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Page 1 CPT Section D Quantitative Aptitude Chapter12 CA.Dharmendra Gupta Page 2 CPT Section D Quantitative Aptitude Chapter12 CA.Dharmendra Gupta MCQ’s Page 3 CPT Section D Quantitative Aptitude Chapter12 CA.Dharmendra Gupta MCQ’s (a) independent of both change of scale and origin (b) independent of the change of scale and not of origin (c)independent of the change of origin and not of scale (d) neither independent of change of scale nor of origin Answer:c Page 4 CPT Section D Quantitative Aptitude Chapter12 CA.Dharmendra Gupta MCQ’s (a) independent of both change of scale and origin (b) independent of the change of scale and not of origin (c)independent of the change of origin and not of scale (d) neither independent of change of scale nor of origin Answer:c (a) the changes in y corresponding to a unit change in ‘x’ (b) the changes in x corresponding to a unit change in ‘y’ (c) the changes in xy (d) the changes in yx Answer:b Page 5 CPT Section D Quantitative Aptitude Chapter12 CA.Dharmendra Gupta MCQ’s (a) independent of both change of scale and origin (b) independent of the change of scale and not of origin (c)independent of the change of origin and not of scale (d) neither independent of change of scale nor of origin Answer:c (a) the changes in y corresponding to a unit change in ‘x’ (b) the changes in x corresponding to a unit change in ‘y’ (c) the changes in xy (d) the changes in yx Answer:b (a) r 2 = 1– (b) r 2 = ( c) both (d) none of these Answer: cRead More
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FAQs on MCQ - Regression - Quantitative Aptitude for CA Foundation
| 1. What is regression analysis? |  |
Ans. Regression analysis is a statistical method used to examine the relationship between a dependent variable and one or more independent variables. It helps in understanding how the independent variables influence the dependent variable and can be used to predict future outcomes.
| 2. How is regression analysis different from correlation analysis? | |
Ans. While both regression analysis and correlation analysis measure the relationship between variables, they serve different purposes. Regression analysis focuses on predicting the value of the dependent variable based on the independent variables, whereas correlation analysis measures the strength and direction of the relationship between variables without predicting values.
| 3. What are the assumptions of regression analysis? | |
Ans. Regression analysis relies on certain assumptions, including linearity, independence of errors, homoscedasticity (constant variance of errors), absence of multicollinearity (no perfect correlation between independent variables), and normality of errors. Violations of these assumptions can affect the validity of the regression results.
| 4. What is the significance of the coefficient of determination (R-squared) in regression analysis? | |
Ans. The coefficient of determination, denoted as R-squared, measures the proportion of the variance in the dependent variable that can be explained by the independent variables in a regression model. It ranges from 0 to 1, where a higher value indicates a better fit of the model to the data. R-squared helps assess the goodness of fit of the regression model.
| 5. How can regression analysis be used in business decision-making? | |
Ans. Regression analysis is widely used in business decision-making. It can help in forecasting sales, understanding the factors influencing customer behavior, optimizing marketing strategies, pricing analysis, risk assessment, and many other areas. By analyzing the relationships between variables, regression analysis provides valuable insights for making informed business decisions.
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Document Description: MCQ - Regression for CA Foundation 2024 is part of Quantitative Aptitude for CA Foundation preparation.
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