In class we lcarned about simple linear regression a method

In class we lcarned about simple linear regression, a method for assessing the association between a single continuous independent variable and a single continuous dependent variable. Multiple linear regression generalizes simple linear regression to assessing the association between two or more continous independent variables and a single continuous dependent variable. In this assignment, you will get some practice using the Im function to perform multiple linear egression. SBP age weight 173 184 194 162 73 211 196 220 188 188 207 128 46 167 166 72 217 132 52 143 59 153 67 154 64 168 74 137 51 149 61 159 65 The following table shows data collected on 11 ndividuals. Three quantitics are measured: systolic blood pressure (SBP) in mm Hg, age in years, and weight in pounds. We would like to build a regression model that simultaneously quantifies the association between SBP and age and the association between SBP and weight. We fit the data to the following linear model where is SBP lil) is age, and r (2) is weiglt of the ith individual. The regression coefficients and quantify how SBP varies with a single unit of change in age and weight respectively. These coefficients can be estimated by the least squares procedure just as in the simple linear regression case and can be computed using the Im function First, the data needs to be in a data frame structure ## SBP age weight ##113252 173 ##214359 184 ##315367 194 ##416273 211 ##515464 196 ##616874 220 ##713754 188

Solution

question 1: Since the interval does not include 0, there is association between SBP and age and SBP and weight also.

question 2: There are equal number of values above an below the x axis, hence the residuals are randomy distirbuted and are uncorrelated.

question 3: residual se is 2.318.

question 4: this means that 97.68% of the variablity in this dataset is explained by these independent variables.