Is the cubic effect significant? How about quadratic and linear effects? Analysis of Variance Table Response: EX Df Sum Sq Mean Sq F value Pr(>F) MET 1 332 332 0.1246 0.7257742 I(MET^2) 1 37504 37504 14.0572 0.0005141 *** I(MET^3) 1 7245 7245 2.7154 0.1065086 Residuals 44 117390 2668 > anova(quadModel) Analysis of Variance Table Response: EX Df Sum Sq Mean Sq F value Pr(>F) MET 1 332 332 0.120 0.7306134 I(MET^2) 1 37504 37504 13.541 0.0006216 *** Residuals 45 124635 2770 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Continuous Probability Distributions
Probability distributions are of two types, which are continuous probability distributions and discrete probability distributions. A continuous probability distribution contains an infinite number of values. For example, if time is infinite: you could count from 0 to a trillion seconds, billion seconds, so on indefinitely. A discrete probability distribution consists of only a countable set of possible values.
Normal Distribution
Suppose we had to design a bathroom weighing scale, how would we decide what should be the range of the weighing machine? Would we take the highest recorded human weight in history and use that as the upper limit for our weighing scale? This may not be a great idea as the sensitivity of the scale would get reduced if the range is too large. At the same time, if we keep the upper limit too low, it may not be usable for a large percentage of the population!
0.6.3 Question 3 Is the cubic effect significant? How about quadratic and linear effects?
Analysis of Variance Table
Response: EX
Df Sum Sq Mean Sq F value Pr(>F)
MET 1 332 332 0.1246 0.7257742
I(MET^2) 1 37504 37504 14.0572 0.0005141 ***
I(MET^3) 1 7245 7245 2.7154 0.1065086
Residuals 44 117390 2668
> anova(quadModel) Analysis of Variance Table Response: EX Df Sum Sq Mean Sq F value Pr(>F) MET 1 332 332 0.120 0.7306134 I(MET^2) 1 37504 37504 13.541 0.0006216 *** Residuals 45 124635 2770 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
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