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):
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):
Practical Management Science
6th Edition
ISBN:9781337406659
Author:WINSTON, Wayne L.
Publisher:WINSTON, Wayne L.
Chapter2: Introduction To Spreadsheet Modeling
Section: Chapter Questions
Problem 20P: Julie James is opening a lemonade stand. She believes the fixed cost per week of running the stand...
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Transcribed Image Text:**Instructor Tip**
For months Jan, Feb, Mar, Apr, and May, use 1, 2, 3, 4, and 5 respectively.
 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).](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F486f0abb-f9b2-484f-831d-c29988945553%2F1430f8c6-0a99-444f-8b51-9bf970b46a3c%2F6sa8fib_processed.png&w=3840&q=75)
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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