Sep 29, 2018 · Which of the following assumptions appears violated based on this plot? A) The errors are independent B) The errors are normally distributed C) The variance of the errors is constant D) The mean of the errors is zero Answer: C 119) 120) It is desired to build a regression model to predict y = the sales price of a single family home, based on the x 1 = size of the …
Regression Analysis It is important to note that the approach used here first exposes the useful concepts of. ... analyze residuals to determine if the regression model is an adequate fit to the data or to see if any underlying assumptions are violated, use the fitted regression model to make a prediction of a future observation and interpret ...
Aug 04, 2018 · 13. Which of the following Gauss-Markov assumptions is violated by the linear probability model? a. The assumption of constant variance of the error term. b. The assumption of zero conditional mean of the error term. c. The assumption of no exact linear relationship among independent variables. d.
In fact we will study three specific cases where this assumption is violated. These are: (i) the errors in measurement case; (ii) the case of a lagged dependent variable with correlated errors; and (iii) simultaneous equations.
T/F- The values of a and b in the regression equation are called the regression coefficients.
94% of the total variation of the dependent variable is explained by the independent variable.
The independent variables and the dependent variable have a linear relationship.
T/F- The multiple coefficient of determination, R square, reports the proportion of the variation in Y that is not explained by the variation in the set of independent variables.
T/F- In multiple regression analysis, an F-statistic is used to test the global hypothesis.
T/F- One assumption underlying linear regression is that the X values are normally distributed.
T/F- Multiple regression analysis is used when one independent variable is used to predict values of two or more dependent variables.
Whenever a relationship between a dependent and an independent variable is spurious, the regression slope between the two variables will fall to zero if we introduce a third variable in the regression model that causes the other two.
The standard deviation of these slope estimates is called the standard error of the slope. Can also be used to test the statistical significance of the slope (using a t-test).
For any given xi, a regression line gets close to yi, but not exactly yi. We call the estimated or predicted value "y-hat" (y ̂i)
Inference helps us determine whether our regression findings (i.e., the equation of the regression line) are statistically valid.
The relationship between two variables can be classified in three ways:
Because the relationship does not change as a result of scalar transformations. -Use nonlinear transformations when you are running regressions with nonlinear relationships to transform them into linear relationships. The method of ordinary least squares REQUIRES a linear relationship between X and Y.
The relationship between two variables can be classified in three ways: 1. Causal/predictive: dependent and independent variables. 2. Functional . 3. Statistical: knowing the value of the independent variable lets us estimate a value for the dependent variable, but the estimate is not exact.
c. The R-square generated by the regression analysis is a measure of how well the regression analysis cost equation fits the data.
If a company sells one unit above its breakeven sales volume, then its operating income would be equal to