The coefficients for logarithmically transformed explanatory variables when the dependent variable is alsologarithmically transformed should be interpreted as the percent change in the dependent variable for a 1% percent change in the explanatory variable. True or false
Solution :
False
The coefficients for logarithmically transformed explanatory variables when the dependent variable is also logarithmically
transformed should be interpreted as the percent change in the dependent variable for a 1% percent change in
the explanatory variable .
Independent variable is a explanatory variable
The coefficients for logarithmically transformed explanatory variables when the dependent variable is alsologarithmically transformed should be...
When two explanatory variables are highly correlated, should you remove one of the correlated explanatory variables to reduce the multicollinearity problem. A. Yes, it will reduce the standard errors on the coefficients and increase the t statistics. B. No, it will not affect the t statistics on the coefficients. C. No, it will cause the coefficient on the remaining variable to be biased. D. Yes, it will improve the fit of the regression model.
Question 8 0.8 pts True or False: Adding explanatory variables that do not have a significant effect on the dependent variable to our model will lower the R-squared. O True O False
Suppose that you estimate a multiple regression model, but that you inadvertently omit an explanatory variable that is correlated with the dependent variable. In this case, the coefficients on the included variables will always be unbiased, but the standard errors and test statistics will be biased. the coefficients on the included variables will always be biased. there is no effect on the coefficients of the included variables since the omitted variable has been omitted. the coefficients on the included variables...
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...
1. Changing the unit of measurement of dependent variable, where log of the dependent variable appears in the regression: a. affects only the slope coefficient. b. affects neither the slope nor the intercept coefficient. c. affects only the intercept coefficient. d. affects both the slope and intercept coefficients. 2. Which of the following statements is true when the dependent variable, y > 0? a. Taking log of variables make OLS estimates more sensitive to extreme values. b. Models using log(y)...
In order to compare the effects of two or more independent variables on a dependent variable the beta weight would be the proper statistic to use. a. True b. False
Use the following linear regression equation to answer the questions. x1 = 1.5 + 3.4x2 – 8.3x3 + 2.3x4 (a) Which variable is the response variable? Which variables are the explanatory variables? (b) Which number is the constant term? List the coefficients with their corresponding explanatory variables. constant? x2 coefficient? x3 coefficient? x4 coefficient? (c) If x2 = 1, x3 = 8, and x4 = 6, what is the predicted value for x1? (Use 1 decimal place.) (d) Explain how...
Regression Variables Entered/Removeda Model Variables Entered Variables Removed Method 1 Warranty_Yearsb . Enter a. Dependent Variable: Number_of_people_mentioned b. All requested variables entered. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .503a .253 .251 .95930 a. Predictors: (Constant), Warranty_Years ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 80.590 1 80.590 87.574 .000b Residual 237.425 258 .920 Total 318.015 259 a. Dependent Variable: Number_of_people_mentioned b. Predictors: (Constant), Warranty_Years Coefficientsa Model Unstandardized...
Once the dependent variable is determined when building a bivariate or multiple-regression model, what is the next step? Multiple Choice Determine what factors contribute to the change in the dependent variable. Define the data series for the model. Specify the correlation between the dependent variables. Identify the other dependent variables.
In regression, we call Y the response or dependent variable, which is modeled in terms of one or more "independent" variables. The independent variables are further classified as explanatory/causal variables or as predictor variables. Discuss and elaborate on whether or not time can be a legitimate explanatory/causal variable, whether time can be a legitimate predictor variable whether a predictor variable must also be a causal/explanatory variable. Provide examples to support your arguments.