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Justify the conclusion made about power regression on page$299 .$

The conclusion model about power regression is $\left[y=c\left(x^{a}\right)\right.$Where $c=e^{b}$ the power regression model for the data $(x, y)$

Algebra

Chapter 3

Exponential, Logistic, and Logarithmic Functions

Section 5

Equation Solving and Modeling

Functions

Exponential and Logarithmic Functions

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Lectures

01:32

In mathematics, the absolu…

01:11

03:18

(a) Compute the power regr…

07:01

01:43

What are the assumptions f…

01:18

01:04

State the hypotheses used …

01:14

What are some of the assum…

01:31

Use the regression feature…

01:30

Writing Explain how a bino…

01:26

What is regression analysi…

03:57

okay for part a of this question, we're gonna be using a graft to graft this function. We can notice that it will look like us. Okay. And then use the regression model on your calculator to obtain why equals 8.9 to 35 Axe to the negative. 0.11228 to five. So this is part A, and we got this using the calculator. Okay, part be, we're gonna be using this. The model we just found to predict the value for X equals 9.2. So 8.9 to 35 times 9.2 to the negative. 0.1128 three fought to 3 8 to 5 equals approximately 6.3 onto part. See, we see that for each point, X comma y you can take the natural log to find the corresponding point. So I'm gonna make a chart over here. A little table, naturally. Axe, unnatural of Of why? Okay. 0.69 Approximately 2.1 approximately. And then we have one point 09 and 1.97 1.557 0.92 1.8 sex. You can see that we could make a scatter part of the data and we'll notice that they seem toe look like they're in a line. 34 If I were to connect these draw dotted line to show, you could see how the points look like their end a line approximately, which is what we're ending. Toe obtain. Okay, now we're onto Part D. That's a four part problem. We're using a linear regression model based on the data points that we got in part. See? And we can obtain a graph that looks something like Thus, we have four points. One, 223 four And the model. You should obtain your calculation. Be natural. Log of why equals negatives. Your 0.113121 natural of ax plus 2.9 131 This should be the linear regression model that we obtained

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