You are interested in studying the impact that someone's weight (in lbs-pounds) might have on their systolic blood pressure (mmHg-millimeters of mercury). ? You collected data from 33 healthy individuals across a variety of weights, ? You measured each of the individual's blood pressures every day for a week and took the average to create each "blood-pressure" observation. You then asked you assistant to analyse the data, who wasn't quite sure what he was doing! He made every graph and analysis he could think of.... It's your job to interpret what is actually going on...

130 140 150 160 Blood Pressure Weight (Lbs) 140 160 180 200 220 240 130 140 150 160 140 160 180 200 220 240 Blood Pressure Weight (Lbs) Call: Lm(formula = blood_pressure ~ weight, data = weightbp) Residuals: Min 10 Median 30 Max . . fr ] -14. 168 -4. 155 -1.085 4.926 11. 126 anova (weightbp . lm1) . . . Coefficients: Estimate Std. Error t value Pr(>Itl) (Intercept) 70.93313 8.21965 8. 630 9.56e-10 * * * Analysis of Variance Table weight 0. 36891 0. 04363 8. 455 1.50e-09 * ** Response: blood_pressure Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '. ' 0.1 ' ' Df Sum Sq Mean Sq F value Pr ( > F ) weight 1 2780.4 2780.42 71. 492 1. 499e-09 *** Residual standard error: 6.236 on 31 degrees of freedom Residuals 31 1205.6 38.89 Multiple R-squared: Adjusted R-squared: F-statistic: 71.49 on 1 and 31 DF, p-value: 1. 499e-09 Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 ' . ' 0.1 ' ' 1 Call: Lm(formula = weight ~ blood_pressure, data = weightbp) Residuals : Min 1Q Median 3Q Max -22. 701 -9.751 -0.328 7.259 35.839 anova (weightbp . lm2) Coefficients: Estimate Std. Error t value Pr(>Itl) (Intercept) -77 . 6398 31.3648 -2.475 0. 019 * Analysis of Variance Table blood_pressure 1.8908 0.2236 8. 455 1.5e-09 * * * Response: weight Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '. ' 0.1 ' ' Df Sum Sq Mean Sq F value Pr ( > F) blood pressure 1 14251.1 14251.1 71.492 1. 499e-09 *** Residual standard error: 14.12 on 31 degrees of freedom Residuals 31 6179.5 199.3 Multiple R-squared: Adjusted R-squared: F-statistic: 71.49 on 1 and 31 DF, p-value: 1.499e-09 Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '. ' 0.1 ' ' 1