Bus Analytics ch. 7 quiz

__________ is used to test the hypothesis that the values of the regression parameters ß1, ß2, ... ßq are all zero. A- An F test B- The least squares method C- Extrapolation D- A t test

A- An F test

In a linear regression model, the variable that is being predicted or explained is known as _____________. It is denoted by y and is often referred to as the response variable. A- dependent variable B- independent variable C- residual variable D- regression variable

A- dependent variable

The coefficient of determination A- is used to evaluate the goodness of fit. B- takes values between -1 to +1. C- is equal to negative one for the poorest fit. D- is equal to zero for a perfect fit.

A- is used to evaluate the goodness of fit.

The degree of correlation among independent variables in a regression model is called A- multicollinearity. B- interaction. C- the coefficient of determination. D- the sum of squared errors (SSE).

A- multicollinearity.

The difference between the observed value of the dependent variable and the value predicted using the estimated regression equation is known as the A- residual. B- Random error C- model parameter. D- constant term.

A- residual.

A __________ is used to visualize sample data graphically and to draw preliminary conclusions about the possible relationship between the variables. A- scatter chart B- pie chart C- contingency table D- Gantt chart

A- scatter chart

A regression analysis involving one independent variable and one dependent variable is referred to as a A- simple linear regression. B- data mining. C- time series analysis. D- factor analysis.

A- simple linear regression.

__________ is a statistical procedure used to develop an equation showing how two variables are related. A- Factor analysis B- Regression analysis C- Time series analysis D- Data mining

B- Regression analysis

The __________ is the range of values of the independent variables in the data used to estimate the regression model. A- confidence interval B- codomain C- experimental region D- validation set

B- codomain

In the simple linear regression model, the ____________ accounts for the variability in the dependent variable that cannot be explained by the linear relationship between the variables. A- constant term B- error term C- model parameter D- residual

B- error term

In a linear regression model, the variable (or variables) used for predicting or explaining values of the response variable are known as the __________. It(they) is(are) denoted by x. A- dependent variable B- independent variable C- residual variable D- regression variable

B- independent variable

The process of making estimates and drawing conclusions about one or more characteristics of a population through analysis of sample data drawn from the population is known as A- deductive inference. B- statistical inference. C- Bayesian inference. D- inductive inference.

B- statistical inference.

When the mean value of the dependent variable is independent of variation in the independent variable, the slope of the regression line is A- infinite. B- zero. C- positive. D- negative.

B- zero.

Prediction of the mean value of the dependent variable y for values of the independent variables x1, x2, . . . , xq that are outside the experimental range is called A- dummy variable. B- overfitting. C- extrapolation. D- interaction.

C- extrapolation.

The process of making a conjecture about the value of a population parameter, collecting sample data that can be used to assess this conjecture, measuring the strength of the evidence against the conjecture that is provided by the sample, and using these results to draw a conclusion about the conjecture is known as A- statistical inference. B- postulation. C- hypothesis testing. D- empirical research.

C- hypothesis testing.

Regression analysis involving one dependent variable and more than one independent variable is known as A- simple regression. B- linear regression. C- multiple regression. D- None of these are correct.

C- multiple regression.

The __________ is a measure of the error that results from using the estimated regression equation to predict the values of the dependent variable in the sample. A- residual B- sum of squares due to regression (SSR) C- sum of squares due to error (SSE) D- error term

C- sum of squares due to error (SSE)

_________ refers to the use of sample data to calculate a range of values that is believed to include the value of the population parameter. A- Statistical inference B- Hypothesis testing C- Point estimation D- Interval estimation

D- Interval estimation

__________ refers to the degree of correlation among independent variables in a regression model. A- Rank B- Confidence level C- Tolerance D- Multicollinearity

D- Multicollinearity

Prediction of the value of the dependent variable outside the experimental region is called A- averaging. B- interpolation. C- forecasting. D- extrapolation.

D- extrapolation.

In the graph of the simple linear regression equation, the parameter ß1 is the ___________ of the true regression line. A- end-point B- y-intercept C- x-intercept D- slope

D- slope

In a simple linear regression model, y = ß0 + ß1x + ε the parameter ß1 represents the A- mean value of x. B- intercept. C- error term. D- slope of the true regression line.

D- slope of the true regression line.

The least squares regression line minimizes the sum of the A- differences between actual and predicted y values. B- absolute deviations between actual and predicted y values. C- absolute deviations between actual and predicted x values. D- squared differences between actual and predicted y values.

D- squared differences between actual and predicted y values.

The graph of the simple linear regression equation is a(n) A- parabola. B- hyperbola. C- ellipse. D- straight line.

D- straight line.

In the graph of the simple linear regression equation, the parameter ß0 represents the ___________ of the true regression line. A- end-point B- slope C- x-intercept D- y-intercept

D- y-intercept