Question

7. The line of best fit is the 8. The point at which the regression line crosses the is referred to as the intercept. 9. The proportionate reduction in error is also called the 10. variables are independent variables that make separate but distinct contributions in the prediction of a dependent variable, as compared with the contributions of other variables. 11. If both the independent and dependent variables are measured as scale variables, then one can use either correlation or to analyze the data 12. The effect size for chi square is 13. If 10 deaths result from the 100 car accidents in which a person is wearing a seatbelt, and 33 deaths result from the 80 car accidents in which a person is not wearing a seatbelt, the conditional probability of dying given that a person is not wearing a seatbelt is 14. It would be inappropriate to use a parametric hypothesis test if the dependent variable was measured on a(n) or an) scale. 15. With nominal variable(s), a chi-square test for goodness-of-fit should be used to analyze the data. Page

Answer 1

As HOMEWORKLIB answering guidelines for multiple questions, answering the first 4 questions completely

7. The line of best fit is the trendline or the regression line

The trendline or regression line is the line which is the best fit for all the given points such that the error (difference in actual value and predicted value is minimized)

8. The point at which regression line crosses the y-axis is referred to as the intercept.

A typical regression line is shown below with the corresponding intercept where the regression line meets the y-axis

9. The proportionate reduction in error is also called the R²

R² (R-squared) is used to denote the proportionate reduction in error or how much of the variance in the dependent variable is explained by the variance in the independent variable

10. Orthogonal variables are independent variables that makes a separate but distinct contributions in the prediction of a dependent variable, as compared with the contributions of another variables

Orthogonal variables are uncorrelated variables. That means the impact of these variables in the output is not dependent on the impact of any other variables.