The deer population in Maine from 1986 to 2006 is...

Name: Problem 37, Chapter 0: Calculus and Its Applications
Uploaded: 2021-05-25T23:35:52-08:00
Duration: 1 min 48 s
Channel: Carson Merrill
Description: The deer population in Maine from 1986 to 2006 is approximated in the graph below. a) In what years was the deer population in Maine at or above 250,000 ? b) In what years was the deer population at 200,000 ? c) In what year was the deer population highest? d) In what years was the deer population lowest?

The deer population in Maine from 1986 to 2006 is approximated in the graph below. a) In what years was the deer population in Maine at or above 250,000 ? b) In what years was the deer population at 200,000 ? c) In what year was the deer population highest? d) In what years was the deer population lowest?

The deer population in Maine from 1986 to 2006 is approximated in the graph below.
a) In what years was the deer population in Maine at or above 250,000 ?
b) In what years was the deer population at 200,000 ?
c) In what year was the deer population highest?
d) In what years was the deer population lowest?

Key Concepts

Extreme Value Analysis

This concept focuses on identifying the highest and lowest values of a function within a given interval. Analyzing extreme values is fundamental in understanding the behavior of datasets, optimizing results, and making informed decisions based on the maximum or minimum outcomes depicted in the graph.

Intersection Identification

This concept is about finding the precise points where a graph intersects a horizontal line representing a specific value. This method is often used to determine when a function attains a particular value, making it essential in solving equations represented graphically.

Threshold Analysis

This concept refers to determining the points on a graph where the function's value exceeds, meets, or falls below a defined level. It is key to identifying intervals or specific points when the data is above or below a certain threshold, which is critical in many applied problems.

Graphical Data Interpretation

This concept involves understanding and extracting quantitative information from visual representations such as graphs. It includes recognizing how data points are plotted over time or another variable and drawing conclusions from trends, patterns, and specific values illustrated in the graph.

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