Select two variables, one independent variable and the other the dependent variable. Explain the cause and effect the relation between the two. How would you construct the Simple Linear Regression (quantity of data and how you will measure the results), and what would you use to validate the equation forecasting an x value. What will be your expectation of the results of this investigation?
Answer the following: Please write this in computer Select two variables, one independent variable and the...
Please write this in the computer, not by the hand. 12. Answer the following: Select two variables, one independent variable and the other the dependent variable. Explain the cause and effect the relation between the two. How would you construct the Simple Linear Regression (quantity of data and how you will measure the results), and what would you use to validate the equation forecasting an x value. What will be your expectation of the results of this investigation?
Consider the following results of a multiple regression model of dollar price of unleaded gas (dependent variable) and a set of independent variables: price of crude oil, value of S&P500, price U.S. Dollars against Euros, personal disposal income (in million of dollars) : Coefficient t-statistics Intercept 0.5871 68.90 Crude Oil 0.0651 32.89 S&P 500 -0.0020 18.09 Price of $ -0.0415 14.20 PDI 0.0001 17.32 R-Square = 97% What will be forecasted price of unleaded gas if the value of independent...
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 linear regression model found the following : Dependent variable : Quantity Independent variables : X1 X2 coefficient constant. 10 price. -2 Income. 3 R^2 = 0.83 t = 2.36 a. write the demand function as an equation b. do the sign of the coefficients make sense ? why? c. if price = 10, Income = 24 what is the predicted quantity sold? d. find the point price elasticity at price =10, Income = 24
Consider the following data for a dependent variable y and two independent variables, w, and as for these data SST = 15,172.4, and SSR - 14.127.4. 30 13 95 47 10 10 24 18 112 51 17 179 41 6 95 52 19 175 758170 37 12 118 59 14 76 17 Round your answers to three decimal places a. Computer b. Computer c. Does the estimated regression equation explain a large amount of the variability in the data? -...
1. If you were to graph a time series and it followed a trend that was close to linear, then what type of forecasting model would you use? Multiple Choice Bass model Bivariate linear regression Simple moving average Gompertz curve 2. Visualization of data allows you to ____________________. Multiple Choice be as transparent to management as required more clearly identify the dependent and independent variables better understand if you need more data see stark differences that would not be apparent...
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...
Based on the graph depicting the relationship between two variables, you would conclude the 10 variable 2 variable 1 A independent variable: discrete/nominal; relationship best tested with univariate test (e.g. analysis of variance) B. independent variable: continuous; relationship best tested with bivariate test (e.g. linear regression) O dependent variable: discrete/nominal relationship best tested with contingency test (eg, chi-square) D. dependent variable: continuous; relationship best tested with bivariate test (e.g. linear regression)
Based on the graph depicting the relationship between two variables above, you would conclude the variable 2 b variable 1 Independent variable: discrete/nominal, relationship best tested with univariate test (e.g. analysis of variance)n 1 independent variable: continuous; relationship best tested with bivariate test (e.g. linear regression) dependent variable: discrete/nominal; relationship best tested with contingency test (e.g. chi-square) dependent variable: continuous; relationship best tested with bivariate test (e.g. linear regression)
1. Which of the following is correct? A. In correlation analysis there are two variables and both are dependent B. I n correlation analysis there are two variables and both are independent C. I n regression analysis there are two variables and both are dependent D: In regression analysis there are two variables and both are independent 2. measures the strength of linear association between two variables. A. Regressor B. Regressand C. Correlation coefficient D. None 3. the independent variables....