ISDS COPE FINAL EXAM QUESTIONS WITH
VERIFIED ANSWERS.
If the interaction between two factors is not significant, the next tests to be done are
___________________.
A. none, the analysis is complete
B. none, gather more data
C. tests about the population means of factor A or factor B using two-way ANOVA
without interaction
D. Tukey's confidence intervals - ✔✔C. tests about the population means of factor A
or factor B using two-way ANOVA without interaction
The correlation coefficient can only range between 0 and 1. - ✔✔FALSE (-1 </= RXY
</= 1)
Simple linear regression includes more than one explanatory variable. - ✔✔FALSE
A simple linear regression model uses one explanatory variable to explain the
variability in the response variable. - ✔✔TRUE
Simple linear regression analysis differs from multiple regression analysis in that
A. simple linear regression uses only one explanatory variable
B. the coefficient of correlation is meaningless in simple linear regression
,C. goodness-of-fit measures cannot be calculated with simple linear regression
D. the coefficient of determination is always higher in simple linear regression - ✔✔A.
simple linear regression uses only one explanatory variable
In a simple linear regression model, if the plots on a scatter diagram lie on a straight
line, which of the following is the standard error of the estimate?
A. -1
B. 0
C. +1
D. Infinity - ✔✔B. 0
In a simple linear regression model, if the plots on a scatter diagram lie on a straight
line, which of the following is the coefficient of determination?
A. -1
B. 0
C. +1
D. Infinity - ✔✔C. +1
, Calculate the value of R2 given the ANOVA portion of the following regression
output:
Source of variance SS df MS F p-value
Regression 2,562 1. 2,562 6.58. 0.0145
Residual 14,395. 37. 389
Total 16,957 38
A. 0.151
B. 0.515
C. 0.849
D. 1.000 - ✔✔A. 0.151
The standard error of the estimate measures ____________.
A. the variability of the explanatory variables.
B. the variability of the values of the sample regression coefficients.
C. the variability of the observed y-values around the predicted y-values.
D. the variability of the predicted y-values around the mean of the observed y-values.
- ✔✔C. the variability of the observed y-values around the predicted y-values.
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