Quiz
Linear Regression
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Questions in this quiz (34)
1If data can be obtained, a statistical procedure called [blank_start]regression analysis[blank_end] can be used to develop an equation showing how the variables are related
- Aregression analysis
2[blank_start]Dependent variable[blank_end]: Variable being predicted
- ADependent variable
3[blank_start]Independent variable[blank_end]s: Variables being used to predict the value of the dependent variable
- AIndependent variable
4[blank_start]Linear regression[blank_end]: A regression analysis involving one independent variable and one dependent variable
- ALinear regression
5The graph of the estimated simple linear regression equation is called the [blank_start]estimated regression line[blank_end]
- Aestimated regression line
6[blank_start]Regression Model[blank_end]: The equation that describes how y is related to x and an error term
- ARegression Model
7[blank_start]Simple linear regression[blank_end]: A regression analysis for which any one unit change in the independent variable, x, is assumed to result in the same change in the dependent variable, y
- ASimple linear regression
8[blank_start]Multiple linear regression[blank_end]: Regression analysis involving two or more independent variables
- AMultiple linear regression
9[blank_start]residual[blank_end]: The error made using the regression model to estimate the mean value of the dependent variable for the ith observation
- Aresidual
10[blank_start]Least squares method[blank_end]: A procedure for using sample data to find the estimated regression equation
- ALeast squares method
11[blank_start]Extrapolation[blank_end]: Prediction of the value of the dependent variable outside the experimental region
- AExtrapolation
12[blank_start]Experimental region[blank_end]: The range of values of the independent variables in the data used to estimate the model
- AExperimental region
13[blank_start]Sum of squares due to error[blank_end]: The value of SSE is a measure of the error in using the estimated regression equation to predict the values of the dependent variable in the sample
- ASum of squares due to error
14[blank_start]Coefficient of Determination[blank_end] or ratio SSR/SST used to evaluate the goodness of fit for the estimated regression equation
- ACoefficient of Determination
15The corresponding sum of squares is called the [blank_start]total sum of squares[blank_end]
- Atotal sum of squares
16[blank_start]Statistical inference[blank_end]: Process of making estimates and drawing conclusions about one or more characteristics of a population (the value of one or more parameters) through the analysis of sample data drawn from the population
- AStatistical inference
17If a βj = 0, there is [blank_start]no linear relationship[blank_end] between the dependent variable y and the independent variable xj
- Ano linear relationship
18If a βj ≠ 0, there is a [blank_start]linear relationship[blank_end] between y and xj
- Alinear relationship
19[blank_start]Confidence level[blank_end]: Indicates how frequently interval estimates based on samples of the same size taken from the same population using identical sampling techniques will contain the true value of the parameter we are estimating
- AConfidence level
20[blank_start]Confidence interval[blank_end]: An estimate of a population parameter that provides an interval believed to contain the value of the parameter at some level of confidence
- AConfidence interval
21Use a [blank_start]t test[blank_end] to test the hypothesis that a regression parameter βj is zero
- At test
22[blank_start]Multicollinearity[blank_end] refers to the correlation among the independent variables in multiple regression analysis
- AMulticollinearity
23[blank_start]Piecewise linear regression model[blank_end]: This model will allow us to fit these relationships as two linear regressions that are joined at the value of Months at which the relationship between Months Employed and Sales changes
- APiecewise linear regression model
24[blank_start]Knot[blank_end]: The value of the independent variable at which the relationship between dependent variable and independent variable changes
- AKnot
25[blank_start]Interaction[blank_end]: This occurs when the relationship between the dependent variable and one independent variable is different at various values of a second independent variable
- AInteraction
26[blank_start]Training set[blank_end]: The data set used to build the candidate models that appear to make practical sense
- ATraining set
27[blank_start]Validation set[blank_end]: The set of data used to compare model performances and ultimately pick a model for predicting values of the dependent variable
- AValidation set
28[blank_start]Holdout method[blank_end]: The sample data are randomly divided into mutually exclusive and collectively exhaustive training and validation sets
- AHoldout method
29[blank_start]Leave-one-out cross-validation[blank_end]: For a sample of n observations, an iteration consists of estimating the model on n – 1 observations and evaluating the model on the single observation that was omitted from the training data
- ALeave-one-out cross-validation
30[blank_start]k-fold cross-validation[blank_end]: The sample data are randomly divided into k equal-sized, mutually exclusive, and collectively exhaustive subsets called fold, and k iterations are executed
- Ak-fold cross-validation
31What is the equation?
- Ab1>0, b2>0
- Bb1>0, b2<0
- Cb1<0, b2>0
- Db1<0, b2<0
32What is the equation?
- Ab1>0, b2>0
- Bb1<0, b2>0
- Cb1>0, b2<0
- Db1<0, b2<0
33What is the equation?
- Ab1>0, b2>0
- Bb1<0, b2>0
- Cb1>0, b2<0
- Db1<0, b2<0
34What is the equation?
- Ab1>0, b2>0
- Bb1<0, b2>0
- Cb1>0, b2<0
- Db1<0, b2<0
Information
- Description
- Quiz on Linear Regression, created by Allona Mae Dapeg on 17/12/2021.
- Author
- Allona Mae Dapeg
- Language
- English
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- 1
- History
- Created on 17 Dec 2021 · updated on 17 Dec 2021
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