Step 1: Null hypothesis \( H_0 \): The advertising campaign was not successful, i.e., \( \mu = 50 \).
Step 2: Alternative hypothesis \( H_1 \): The advertising campaign was successful, i.e., \( \mu>50 \).
Step 3: Compute the \( t \)-statistic: \[ t = \frac{\bar{x} - \mu}{s / \sqrt{n}} = \frac{55 - 50}{10 / \sqrt{20}} = \frac{5}{10 / 4.47} = \frac{5 \cdot 4.47}{10} = 2.235. \]
Step 4: Compare \( t \)-statistic with \( t_{19}(0.05) \): Since \( t = 2.235>t_{19}(0.05) = 1.729 \), we reject \( H_0 \).
Step 5: Conclusion: The advertising campaign was successful.
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:
\[ \begin{array}{|c|c|c|c|c|c|c|} \hline \textbf{Year} & 1996 & 1997 & 1998 & 1999 & 2000 & 2001 \\ \hline \textbf{Sales (in lakh ₹)} & 6.5 & 5.3 & 4.3 & 6.1 & 5.6 & 7.8 \\ \hline \end{array} \]