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The method applied for deriving the regression equations is known as
- a)Least squares
- b)Concurrent deviation
- c)Product moment
- d)Normal equation
Correct answer is option 'A'. Can you explain this answer?
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The method applied for deriving the regression equations is known asa)...
Explanation:
The method applied for deriving the regression equations is known as Least squares. It is a statistical method for finding the best-fit curve to a given set of data points by minimizing the sum of the squares of the deviations between the observed values and the predicted values. The regression equation is used to predict the value of a dependent variable based on the value of one or more independent variables. The least squares method is widely used in statistical analysis and data science to model the relationship between variables.
Steps involved in the least squares method:
The method applied for deriving the regression equations is known as Least squares. It is a statistical method for finding the best-fit curve to a given set of data points by minimizing the sum of the squares of the deviations between the observed values and the predicted values. The regression equation is used to predict the value of a dependent variable based on the value of one or more independent variables. The least squares method is widely used in statistical analysis and data science to model the relationship between variables.
Steps involved in the least squares method:
- Step 1: Collect the data points for which the regression equation is to be derived.
- Step 2: Plot the data points on a graph to visualize the relationship between the variables.
- Step 3: Choose a mathematical model that represents the relationship between the variables. For example, a linear regression model can be represented as Y = a + bX, where Y is the dependent variable, X is the independent variable, a is the intercept, and b is the slope.
- Step 4: Calculate the deviations between the observed values and the predicted values for each data point.
- Step 5: Square each deviation and sum them up to get the sum of the squares of the deviations. This is known as the residual sum of squares (RSS).
- Step 6: Calculate the coefficients (a and b) of the mathematical model that minimize the residual sum of squares. This can be done using calculus.
- Step 7: Write the regression equation using the calculated coefficients.
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The method applied for deriving the regression equations is known asa)Least squaresb)Concurrent deviationc)Product momentd)Normal equationCorrect answer is option 'A'. Can you explain this answer?
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The method applied for deriving the regression equations is known asa)Least squaresb)Concurrent deviationc)Product momentd)Normal equationCorrect answer is option 'A'. Can you explain this answer? for CA Foundation 2025 is part of CA Foundation preparation. The Question and answers have been prepared according to the CA Foundation exam syllabus. Information about The method applied for deriving the regression equations is known asa)Least squaresb)Concurrent deviationc)Product momentd)Normal equationCorrect answer is option 'A'. Can you explain this answer? covers all topics & solutions for CA Foundation 2025 Exam. Find important definitions, questions, meanings, examples, exercises and tests below for The method applied for deriving the regression equations is known asa)Least squaresb)Concurrent deviationc)Product momentd)Normal equationCorrect answer is option 'A'. Can you explain this answer?.
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