Question
Interpret the slope of the regression line estimated above.
Answer :
Word Count : 1193
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Skip in 10
The slope of a regression line is one of the most important measures used in regression analysis because it explains the direction and magnitude of the relationship between two variables. In Business Mathematics and Statistics, regression analysis is commonly used to estimate the value of one variable on the basis of another variable. The regression line provides a mathematical relationship between the independent variable and the dependent variable, while its slope indicates how much the dependent variable is expected to change when the independent variable changes by one unit. A simple regression equation is generally written as: [ Y=a+bX ] where (Y) is the dependent variable, (X) is the independent variable, (a) is the intercept, and (b) is the slope or regression coefficient. The slope (b) is particularly important because it measures the average change in (Y) corresponding to a one-unit change in (X). The slope of the estimated regression line can be calculated using: [ b=\frac{\sum (X-\bar X)(Y-\bar Y)}{\sum (X-\bar X)^2} ] Once the regression equation has been estimated, the numerical value of (b) provides the basis for interpretation. For example, if the estimated regression equation is: [ Y=20+5X ] then the slope is (5). This means that for every one-unit increase in (X), the estimated or predicted value of (Y) increases by 5 units, on average, assuming other relevant factors remain unchanged. If the regression line estimated above has a positive slope, it indicates a positive relationship between the independent and dependent variables. In such a situation, an increase in the independent variable is associated with an increase in the dependent variable. For instance, if (X) represents advertising expenditure and (Y) represents sales, a positive slope suggests that higher advertising expenditure is associated with higher sales. If the slope is 4, the interpretation would be that an increase of one unit in advertising expenditure is associated, on average, with an increase of 4 units in sales. The units of measurement are important when interpreting the slope. Suppose the independent variable represents advertising expenditure measured in thousands of rupees and the dependent variable represents sales
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