Please see the attached images for data and scatter plots. Also, help with the correct formula and calculations for the questions 'm, n, o, p' would very appreciative.

Y = ?o + ?1(X1)

How do you interpret multivariate regression coefficients? We decide we think age may also explain some of the variability in energy expenditure per week.

Question =

m. When age is added to the prediction equation, we get the following:

Energy Expenditure (kcals/week) = -1100 + 909 (number of bouts/week) + -5(age)

Write out below how each of the following numbers could be interpreted?:

-1100:

909:

-5:

n. Use this prediction equation to predict the number of bouts per week for 1) someone who gets 3 bouts per week and it 22 years old; and someone who gets 5 bouts per week and is 35 years old?

o. The R2 value for the univariate regression equation (bouts with energy expenditure) is the same as that for the simple Pearson's correlation (R2= 45%). When we add age to the model, the R2 increases to 51%. Remember R2 is interpreted as the amount of variability explained by the independent variable(s). Discuss whether adding age to the model (multivariate model with bouts/week and age as a covariate and R2=51%) appears to improve prediction of energy expenditure vs. the univariate models (bouts only, R2=45%).

p. So far, we haven't considered the statistical significance of the coefficients we have seen in the above regression models. Below, in table format, the p-values for these coefficients are reported for Model 2.

Model 2 ? p-value

? age -5 0.12

? bouts 909

Interpret the p-value for the coefficients of age (? age) and bouts (? bouts) in a sentence below. Also, state whether you would accept or reject the null hypothesis for each relationship?

? age

? bouts