BIA 261: Exam 4 Review

Dummy variables usually assume the values of ______.
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Dummy variables usually assume the values of ______.
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Consider the model y = β0 + β1x + β2D + ε, where x is a quantitative variable and D is a dummy variable. For D = 1, the predicted value of y is computed as ______
Y- hat= (b0 + b2) + b1x
dummy variable
A variable that takes on the values 0 or 1 and is used to describe the effects of the different levels of a qualitative independent variable in a regression model.
Line graphs of y versus x1, for two different levels of x2, are plotted on the same grid. How can we determine if interaction exists?
Interaction exists if the lines are not parallel.
Consider the model y = β0 + β1x + β2D + β3xD + ε, where x is a quantitative variable and D is a dummy variable. For D = 1, the predicted value of y is computed as ______
Y-hat = (b0 + b2) + (b1 + b3)x
If lines have different _________ there is an interaction
Slopes
If a qualitative independent variable has four levels, how many dummy variables are needed to represent these levels in a regression model?
3 dummy variables
A quadratic regression model is a type of _______.
Nonlinear regression
When would it be appropriate to use a quadratic regression model?
When a scatter plot shows points that approximate a curve.
When there is an interaction between x1 and x2, we consider the model ______
y = β0 + β1x1 + β2x2 + β3x1x2 + ε
If the relationship between the mean value of the dependent variable and one of the independent variables is dependent on the value of another independent variable, we model this by multiplying the two independent variables together to form a(n) ______________ term for our model.InteractionWhich of the following is the quadratic regression model relating y to x?y = β0 + β1x + β2x2 + εWhen a scatter plot shows points that approximate a curve, a ________ regression model should be considered.QuadraticWhen an interaction exists between x1 and x2, we can create an interaction variable by ______ them.MultiplyingIf the relationship between the mean value of the dependent variable and an independent variable is dependent on the value of another independent variable, we have ______.InteractionWhat type of regression model is y = β0 + β1x + β2x2 + ε?Quadratic regression modelWe form an interaction variable by multiplying two independent variables together. For instance x1 and x2 makes...x1x2Suppose a bicycle manufacturer wants to predict y, a rider's comfort on a bike, from arm length, leg length, and overall height. Because of the proportionality in the human body, we should expect that a regression model based on these three independent variables will be characterized byMulticolinearityGiven a correlation matrix, multicollinearity is considered severe if at least one simple correlation coefficient between independent variables is ______.At least .9Given the variance inflation factors, multicollinearity is considered severe if ______.the largest variance inflation factor is greater than 10Why can multicollinearity make it difficult to analyze a multiple linear regression model?Multicollinearity can make some of the independent variables seem less helpful in explaining y than they really are; It makes it difficult to interpret the signs and sizes of the individual bi values.Suppose you are considering two different models for predicting y. Model #1 uses independent variables x1, x2, and x3 and has SSE = 10.8. Model #2 uses the 3 independent variables from #1 plus a fourth variable x4. Model #2 has SSE = 4.7. Both models are based on the same data set with n = 25 and total sum of squares = 122. Which is correct?In Model #2, R2 =.961 and adjusted R2 =.954; It appears x4 contains useful information about y not already found in x1, x2, and x3.Multicollinearity exists when ______Independent variables are correlated.When strong multicollinearity exists, how does it affect the t statistics?They have smaller absolute values.Suppose you are considering two different models for predicting y. Model #1 uses 3 independent variables and has SSE = 103.5. Model #2 uses the 3 independent variables from #1 plus a fourth variable x4. Model #2 has SSE = 98.7. Both models are based on the same data set with n = 20 and total sum of squares = 1050. Which statement(s) below is(are) correct?Model #1 will have the smaller value of s; Model #1 has a higher adjusted R2 than Model #2, so we might prefer Model #1.If s increases, this tells us we shouldNot add the independent variable to the model.If a time series consists of quarterly observations that exhibit variations that repeat from year to year, then it is likely that a _____ component exists in the time series.SeasonalNancy is the manager of a gas station in Cleveland, Ohio. On January 1st, she collected data on the price of a gallon of gas from every gas station in Cleveland. True or false: In this example Nancy collected time series data.False, cross sectionalWhich of the following represents seasonal variation in a time series?Attendance at amusement parksSuppose that a courier service has seen its number of courier trips per month increase in a linear fashion for the last two years. If it anticipates this growth pattern will continue for the foreseeable future, what sort of time series model would be appropriate?Linear trend regression modelWhich model is appropriate for a quarterly time series that seems to change on average by a fixed amount each period and appears to have a seasonal component?yt=β0+β1d1+β2d2+β3d3+β4t+εt