Question
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One researcher wants to study if there is relationship between the nurses' assessment (X) and physicians' assessment (Y) of the condition of 10 patients at time of admission to a trauma center. Following is the output for the simple linear regression Term Estimate Std Error t Ratio Prob>|t| Intercept 1.2111801 2.055673 0. 59 0.5720 1.0822981 0.172266 6.28 0.0002* Which of the following statements is true? Since the estimate of the intercept is 1.211ISOl.whichis not 0, there is a significant linear relationship between X andY. Since the p-value for the teimX is 0.00025wiiich is smaller than 0.05, there is a significant linear relationship between X and Y at the significance level a= 0.05. For every 1-unit increase in the nurses' assessment, there is 1.2111801 increase in the physicians' assessment. The t ratio for the termX should have 9 degree of freedom. One can interpret r = 0.30 or the conespondingr2 = 0.09 as follows: A 30% reduction in error occurs when using x to predict y A 9% reduction in error occurs when using x to predict y compared to using to predict y. 9% of the time = - y. changes 0.3 units for every one-unit increase in x. x changes 0.3 standard deviations when v changes one standard deviation. R2 increases as the number of increases. R2-adj is adjusted for repressors that are significant in the model. In MLR the beta s represent a change in y given one-unit change on X. SSsm and SSe are similar because they are used to estimate the error variance. In a two sample test if the two sided CI includes zero, then you fail to reject the null
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