(1 pts) In regression another name for the independent variable is the regressor

Question

1. (1 pts)
   In regression another name for the independent variable is
     the regressorvariable.
     the predictorvariable.
     both (a) and(b).
     neither (a) nor(b).
   
   
2. (1 pts)
   A probabilistic model is a determanistic model plus randomerror.
     TRUE
     FALSE
   
   
3. (1 pts)
   A simple linear regression line estimates
     the value of the nextresponse observed at a given value of the explanatoryvariable.
     the mean response observedat a given value of the explanatory variable.
     the minimum responseobserved at a given value of the explanatory variable.
     the maximum responseobserved at a given value of the explanatory variable.
   
   
4. (1 pts)
   The method of least squares
     minimizes the sum of thedistances between all the responses and the regressionline.
     minimizes the sum of thesquared distances between all the responses and the regressionline.
     maximizes the sum of thedistances between all the responses and the regressionline.
     maximizes the sum of thesquared distances between all the responses and the regressionline.
   
   
5. (1 pts)
   Which is not an assumption of the simple linear regressionprocedure?
     Errors are normallydistributed at all values of x.
     The mean the errors at eachvalue of x is 0.
     The variance of the errorsat each value of x is equal.
     The slope of the regressionline is not equal to zero.
   
   
6. (1 pts)
   Given the regression line y = 10 - 1.2(x) and a coefficient ofdetermination of 0.81, what is the correlation coefficient?
   Response:
   
   
7. (1 pts)
   The prediction interval at a given value of x is always wider thanthe confidence interval at the same value of x in a simple linearregression.
     TRUE
     FALSE

Explanation / Answer

The independent variable is also called both a and b. The regressor and predictor variables. A probabilistic model is a determanistic model plus randomerror.
It is true. A simple linear regression line estimates the mean response observed at a given value of the explanatoryvariable. The method of least square is used to minimizes the sum of the squared distances between all theresponses and the regression line. The slope of the regression line is notequal to zero is not an assumption. The correlation coefficient is 0.81 =0.9. It is true that the prediction interval at a given value of x isalways wider than the confidence interval at the same value of x ina simple linear regression.

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