R - Multiple regression - Quizz

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<quiz display=simple > {The multiple correlation (R) is: (check all that apply)

|type="[]"} +The correlation between predicted and observed scores. -The sum of the simple r's. -The highest simple r. +Always between 0 and 1 (inclusive).

{

Answer >>

R is the correlation between predicted and observed scores when there are two or more predictors. It is always between 0 and 1.

}

{In multiple regression there are:

|type="[]"} -multiple criterion variables. +multiple predictor variables. -two predictor variables.

{

Answer >>

Having two or more predictor variables is what distinguishes multiple regression from simple regression.

}

{The difference between a regression weight and a beta weight is:

|type="[]"} -A regression weight assumes linearity. -A beta weight is for the population while a regression weight is for the sample. -A regression weight is less biased. +A beta weight is a standardized regression weight.

{

Answer >>

A beta weight is a standardized regression weight.

}

{In the regression equation Y' = b1X1 + b2X2 + A, if b1 = 5, then how much would the predicted value of Y differ for two observations that had the same value of X2 but differed by 7 on X1?

|type="{}"} { 35 }

{

Answer >>

35

}

{The difference between a regression weight and a regression coefficient is: (check all that apply)

|type="[]"} -The regression weight is more important. -The regression weight is unbiased. -The regression weight is added rather than multiplied.

{

Answer >>

They are synonymous.

}

{A regression weight is a partial slope because:

|type="[]"} +It is the slope when the part of the predictor independent of the other predictors is used to predict the criterion. -It is only one of several slopes, so it is only part of the prediction equation. -It is the relationship between the significant part of a predictor and the criterion. -It is only an estimate of the true slope and so is a partial solution.

{

Answer >>

It is the slope when the part of the predictor independent of the other predictors is used to predict the criterion. The other predictors are "partialled out."

}

{Find the value of the multiple correlation (R). You should use a computer to find the solution.

Y X1 X2 X3

27.6	  1	  4	  4
 9.4	  3	  5	  3
15.6	  4	  7	  1
20.3	  5	  5	  4
12.3	  3	  7	  3
 8.7	  5	  3	  6
 7.3	  7	  5	  7
14.9	  6	  4	  8
17.0	  5	  3	  9
-0.8	  4	  2	  0

|type="{}"} { 0.7575 }

{

Answer >>

0.7575

}

{These are the same data as in the previous question. Find the value of b2. You should use a computer to find the solution.

Y X1 X2 X3

27.6	  1	  4	  4
 9.4	  3	  5	  3
15.6	  4	  7	  1
20.3	  5	  5	  4
12.3	  3	  7	  3
 8.7	  5	  3	  6
 7.3	  7	  5	  7
14.9	  6	  4	  8
17.0	  5	  3	  9
-0.8	  4	  2	  0

|type="{}"} { 1.6848 }

{

Answer >>

1.6848

}

{The sum of squares explained is 200 and the sum of squares error is 100. What is the R2?

|type="{}"} { 0.667 }

{

Answer >>

0.667

}

{The sum of the simple r2's is typically

|type="[]"} -less than R2 -equal to R2 +greater than R2

{

Answer >>

Greater than R squared. Typically there is overlap in the variance explained by the predictors.

}

{Which of the following assumptions pertain to inferential statistics in multiple regression?

|type="[]"} -The predictor variables are normally distributed. -The criterion variable is normally distributed. +The errors of prediction (the residuals) are normally distributed. +The variance about the regression line is the same for all predicted values. +The predictor variables are linearly related to the criterion.

{

Answer >>

Residuals are normally distributed. The variance about the regression line is the same for all predicted values (homoscedasticity). The predictor variables are linearly related to the criterion.

}