The data show the bug chirps per minute at different temperatures. Find the regression equation, letting the first variable be the independent (x) variable. Find the best predicted temperature for a time when a bug is chirping at the rate of 3000 chirps per minute. Use a significance level of 0.05. What is wrong with this predicted value? Chirps in 1 min: 751 805 800 959 1053 1078 Temperature (°F): 71.1 72.8 66.1 80.9 78.6 84.8 What is the regression equation? y=_?_+_?_ x (Round the x-coefficient to four decimal places as needed. Round the constant to two decimal places as needed.) The best predicted temperature when a bug is chirping at 3000 chirps per minute is ??? Round to one decimal place as needed.)
Minimization
In mathematics, traditional optimization problems are typically expressed in terms of minimization. When we talk about minimizing or maximizing a function, we refer to the maximum and minimum possible values of that function. This can be expressed in terms of global or local range. The definition of minimization in the thesaurus is the process of reducing something to a small amount, value, or position. Minimization (noun) is an instance of belittling or disparagement.
Maxima and Minima
The extreme points of a function are the maximum and the minimum points of the function. A maximum is attained when the function takes the maximum value and a minimum is attained when the function takes the minimum value.
Derivatives
A derivative means a change. Geometrically it can be represented as a line with some steepness. Imagine climbing a mountain which is very steep and 500 meters high. Is it easier to climb? Definitely not! Suppose walking on the road for 500 meters. Which one would be easier? Walking on the road would be much easier than climbing a mountain.
Concavity
In calculus, concavity is a descriptor of mathematics that tells about the shape of the graph. It is the parameter that helps to estimate the maximum and minimum value of any of the functions and the concave nature using the graphical method. We use the first derivative test and second derivative test to understand the concave behavior of the function.
The data show the bug chirps per minute at different temperatures. Find the regression equation, letting the first variable be the independent (x) variable. Find the best predicted temperature for a time when a bug is chirping at the rate of 3000 chirps per minute. Use a significance level of 0.05. What is wrong with this predicted value?
Chirps in 1 min: 751 805 800 959 1053 1078
Temperature (°F): 71.1 72.8 66.1 80.9 78.6 84.8
What is the regression equation? y=_?_+_?_ x (Round the x-coefficient to four decimal places as needed. Round the constant to two decimal places as needed.)
The best predicted temperature when a bug is chirping at 3000 chirps per minute is ??? Round to one decimal place as needed.)
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