Write a Linear Model For this Data

In summary, the average weight of a male child's brain increases by 150 grams per year according to a linear model, with an initial weight of 970 grams at age 1 and a final weight of 1270 grams at age 3. The slope of the linear model represents the change in brain weight over time, with units of grams per year.
  • #1
nycmathguy
Homework Statement
Write a linear model for the given data.
Relevant Equations
y = mx + b
The average weight of a male child’s
brain is 970 grams at age 1 and 1270 grams at age 3. (Source: American Neurological Association)

(a) Assuming that the relationship between brain weight y and age t is linear, write a linear model for the data.

(b) What is the slope and what does it tell you about brain weight?

For (a), I see 2 points in the form (t, y) = (age, weight). The two points are (1, 970) and (3, 1270).

The general form is y = mt + b.

Let m = slope

m = (1270 - 970)/(3 - 1)

m = 300/2

m = 150

I think the point-slope formula needed now.

y - 970 = 150(t - 1)

y - 970 = 150t - 150

y = 150t - 150 + 970

y = 150t + 820

You say?

For (b), the slope is 150. I don't know what the slope tells me about weight gain.

Do you know?
 
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  • #2
Think about how much the brain weight is increasing every year that passes. (according to the linear model).
 
  • #3
Delta2 said:
Think about how much the brain weight is increasing every year that passes. (according to the linear model).
The brain weight increases 150 grams per age.

Yes?
 
  • #4
what exactly you mean per age? You mean per year right?
 
  • #5
nycmathguy said:
y = 150t + 820

You say?
Can't you check this for yourself? If t = 1, what is y? If t = 3, what is y? Do these y values match the data given in the problem? If so, your equation is correct and you don't need us to confirm your solution.
nycmathguy said:
Homework Statement:: Write a linear model for the given data.
Relevant Equations:: y = mx + b

For (b), the slope is 150. I don't know what the slope tells me about weight gain.
The slope is the rise over the run, which in this case is the change in brain weight (in grams) over the change in time (in years). The units of the slope here are ##\frac{\text{grams}}{\text{years}}##, or grams per year.
 
  • #6
I am moving on. Sorry but I am tired of the belittling. I have not learned anything here. Sorry that I don't recall material learned 28 years ago. I am done! Good night.
 
  • Wow
Likes Delta2
  • #7
Delta2,

You are welcomed to join me in my new precalculus FB group.
 

FAQ: Write a Linear Model For this Data

What is a linear model?

A linear model is a mathematical representation of a relationship between two or more variables that can be described by a straight line. It is commonly used in statistics and data analysis to make predictions and draw conclusions about the data.

How do you write a linear model for a set of data?

To write a linear model for a set of data, you need to determine the equation of the line that best fits the data points. This can be done by calculating the slope and y-intercept of the line using the formula y = mx + b, where m is the slope and b is the y-intercept. Once you have these values, you can write the linear model in the form of y = mx + b.

What is the purpose of writing a linear model for data?

The purpose of writing a linear model for data is to summarize and describe the relationship between two or more variables in a simple and understandable way. It allows us to make predictions and draw conclusions about the data, which can be useful in various fields such as science, economics, and engineering.

What are some assumptions of a linear model?

Some assumptions of a linear model include that the relationship between the variables is linear, the data is normally distributed, and the error terms are independent and have equal variance. Additionally, the data should not have any outliers or influential data points that could affect the accuracy of the model.

Can a linear model be used for any type of data?

No, a linear model is only suitable for data that has a linear relationship between the variables. If the relationship is not linear, a different type of model, such as a quadratic or exponential model, should be used. It is important to assess the data and choose the appropriate model for the best results.

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