A study of nutrition in developing countries collected data from the Egyptian village of Nahya. Researchers recorded the mean weight (in kilograms) for 170 infants in Nahya each month during their first year of life. A hasty user of statistics enters the data into software and computes the least-squares li~thout looking at the scatterplot first. The result is weight= 4.88 + 0.267 (age). Required:
Use the residual plot to determine if this linear model is appropriate.

A residual plot for the nutrition data would display the residuals on the y-axis and the age on the x-axis.

The residual plot is not appropriate for a linear model.

From the residual plot (see attachment), we can see that the points on the residual plot follows a curved pattern.

For a residual plot to be a linear model, the points on the plot must be at random on the horizontal axis of the plot.

This means that, the residual plot is not appropriate for a linear model.

Read more about residual plots at:

link

The linear model is not appropriate.

Step-by-step explanation:

In regression, the difference amid the observed-value of the dependent-variable (y) and the predicted-value () is known as the residual (e).

A residual plot is a graphical representation of the residuals on the y-axis and the independent variable on the x-axis. If the data-points on the residual plot are randomly spread around the x-axis, a linear regression model is appropriate for the data. And if the residual plots shows a non-random or a U or inverted U pattern, a nonlinear model is more appropriate for the data.

The residual provided shown an inverted U pattern. That is the points are not scattered around he graph.

Thus, the linear model is not appropriate.

the correct answer is b. negative exponential growth.

step-by-step explanation:

you can tell it's an exponential function because it decreases rapidly then levels out and never touches the x-axis.

it is negative because the y-axis decreases as the x-axis increases.

therefore it is a negative exponential growth graph.

hope i !

sorry

step-by-step explanation:

sorry, but im pretty sure its a now that i look at it