Adataset "women" in the datasets package in r gives the average heights (inches) and weights (pounds) for american women aged 30-39. use the dataset to answer the following questions.
fit a simple linear model weight=β0+β1*height 98jdqkvjtbajnlcvj5pzsla0ghcmvsiml2c jsffdto predict the weight using height and report the fitted line.
fit a polynomial model weight=β0+β1*height+β2*height2 velftksuqmccto predict the weight and report the fitted line.
draw a scatter plot of height and weight. add the fitted lines from problem 1 and 2, respectively.

Step-by-step explanation:

Suppose the dataset "women" has 2 numeric columns namely "heights" and "weights".

By using function lm(formula, data) we can calculate the weight for the linear model

linmod = lm(weights ~ heights, data = women)

summary(linmod)

Similarly for the polynomial model

polymod = lm(women$weights ~ women$heights + I(women$heights^2))

summary(polymod)

To plot the fitted lines, we would need to calculate their y-values

plot(women$heights, women$weights, pch=16, col='black)

lines(women$heights, predict(linmod), col='blue')

lines(women$heights, predict(polymod), col='red')

slope is 35 and 20 miles is the y-intercept.

step-by-step explanation:

assuming this is from a math graphing problem.

7 would be on the x-axis which is the horizontal line on the graph

84 would be on the y-axis which is the vertical line on the graph