There are 10 questions pertaining to modeling, regression, and general stats. I am looking for input where my answers are incorrect or where you feel additional input would help.
1. The regression coefficient is most likely a useful predictor if its value is equal to zero
My guess is True
2. The sum and count function do basically the same thing and can be used interchangeably.
Is there a difference with count if and does this fall under this question?
My guess is false
4. Under a "scatter diagram" there is a notation that the coefficient of correlation is .10. What does this mean?
a. plus and minus 10% from the means includes about 68% of the cases
b. one-tenth of the variance of the variable is shared with the other variable
c. one-tenth of one variable is caused by the other variable
d. on a scale from -1 to +1, the degree of linear relationship between the two variables is +.10
5. Unlike data analysis data modeling is not restricted and limited by using thresholds to reduce the volume of data being evaluated.
6. Using absolute call references in Excel is not helpful in building scoring model equations.
I don't ever recall using this function when building a model.
7. What would you guess the value of the correlation coefficient to be for the pair of variables: "number of analyst-hours worked" and "number of under of units of work completed"?
a. Approximately 0.9
b. Approximately 0.4
c. Approximately 0.0
d. Approximately -0.4
e. Approximately -0.9
Not sure how to calculate this.
8. When a relationship exists between two variables that can not be represented by a straight line, linear regression can give misleading results
My guess is True
9. When building regression models with Excel, independent variables do not have to be in adjacent columns,
I am under the impression that dependent variables have to be in adjacent columns, but not sure about independent variables.
10. When two independent variables are highly correlated including both variables in a regression model will not likely raise the problem of multicollinearity.
Not sure what is meant by multicollinearity
The solution answers of 10 multiple choice question from regression analysis