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CPT Section D Quantitative Aptitude Chapter12 
CA.Dharmendra Gupta  
Page 2


CPT Section D Quantitative Aptitude Chapter12 
CA.Dharmendra Gupta  
Regression is the measure of 
average relationship between 
two or more variables in terms 
of original units of the data 
Page 3


CPT Section D Quantitative Aptitude Chapter12 
CA.Dharmendra Gupta  
Regression is the measure of 
average relationship between 
two or more variables in terms 
of original units of the data 
Regression analysis is a statistical tool 
to study the nature and extent of 
functional relationship between two or 
more variables and to estimate the 
unknown values of dependent variable. 
Page 4


CPT Section D Quantitative Aptitude Chapter12 
CA.Dharmendra Gupta  
Regression is the measure of 
average relationship between 
two or more variables in terms 
of original units of the data 
Regression analysis is a statistical tool 
to study the nature and extent of 
functional relationship between two or 
more variables and to estimate the 
unknown values of dependent variable. 
• The Variable Which is predicted 
on the basis of another variable 
is called  Dependent variable or 
explained variable  
Dependent 
variable : 
• :The Variable Which is used to  
predict another variable is called 
independent variable or 
explanatory variable   
Independent 
variable 
Page 5


CPT Section D Quantitative Aptitude Chapter12 
CA.Dharmendra Gupta  
Regression is the measure of 
average relationship between 
two or more variables in terms 
of original units of the data 
Regression analysis is a statistical tool 
to study the nature and extent of 
functional relationship between two or 
more variables and to estimate the 
unknown values of dependent variable. 
• The Variable Which is predicted 
on the basis of another variable 
is called  Dependent variable or 
explained variable  
Dependent 
variable : 
• :The Variable Which is used to  
predict another variable is called 
independent variable or 
explanatory variable   
Independent 
variable 
1.Regression line facilitates to predict the 
values of a dependent variable from the given 
value of independent variable. 
2.Through Standard Error facilitates to obtain 
a measure of the error involved in using the 
regression line as basis for estimation. 
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FAQs on PPT - Regression - Quantitative Aptitude for CA Foundation

1. What is regression analysis and how is it used in CA Foundation?
Ans. Regression analysis is a statistical technique used to model the relationship between a dependent variable and one or more independent variables. In CA Foundation, regression analysis is used to analyze and predict financial and economic data, helping in making informed decisions and understanding the impact of various factors on financial outcomes.
2. How is regression analysis different from correlation analysis?
Ans. While both regression analysis and correlation analysis deal with the relationship between variables, they have some key differences. Regression analysis focuses on modeling the relationship between a dependent variable and one or more independent variables, allowing for prediction and understanding of causality. Correlation analysis, on the other hand, measures the strength and direction of the linear relationship between two variables without establishing causality.
3. What are the assumptions of regression analysis in CA Foundation?
Ans. Regression analysis in CA Foundation relies on certain assumptions for accurate interpretation and reliable results. These assumptions include linearity, independence of errors, homoscedasticity (constant variance of errors), absence of multicollinearity (no high correlation between independent variables), and normality of errors. Violation of these assumptions may affect the validity of the regression analysis results.
4. How can regression analysis be used in financial forecasting for CA Foundation?
Ans. Regression analysis can be a useful tool for financial forecasting in CA Foundation. By analyzing historical financial data and identifying significant independent variables, regression models can be built to predict future financial outcomes. This can assist in budgeting, investment decision-making, and evaluating the impact of various economic factors on financial performance.
5. What are the limitations of regression analysis in CA Foundation?
Ans. While regression analysis is a valuable statistical technique, it also has its limitations. Some limitations include the assumption of a linear relationship between variables, sensitivity to outliers, potential multicollinearity issues, and the need for a sufficient number of observations. Additionally, regression analysis may not capture all relevant factors influencing financial outcomes, and its predictions are subject to uncertainty.
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