To help you with this, I'll outline the steps for creating a scatter plot and finding the line of best fit using the given data. Here's how to approach it:

Given Data:

• Years: 2005, 2007, 2009, 2011, 2013, 2015, 2017
• Time (minutes): 169, 175, 175, 176, 184, 180, 189

---

Steps to Create the Scatter Plot and Line of Best Fit:

1. Plot the Data Points:

   • Each point will represent a year and its corresponding game length (e.g., for 2005, the point is (2005, 169)).

2. Draw the Line of Best Fit:

   • Use a linear regression formula or calculator to determine the line that best fits the data.
   • The line of best fit typically has the form $y = mx + b$, where:
     • $m$ is the slope.
     • $b$ is the y-intercept.

3. Calculate the Slope ($m$) and Intercept ($b$):

   • You can calculate the slope and intercept manually or using software such as Excel, Python, or a graphing calculator.

---

Calculation Example:

Let's calculate the line of best fit mathematically:

1. Mean of x-values (years): $\bar{x} = \frac{2005 + 2007 + 2009 + 2011 + 2013 + 2015 + 2017}{7} = \frac{14077}{7} = 2011$

2. Mean of y-values (time): $\bar{y} = \frac{169 + 175 + 175 + 176 + 184 + 180 + 189}{7} = \frac{1248}{7} = 178.29$

3. Calculate the slope ($m$) using the formula: $m = \frac{\sum (x_i - \bar{x})(y_i - \bar{y})}{\sum (x_i - \bar{x})^2}$

4. Determine the intercept ($b$): $b = \bar{y} - m\bar{x}$

---

Graphing Tools:

You can use tools like Desmos, Excel, or even Python (Matplotlib) to input this data and generate the scatter plot with the line of best fit. If you'd like, I can walk you through the code for plotting in Python or explain further how to do it in Excel.

---

Would you like a more detailed calculation or help with a specific graphing tool?

Related Questions:

1. What factors might have contributed to the increase in game duration over the years?
2. How do you calculate residuals for a