Regression has multiple uses
a)It can be used to predict and extrapolate an output based on the insample regression model->A is correct
b)It is for sure used to exlain the changes in an independent variable on the dependent variable->B is correct
c)Regression doesnot always prove causation. It could be a one way relationship->C is not always correct
Ans->D. A&B only
Regression analysis is used to: A. Predict the value of the dependent variable vased on the...
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
1._____________________ is used to predict the value of a dependent variable based on the value of at least one independent variable. a. Correlation b. Regression c.Confidence interval d.Strength test e.Scatter diagram 2. In order to establish a confidence interval for a mean when the population standard deviation is known, what four data points are necessary? 3.What is the only exception to the rule to using a z score for a critical value for confidence intervals?
11. Multiple regression analysis is used when one independent variable is used to predict values of two or more dependent variables. True or False 13. For a two-tailed null hypothesis, the test statistic Z=1.96. Therefore, the p-value is 0.05. True False
Use linear regression to predict the value at X=10. Independent Variable Dependent Variable о о л о Ол Select one: a. 23.64 b. 2164 C. 4.23 d. 0,45
1. In multivariate regression: a) More than one independent variable is used to predict a single dependent variable b) The value of r gives you the slope c) More than one dependent variable is predicted by a single independent variable d) More regressions are necessary
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...
QUESTION 1 The Simple Linear Regression is fit or constructed to predict a dependent variable. True False QUESTION 2 The Coefficient of Determination is used to explain in what percent (%) the independent variable is affecting the dependent variable. True False
If you perform a hypothesis test on the population slope Parameter (β1) in regression analysis and reject the Null hypothesis: Ho: β1= 0. Your conclusion would be: A.) The least squares sample regression equation should not be used because there is not sufficient evidence of a relationship between the independent variable and the dependent variable.. B.) The least squares sample regression equation should be used because there is sufficient evidence of a relationship between the independent variable and the dependent...
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
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