A simple linear regression model was fitted to two variables: Y, the response variable and X, the explanatory variable. A scatter plot of Y versus X as well as the residual plot after fitting the...


A simple linear regression model was fitted to two variables: Y, the response variable<br>and X, the explanatory variable.<br>A scatter plot of Y versus X as well as the residual plot after fitting the least squares<br>regression line to the data are given below.<br>0.4<br>0.35<br>0.3<br>0.25<br>> 0.2<br>0.15<br>0.1<br>0.05<br>2<br>10<br>X<br>0.2<br>0.15<br>0.1<br>0.05<br>10<br>-0.05<br>-0.1<br>Residuals<br>

Extracted text: A simple linear regression model was fitted to two variables: Y, the response variable and X, the explanatory variable. A scatter plot of Y versus X as well as the residual plot after fitting the least squares regression line to the data are given below. 0.4 0.35 0.3 0.25 > 0.2 0.15 0.1 0.05 2 10 X 0.2 0.15 0.1 0.05 10 -0.05 -0.1 Residuals
Which ONE of the following concerns with the assumptions for simple linear<br>regression is NOT indicated by the plots?<br>A straight line does not appear to be a good fit for the data.<br>The scatter about the trend increases as x increases.<br>There are doubts about the Normality of the errors.<br>The relationship between x and y appears curved.<br>The amount of variability in the residuals appears to depend on x.<br>

Extracted text: Which ONE of the following concerns with the assumptions for simple linear regression is NOT indicated by the plots? A straight line does not appear to be a good fit for the data. The scatter about the trend increases as x increases. There are doubts about the Normality of the errors. The relationship between x and y appears curved. The amount of variability in the residuals appears to depend on x.

Jun 07, 2022
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