The following gives the number of accidents that occurred on Florida State Highway 101 during the last 4 months: Month Mar Feb 40 Jan Apr Number of Accidents 30 60 95 Using the least-squares regression method, the trend equation for forecasting is (round your responses to two decimal places):

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**Instructor Tip**

For months Jan, Feb, Mar, Apr, and May, use 1, 2, 3, 4, and 5 respectively.
Transcribed Image Text:**Instructor Tip** For months Jan, Feb, Mar, Apr, and May, use 1, 2, 3, 4, and 5 respectively.
The following gives the number of accidents that occurred on Florida State Highway 101 during the last 4 months:

| Month              | Jan | Feb | Mar | Apr |
|--------------------|-----|-----|-----|-----|
| Number of Accidents| 30  | 40  | 60  | 95  |

Using the [least-squares regression](#) method, the trend equation for forecasting is (round your responses to two decimal places):

\[
\hat{y} = \Box + \Box x
\]

### Definition

This approach results in a straight line that minimizes the sum of the squares of the vertical differences or deviations from the line to each of the actual observations.

A least-squares line is described in terms of its y-intercept (the height at which it intercepts the y-axis) and its expected change (slope).
Transcribed Image Text:The following gives the number of accidents that occurred on Florida State Highway 101 during the last 4 months: | Month | Jan | Feb | Mar | Apr | |--------------------|-----|-----|-----|-----| | Number of Accidents| 30 | 40 | 60 | 95 | Using the [least-squares regression](#) method, the trend equation for forecasting is (round your responses to two decimal places): \[ \hat{y} = \Box + \Box x \] ### Definition This approach results in a straight line that minimizes the sum of the squares of the vertical differences or deviations from the line to each of the actual observations. A least-squares line is described in terms of its y-intercept (the height at which it intercepts the y-axis) and its expected change (slope).
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