Curve Fitting MCQ Quiz Questions And Answers

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Curve Fitting MCQ Quiz Questions And Answers - Quiz

Curve fitting is a type of regression analysis. Test your knowledge about the concept with our Curve Fitting MCQ Quiz Questions and Answers. The concept is used to make the model that helps provide the best curves to your dataset. Are you ready for the test? Please answer the questions correctly to get a good score. You can find out your score at the end of the quiz. The quiz can also help prepare you for your test. Good luck & get ready to learn!


Questions and Answers
  • 1. 

    The method of least squares finds the best fit line that _____ the error between observed and estimated points on the line

    • A.

      Maximize

    • B.

      Minimize

    • C.

      Reduces to zero

    • D.

      Approaches to infinity

    Correct Answer
    B. Minimize
    Explanation
    The method of least squares finds the best fit line that minimizes the error between observed and estimated points on the line. This means that it aims to reduce the overall difference between the observed data points and the estimated values on the line, making it the best possible fit. By minimizing the error, the method of least squares ensures that the line is as close as possible to the actual data points.

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  • 2. 

    The normal equation for fitting of a straight line y = a + bx is ∑ y =_____ _

    • A.

      Na + b∑x

    • B.

      N^2a + b∑ x^2

    • C.

      Na + b∑ x^2

    • D.

      A + b∑ x

    Correct Answer
    A. Na + b∑x
    Explanation
    The normal equation for fitting a straight line y = a + bx is given by na + b∑x. This equation represents the sum of the y-values (∑y) being equal to the product of the number of data points (n) and the y-intercept (a), plus the product of the slope (b) and the sum of the x-values (∑x).

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  • 3. 

    If the regression equation is equal to Y=23.6−54.2X, then 23.6 is the _____ while -54.2 is the ____ of the regression line

    • A.

      Slope,intercept

    • B.

      Radius, intercept

    • C.

      Intercept,slope

    • D.

      Intercept,regression coefficient

    Correct Answer
    C. Intercept,slope
    Explanation
    In a regression equation, the coefficient of the independent variable (X) is known as the slope, while the constant term (Y-intercept) is known as the intercept. In this case, the regression equation Y=23.6-54.2X indicates that 23.6 is the intercept and -54.2 is the slope of the regression line.

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  • 4. 

    In the model Y=mX+a, Y is also known as the:

    • A.

      Independent variable

    • B.

      Predictor variable

    • C.

      Explanatory variable

    • D.

      Predicted (dependent) variable

    Correct Answer
    D. Predicted (dependent) variable
    Explanation
    In the given model Y=mX+a, Y is the predicted (dependent) variable. This means that the value of Y is dependent on the value of X, which is the independent variable. The equation represents a linear relationship between the independent variable X and the predicted variable Y, where m represents the slope of the line and a represents the y-intercept. The predicted variable Y is the variable that is being estimated or predicted based on the values of the independent variable X.

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  • 5. 

    The normal equations obtained after applying least square criteria in curve fitting can be solved by

    • A.

      Gauss Elimination method

    • B.

      Newton raphson method

    • C.

      Cramers rule

    • D.

      Eulers method

    Correct Answer(s)
    A. Gauss Elimination method
    C. Cramers rule
    Explanation
    The normal equations obtained after applying the least square criteria in curve fitting can be solved by Gauss Elimination method and Cramer's rule. The Gauss Elimination method is a systematic way of solving a system of linear equations by eliminating variables. Cramer's rule is a method that uses determinants to solve a system of linear equations. Both methods can be used to find the solutions to the normal equations and determine the coefficients of the curve fitting equation.

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  • 6. 

    WHAT TYPE OF MODEL SEEMS APPROPRAITE FOR EACH OF THE 3 ( IN order from left to Right1

    • A.

      POWER,EXPONENTIAL,LINEAR

    • B.

      POWER,LINEAR,EXPONENTIAL

    • C.

      EXPONENTIAL,POWER,LINEAR

    • D.

      EXPONENTIAL,LINEAR,POWER

    Correct Answer
    D. EXPONENTIAL,LINEAR,POWER
    Explanation
    The given answer suggests that an exponential model seems appropriate for the first set of data, a linear model seems appropriate for the second set of data, and a power model seems appropriate for the third set of data. This implies that the relationship between the variables in the first set can be described by an exponential function, the relationship in the second set can be described by a linear function, and the relationship in the third set can be described by a power function.

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  • 7. 

    The normal equation for fitting of a straight line y = ax + b is ∑xy =_____ _

    • A.

       =a∑ x+nb

    • B.

      =a∑x^2 +b∑x

    • C.

      =a∑x^2 +b∑y

    • D.

      =a∑x +b∑x^2

    Correct Answer
    B. =a∑x^2 +b∑x
    Explanation
    The given equation is the result of applying the normal equation for fitting a straight line to the data. The equation is in the form of ∑xy = a∑x^2 + b∑x, where ∑xy represents the sum of the products of x and y values, ∑x represents the sum of all x values, ∑x^2 represents the sum of the squares of x values, a is the slope of the line, and b is the y-intercept of the line. Therefore, the correct answer is =a∑x^2 + b∑x.

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  • 8. 

    A process by which we estimate the value of dependent variable on the basis of one or more independent variables is called:

    • A.

      Residual

    • B.

      Slope

    • C.

      Regression

    • D.

      Correlation

    Correct Answer
    C. Regression
    Explanation
    Regression is the correct answer because it refers to the process of estimating the value of a dependent variable based on one or more independent variables. In regression analysis, a mathematical equation is used to model the relationship between the dependent variable and the independent variables, allowing for predictions to be made about the dependent variable based on the values of the independent variables.

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  • 9. 

    The value of coefficient of correlation lies between

    • A.

      -1 to 1

    • B.

      -1 to 0

    • C.

      91 to 0

    • D.

      0 to -1

    Correct Answer
    A. -1 to 1
    Explanation
    The coefficient of correlation measures the strength and direction of the linear relationship between two variables. It can range from -1 to 1, where -1 indicates a perfect negative correlation, 0 indicates no correlation, and 1 indicates a perfect positive correlation. Therefore, the correct answer is -1 to 1.

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  • Current Version
  • Jun 26, 2023
    Quiz Edited by
    ProProfs Editorial Team
  • Apr 15, 2020
    Quiz Created by
    Amit Bhagure
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