In an ANOVA calculation evaluating causation, the mean square ...

[Sufayan.ah]

In an ANOVA calculation evaluating causation, the mean square within groups value represents the ordinary amount of variation that exists in the data set. What does the mean square between groups value represent?
 
  a. The effect that the interventional conditions produce
  b. The average of all variations in the data set
  c. Total variation
  d. The differences that exist among the several interventional conditions

Question 2

Which of the following statements about prediction is true? (Select all that apply.)
 
  a. Simple linear regression can predict possible changes in A, given B.
  b. Multicollinearity is used to provide definitive attribution in predicting dependent variables with similar outcomes.
  c. Multiple regression can provide information about the strongest predictors, C, D, E, and F, associated with an outcome variable G.
  d. Odds ratio is used to predict the likelihood of a dichotomous event, in the light of a different dichotomous variable.
  e. Logistic regression is used to predict a dichotomous variable, using a variety of other variables.
  f. Cox hazard regression can predict the likelihood of an event occurring at certain points in time.

[chevyboi1976]

Answer to Question 1

ANS: A
The term mean square (MS) is used interchangeably with the word variance. The formulas for ANOVA compute two estimates of variance: the between groups variance and the within groups variance. The between groups variance represents differences between the groups/conditions being compared, and the within groups variance represents differences among (within) each groups' data. Therefore, the formula is F = MS between/MS within.

Answer to Question 2

ANS: A, C, D, E, F
Simple linear regression provides a means to estimate the value of a dependent variable based on the value of an independent variable. Multicollinearity occurs when the independent variables in a multiple regression equation are strongly correlated with one another. Multiple regression is an extension of simple linear regression in which more than one independent variable is entered into the analysis. When both the predictor and the dependent variable are dichotomous (having only two values; also called binary), the odds ratio is a commonly used statistic to obtain an indication of association. Logistic regression replaces linear regression when the researcher wants to test a predictor or predictors of a dichotomous dependent variable. Logistic regression can have continuous predictors or nominal predictors or a combination of the two, with no assumptions regarding normality of the distribution. The major difference between using Cox regression as opposed to linear regression is the ability of survival analysis to handle cases where survival time is unknown. Whereas logistic regression yields odds ratios for each predictor to represent the relationship between that predictor and y, Cox regression yields hazard ratios.