Answer:
Step-by-step explanation:
Hello!
a) The coefficient of determination can assume negative values.
FALSE
The coefficient of determination gives you an idea of how much of the variability of the dependent variable (Y) is due to the explanatory variable(s). It takes values in the range from 0 to 1, where 0 indicates that the explanatory variable has nothing to do with the variability of the dependent variable and 1 indicates that all the variability of the dependent variable is due to the explanatory variable. The closer the coefficient is to 1, the better the regression model.
b) A negative correlation indicates that as values of x decrease, values of y will decrease.
FALSE
The linear correlation analysis studies the association between two variables X and Y. A negative linear correlation means that when you draw the linear association the slope of it will be negative. A negative slope indicates that every time X increases, Y will decrease. (see example attached)
c) If there is no correlation between the independent and dependent variables, then the value of the correlation coefficient must be -1.
FALSE
This coefficient gives an idea of the degree of correlation between the variables.
If ρ = 0 then there is no linear correlation between X and Y Graphically, the slope is cero
If ρ < 0 then there is a negative association between X and Y (i.e. when one variable increases the other one decreases) In a graphic, the slope of the line is negative.
If ρ > 0 then there is a positive association between X and Y (i.e. Both variables increase and decrease together)
d) The variable that is being predicted in regression analysis is the dependent variable.
TRUE
The dependent variable, Y, is the variable that is expected to change when the researcher manipulated the independent variable(s).
e) The correlation coefficient r is always greater than 1.
FALSE
The coefficient r takes values between -1 and 1
I hope it helps!
