Question

Use the graph to answer the question.

The city of Harmonville created a scatter plot to illustrate the number of robberies that have been
reported there over the past several years.
Top of graph
Reported Robberies Per Year
Left side of graph:

# of Robberies

1,800

1,600

1,400

1,200

1,000

800

600

400

200

Bottom of graph:
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
Year

Use linear regression to predict the number of reported robberies that occurred during 2006.
answers:
1,000

1,200

1,400

1,600

Answer 1

To predict the number of reported robberies in Harmonville for the year 2006 using linear regression, we would typically need to perform a few steps:

1. **Data Collection**: Identify the (x, y) pairs from the graph, where x represents the years (1996-2007) and y represents the number of reported robberies for each corresponding year.

2. **Linear Regression Calculation**: Use the collected data points to perform linear regression, which would determine the best-fit line through these points.

3. **Prediction**: Use the equation of the line (often written in the form \( y = mx + b \), where \( m \) is the slope and \( b \) is the y-intercept) to predict the value of y (reporting robberies) for x = 2006.

Since we don't have the specific data points from the scatter plot, I can’t calculate the exact value. However, based on your provided options (1,000; 1,200; 1,400; 1,600), you could analyze the trend in robberies from 1996 to 2005 and see which of these numbers seems to fit the pattern of the previous years.

If you remember the trend (e.g., if the number of robberies has been increasing, decreasing, or staying constant) or if you have specific values for each year, you can make a good guess based on that trend.

If the graph shows a steady increase leading into 2006, it might make sense to predict a value closer to the higher numbers (1,400; 1,600). If it was more stable, then lower options like 1,000 or 1,200 could be reasonable.

If you have more details from the graph, I can help further refine the prediction!