Exam 11: Regression Analysis: Statistical Inference

The term autocorrelation refers to:

The value k in the number of degrees of freedom,n-k-1,for the sampling distribution of the regression coefficients represents:

In the standardized value $\left( b _ { i } - B _ { i } \right) / s _ { b _ { j } }$ ,the symbol $s _ { b _ { 1 } }$ Represents the:

In regression analysis,homoscedasticity refers to constant error variance.

In multiple regression with k explanatory variables,the t-tests of the individual coefficients allows us to determine whether $B _ { i } \neq 0$ (for i = 1,2,…. ,k),which tells us whether a linear relationship exists between $x$ and Y.

The Durbin-Watson statistic can be used to measure of autocorrelation.

In multiple regression,the problem of multicollinearity affects the t-tests of the individual coefficients as well as the F-test in the analysis of variance for regression,since the F-test combines these t-tests into a single test.

When there is a group of explanatory variables that are in some sense logically related,all of them must be included in the regression equation.

The test statistic in an ANOVA analysis is:

Determining which variables to include in regression analysis by estimating a series of regression equations by successively adding or deleting variables according to prescribed rules is referred to as:

In regression analysis,the ANOVA table analyzes:

Homoscedasticity means that the variability of Y values is the same for all X values.

In order to estimate with 90% confidence a particular value of Y for a given value of X in a simple linear regression problem,a random sample of 20 observations is taken.The appropriate t-value that would be used is 1.734.

The objective typically used in the tree types of equation-building procedures are to:

Which of the following is not one of the assumptions of regression?

In a simple linear regression model,testing whether the slope $\beta _ { 1 }$ of the population regression line could be zero is the same as testing whether or not the linear relationship between the response variable Y and the explanatory variable X is significant.

Which of the following is the relevant sampling distribution for regression coefficients?

A point that "tilts" the regression line toward it,is referred to as a(n):

In order to test the significance of a multiple regression model involving 4 explanatory variables and 40 observations,the numerator and denominator degrees of freedom for the critical value of F are 4 and 35,respectively.

When the error variance is nonconstant,it is common to see the variation increases as the explanatory variable increases (you will see a "fan shape" in the scatterplot).There are two ways you can deal with this phenomenon.These are: