In regression analysis: a. the independent variable must be categorical in nature. b. the variables being investigated must not be correlated. c. the independent variable is indisputably influenced by the dependent variable. d. one variable is believed to be influenced by the other.
In regression analysis: a. the independent variable must be categorical in nature. b. the variables being...
In statistical modeling, regression analysis helps you to: a. None of them b. estimate the relationships between two dependent variables and one independent variable. c. estimate the relationships between a dependent variable and one or more independent variables. d. calculate the exact values for the dependent and independent variables.
In a regression analysis, the variable that is used to predict the dependent variable a. is the independent variable b. must have the same units as the variable doing the predicting c. is the dependent variable d. usually is denoted by x
4 & 5
QUESTION 4 What is a major difference between linear regression and logistic regression? a. The nature of the independent variable(s) b. The nature of the dependent variable c. The number of independent variables d. The number of dependent variables QUESTION 5 Which one of the following statistical tests would the researcher hope to have a non-significant result (p > .05) in a logistic regression analysis? a. The likelihood ratio test b. The logit step test C. The...
In a simple linear regression study between two variables x ( the independent variable) and y (the dependent variable), a random large sample is collected and the coefficient of correlation r = −.98 is calculated. A)Which of the following conclusion may be made? Group of answer choices x and y are almost perfectly correlated, and y increases as x is increased. x and y are almost perfectly correlated, and y decreases as x is increased. x and y are moderately...
The equation of the regression line between two variables x (independent variable) and y (dependent variable) is given by y-hat = -3x + 2; and the correlation coefficient is r = -.95. The possible x-values range from 1 to 10. Which of the following statements are correct? I. The variable y is strongly positive correlated to the variable x. II. The variable y is strongly negative correlated to the variable x. III. If x = 5, one would predict that...
Regression analysis is used to: A. Predict the value of the dependent variable vased on the value of at least one independent variable B. Explain changes in an independent variable on the dependent variable C. It offers proof of causation D. A & B only E. A, B, & C
A valid multiple regression analysis assumes or requires that Select one: O a. The dependent variable is measured using an ordinal, interval, or ratio scale O b. The residuals follow an F distribution O c. The independent variables and the dependent variable have a linear relationship O d. The observations are autocorrelated
Multicollinearity occurs when... Select one: independent variables are perfectly correlated dependent variables are perfectly correlated an independent variable is perfectly correlated with the dependent variable the error term is perfectly correlated with the intercept All/Any of the above. Which of the following statements is true regarding an F-Test? Select one: It is a joint hypothesis test. The null hypothesis states the all slope coefficients in the population regresion model are equal to zero. It tests whether or not one's regression...
Regression and Multicollinearity When multiple independent variables are used to predict a dependent variable in multiple regression, multicollinearity among the independent variables is often a concern. What is the main problem caused by high multicollinearity among the independent variables in a multiple regression equation? Can you still achieve a high r for your regression equation if multicollinearity is present in your data? Regression and Multicollinearity When multiple independent variables are used to predict a dependent variable in multiple regression, multicollinearity...
A multiple regression model has _____. a. at least two dependent variables b. more than one dependent variable c. more than one independent variable d. only one independent variable