1. Problem statement: Represent the given data on a graph. 2. Note: you did not supply data, so I will illustrate the steps with an example dataset and show all intermediate work. 3. Example dataset: the x-values are 1, 2, 3, 4, 5 and the y-values are 2, 3, 5, 4, 6. 4. Choose plot type: use a scatter plot to show the data points and draw the best-fit (least-squares) line to represent the trend. 5. Compute basic sums needed for the least-squares line. The number of points is $n=5$. The sums are $\sum x=1+2+3+4+5=15$. The sums are $\sum y=2+3+5+4+6=20$. The sums are $\sum x^2=1^2+2^2+3^2+4^2+5^2=55$. The sums are $\sum xy=1\cdot2+2\cdot3+3\cdot5+4\cdot4+5\cdot6=69$. 6. Use the least-squares formulas to find slope $m$ and intercept $b$. The slope formula is $$m=\frac{n\sum xy-\sum x\sum y}{n\sum x^2-(\sum x)^2}$$ Substitute numbers and compute the slope. $$m=\frac{5\cdot 69-15\cdot 20}{5\cdot 55-15^2}=\frac{345-300}{275-225}=\frac{45}{50}=0.9$$ Now compute the intercept using $$b=\frac{\sum y-m\sum x}{n}$$ Substitute numbers and compute the intercept. $$b=\frac{20-0.9\cdot 15}{5}=\frac{20-13.5}{5}=\frac{6.5}{5}=1.3$$ 7. Write the final best-fit line equation and interpret it. The regression line is $$y=0.9x+1.3$$ This means for each increase of 1 in $x$ the predicted $y$ increases by 0.9, and the predicted value at $x=0$ is 1.3. 8. How to represent the data on a graph step-by-step. a. Draw orthogonal axes and label the horizontal axis $x$ and vertical axis $y$. b. Mark a suitable scale that includes the range of your data for both axes. c. Plot each data point from the example: (1,2), (2,3), (3,5), (4,4), (5,6). d. Draw the best-fit line $y=0.9x+1.3$ across the plot so it visually summarizes the trend. e. Optionally show the line as a solid line and points as markers, and indicate intercept and a couple of predicted values, e.g., predicted $y$ at $x=6$ is $y=0.9\cdot 6+1.3=6.7$. 9. Final answer: plot the scatter of the points (1,2), (2,3), (3,5), (4,4), (5,6) and draw the best-fit line $$y=0.9x+1.3$$ to represent the data on the graph.