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Which of the following graphics are commonly used to diagnose the ANOVA model residual? I. QQ-plot II. Cube plot III: Dot plot IV. Residual plot[and III and II and ...

Question

Which of the following graphics are commonly used to diagnose the ANOVA model residual? I. QQ-plot II. Cube plot III: Dot plot IV. Residual plot[and III and II and and IV

Which of the following graphics are commonly used to diagnose the ANOVA model residual? I. QQ-plot II. Cube plot III: Dot plot IV. Residual plot [and III and II and and IV



Answers

Which of the following can cause the usual OLS $t$ statistics to be invalid (that is, not to have $t$ distributions under $\mathrm{H}_{0}$ )? i. Heteroskedasticity. ii. A sample correlation coefficient of .95 between two independent variables that are in the model. iii. Omitting an important explanatory variable.

Okay. Here were asked which of the following statements about residuals from the least squares line are true and we have one. But the main of the residuals is always zero to that. The regression line for a residual plot is a horizontal line and three a definite pattern in the residual plot is an indication that a nonlinear model will show a better fit to the data than a straight regression line. And so here the correct answer is going to be D. That all three are true and that's because the sum and the slamming of the residuals is always zero. Okay. Okay. Yeah, mm hmm. And in a good straight line fit residuals show a random pattern. Yeah. Yeah. Yeah. Yeah. Mhm. So again, all three are true. So that leaves us with answer D. That all both 12 and three or true being correct? Yeah. Yeah. Uh huh.

Which of the following can cost oils s meters to be biased and options are a terrorist conduct city Second option is a meeting and important variable on example Correlation coefficient off 0.95 between two independent variables, both included in the model. So oils estimators can be biased as omitting on important variable. So the answer would be the second option meeting an important variable. So that's the answer.

So we are going to compute the our values and test the hypothesis that is our hypothesis right there. We're going to graph the scatter plot and I'll show you what it looks like. That's what the scatter plot looks like. Now look at the points, this looks nothing linear. Remember, the R. Value tells us the slope. So if I go back here and I look at my our value, it is undefined. And what undefined means is there is no slope because it's probably curvilinear, so it's a parabola, there is no slope, it's undefined. So we have to take that.


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