Step 1: Equate the given matrices element by element: \[ x - y = -1, \quad 2x + z = 5, \quad 2x - y = 0, \quad 3z + w = 13. \]
Step 2: Solve for \( x \) and \( y \) from \( x - y = -1 \) and \( 2x - y = 0 \): From \( 2x - y = 0 \), we get \( y = 2x \). Substitute \( y = 2x \) into \( x - y = -1 \): \[ x - 2x = -1 \quad \Rightarrow \quad -x = -1 \quad \Rightarrow \quad x = 1. \] Then \( y = 2(1) = 2 \).
Step 3: Solve for \( z \) from \( 2x + z = 5 \): \[ 2(1) + z = 5 \quad \Rightarrow \quad z = 3. \]
Step 4: Solve for \( w \) from \( 3z + w = 13 \): \[ 3(3) + w = 13 \quad \Rightarrow \quad 9 + w = 13 \quad \Rightarrow \quad w = 4. \] Thus, \( x = 1, y = 2, z = 3, w = 4 \).
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:
\[ \begin{array}{|c|c|c|c|c|c|c|} \hline \textbf{Year} & 1996 & 1997 & 1998 & 1999 & 2000 & 2001 \\ \hline \textbf{Sales (in lakh ₹)} & 6.5 & 5.3 & 4.3 & 6.1 & 5.6 & 7.8 \\ \hline \end{array} \]