For \( R_2 \), the region includes \( x + 2y \geq 100 \), \( x \geq 0 \), and \( y \geq 0 \), but excludes the constraints forming the region \( R_1 \). Based on the graph, the constraints are derived as: - \( x + 2y \geq 100 \), - \( y - \frac{4}{3}x + \frac{80}{3} \leq 0 \).
Final Answer: The constraints for \( R_2 \) are: \[ x + 2y \geq 100 \quad {and} \quad y - \frac{4}{3}x + \frac{80}{3} \leq 0. \]
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:
\[ \begin{array}{|c|c|c|c|c|c|c|} \hline \textbf{Year} & 1996 & 1997 & 1998 & 1999 & 2000 & 2001 \\ \hline \textbf{Sales (in lakh ₹)} & 6.5 & 5.3 & 4.3 & 6.1 & 5.6 & 7.8 \\ \hline \end{array} \]