbf{Sales (in lakh ₹)} & 6.5 & 5.3 & 4.3 & 6.1 & 5.6 & 7.8 \\ \hline \end{array} \]Using flat rate method, the EMI to repay a loan of ₹ 20,000 in \( 2 \frac{1}{2} \) years at an interest rate of 8% p.a. is:
Fit a straight-line trend by the method of least squares for the following data:
\[ \begin{array}{|c|c|c|c|c|c|c|c|} \hline \textbf{Year} & 2004 & 2005 & 2006 & 2007 & 2008 & 2009 & 2010 \\ \hline \textbf{Profit (₹ 000)} & 114 & 130 & 126 & 144 & 138 & 156 & 164 \\ \hline \end{array} \]