Quiz
Linear Regression
Questions in this quiz (13)
1Regression analysis is a form of predictive modelling technique which investigates the relationship between a dependent and independent variable.
- ATrue
- BFalse
2Linear regression is a basic and commonly used type of predictive analysis which usually works on continuous data.
- ATrue
- BFalse
3Explain the equation: Y(predicted) = (β1*x + βo) + Error value
- Awrite your answers down
- Bcheck them later
4Explain the equation
- Awrite your answer down
- Bcheck time later
5The main goal of Gradient descent is to minimize the cost value. i.e. min J(θo, θ1)
- ATrue
- BFalse
6Choosing a perfect learning rate is a very important task as it depends on how large of a step we take downhill during each iteration.
- ATrue
- BFalse
7This general equation is for?
- ALinear Regression
- BPolynomial Regression
8Advantages of using Polynomial Regression are:
- APolynomial provides the best approximation of the relationship between the dependent and independent variables.
- BA broad range of functions can be fit under it.
- CPolynomial basically fits a wide range of curvature.
- DAll of the above.
9Disadvantages of using Polynomial Regression are:
- AThe presence of one or two outliers in the data can seriously affect the results of the nonlinear analysis.
- BThese are too sensitive to the outliers.
- CIn addition, there are unfortunately fewer model validation tools for the detection of outliers in nonlinear regression than there are for linear regression.
- DAll of the above.
10simple linear regression is a type of regression analysis where the number of independent variables is ____ and there is a linear relationship between the independent(x) and dependent(y) variable.
- Aone
- Btwo
11Residual plot helps in analyzing the model using the values of residues. It is plotted between predicted values and residue. Their values are standardized. The distance of the point from 0 specifies how bad the prediction was for that value. If the value is positive, then the prediction is low. If the value is negative, then the prediction is high. 0 value indicates prefect prediction. Detecting residual pattern can improve the model.
- ATrue
- BFalse
12Non-random pattern of the residual plot indicates that the model is,
- AMissing a variable which has significant contribution to the model target
- BMissing to capture non-linearity (using polynomial term)
- CNo interaction between terms in model
- DAll of the above
13Characteristics of a residue are:
- AResiduals do not exhibit any pattern
- BAdjacent residuals should not be same as they indicate that there is some information missed by system.
- CAll of the above
Information
- Description
- Introduction to Linear Regression
- Author
- Vishakha Achmare
- Language
- English
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- 123
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