Stats 351 short answer quizzes
In a simple linear regression analysis, the coefficient of correlation between response and predictor is 0.65. The coefficient of determination in this situation is:
0.42
When dealing with the problem of non-constant variance, the reciprocal transformation means using:
1/Y as the dependent variable instead of Y
In order to test for the significance of a regression model involving 3 independent variables and 47 observations, the numerator and denominator degrees of freedom (respectively) for the critical value of F are:
3 and 43
The critical F value with 6 numerator and 60 denominator degrees of freedom at alpha=.05 is:
3.74
In an analysis of variance problem if SST = 120 and SSTR = 80, then SSE is:
40
In multiple regression analysis, the word linear in the term "general linear model" refers to the fact that:
B0, B1, ...Bp, all have exponents of 1
In a regression model involving more than one independent variable, which of the following tests must be used in order to determine if the relationship between the dependent variable and the set of independent variables is significant?
F test
The F ratio in a completely randomized ANOVA is the ratio of:
MSTR/MSE
In simple linear regression analysis, which of the following is true?
The F test and the t test yield the same conclusion
A variable that takes on the values of 0 or 1 and is used to incorporate the effect of categorical variables in a regression model is called:
a dummy variable
Which of the following tests is used to determine whether one additional variable makes a significant contribution to a multiple regression model?
an F test
The estimate of the multiple regression equation based on the sample data, which has the form of E(y) = y^=b0+b1x1+b2x2+...bpxp is:
an estimated multiple regression equation
A variable such as Z, whose value is Z=X1X2 is added to a general linear model in order to account for potential effects of two variables X1 and X2 acting together. This type of effect is:
called interaction
In multiple regression analysis, the general linear model:
can be used to accommodate curvilinear relationships between the independent variables and dependent variable
The interval estimate of the mean value of y for a given value of x is:
confidence interval estimate
In regression analysis, the variable that is being predicted is the:
dependent variable
The model developed from sample data that has the form of y^=b0 + b1x is known as:
estimated regression equation
A model in the form of y=B0+B1z1+B2z2+...+Bpzp + E where each independent variable zj (for j=1,2...p) is a function of xj, is known as the:
general linear model
A regression analysis between sales (Y in $1000) and price (X in dollars) resulted in the following equation: Y^= 60-8x. The equation implies that an:
increase of $1 in prices is associated with a decrease of $8000 in sales
If the coefficient of determination is 0.9, the percentage of variation in the dependent variable explained by the variation in the independent variable:
is 90%
The mean square is the sum of squares divided by:
its corresponding degrees of freedom
A measure of the effect of an unusual x value on the regression results is called:
leverage
A least squares regression line:
may be used to predict a value of y if the corresponding x value is given
In regression analysis, the unbiased estimate of the variance is:
mean square error
A term used to describe the case when the independent variables in a multiple regression model are correlated is:
multicollinearity
A measure of goodness of fit for the estimated regression equation is the:
multiple coefficient of determination
Compared to the prediction interval estimate for an individual value of y (in a linear regression model), the confidence interval estimate for an average value of y will be:
narrower
The interval estimate of an individual value of y for a given value of x is:
prediction interval estimate
The mathematical equation relating the independent variable to the expected value of the dependent variable; that is, E(y)=B0+B1x, is known as:
regression equation
In regression analysis, the model in the form y=Bo + B1x +E is called:
regression model
In regression analysis, an outlier is an observation whose:
residual is much larger than the rest of the residual values
The following regression model y=B0+B1x1+B2x1^2+E is known as:
second-order model with one predictor variable
In regression analysis, which of the following is not a required assumption about the error term E?
the expected value of the error term is one
The adjusted coefficient of determination is adjusted for:
the number of independent variables
If two variables, x and y, have a strong linear relationship, then:
there may or may not be any causal relationship between x and y
In regression analysis, the independent variable is:
used to predict the dependent variable
In a regression analysis, the error term E is a random variable with a mean or expected value of:
zero
