a)
s2 =2.533
b)
s =1.592
c)
sb1=0.503
d)test statistic =4.77
p value 0.01 < p value <0.025
conclusion:reject Ho: there is a significant linear relationship
e)
Source | DF | SS | MS | F |
regression | 1 | 57.600 | 57.600 | 22.74 |
Residual error | 3 | 7.600 | 2.533 | |
Total | 4 | 65.200 |
p value : 0.01 < p value <0.025
conclusion:reject Ho: there is a significant linear relationship
In an analysis of variance problem involving 3 treatments and 10 observations per treatment, SSE = 399.6. The MSE for this situation is which? 133.2 13.32 14.8 30.0
If the MSE for Forecast 1 was 38 and the MSE for Forecast 2 was 34, Forecast 1 performed better. True False
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9. Consider the ANOVA model where Xij ~ N(μί,02). Then show that SSE (a) the random variable-O-~ χ2(1(J-1), and (b) the statistics SSE and SSTr are independent. Further, if the null hypothesis Ho : μ.-μ2-...-μι-, μ is true, then SSTr (c) the random variable-"K2(1-1) MSTr MSE (e) the random variable ~ χ2(U-1). (d) the statistics MsF), and
The Mean Squared Error (MSE) of an estimator ?̂ of θ is defined as MSE = E[(?̂ − θ)2] Prove that MSE = Var(?̂) + [bias(?̂)]2 where bias(θ) = E(?̂) − θ
e Pas 2. If SSE is near zero in a regression, conclusion will be that the proposed model probably has a. too poor a fit to be useful b. near perfect fit c. a very strong positive linear relation d. a very strong negative linear relation 3. A residual/error shows the difference between Yactual and Yestimated on a standard scale. True False 4. In a simple regression, the F statistic is calculated by taking the ratio of MSR to the...
2. Decide if the following is true or false: In every RBD setup, either the MSTR MSE or MSB2 MSE (or maybe both). If it is true, explain why. If it is false, give a data set that is a counterexample.
2. Decide if the following is true or false: In every RBD setup, either the MSTR MSE or MSB2 MSE (or maybe both). If it is true, explain why. If it is false, give a data set that is...
1. if SSR = 25 and SSE = 20, what is R2 ? 2. If SSR = 47 and SSE = 12, what is R? Please, do all the problems and show processes
In simple linear regression, select the correct interpretation of the error sum of squares (SSE): SSE is the amount of variation in the explanatory variable that is not accounted for by the response variable. SSE is the amount of variation in the response variable that is accounted for by the explanatory variable. SSE is the amount of variation in the response variable that is not accounted for by the explanatory variable. SSE is the amount of variation in the explanatory...
1. if SSR = 25 and SSE = 20, what is R2 ? 2. If SSR = 47 and SSE = 12, what is R? Please, do all the problems and show processes