📚 Part of: Ancient Indian History & Environmental Changes Mcqs

Measurements (in centimeters) on young children in Mumbai, India, found this least-squares line for predicting height y from arm span x:By Looking at the equation of the least squares regression line, you can see that the correlation between height and arm span is

Category: Miscellaneous Indian Gk

Correct Answer: A) Greater than zero.

Exam Relevance: UPSC, SSC, Banking Exams, MBA Entrance Exams

Difficulty: Easy

Concept notes:

The least squares regression line is used to predict one variable (y) from another (x) based on a linear relationship. The slope of this line indicates the direction of the relationship between the two variables. A positive slope suggests a positive correlation, meaning as one variable increases, the other tends to increase as well.

Common Mistakes:
  • Confusing the slope of the regression line with the correlation coefficient.
  • Assuming that the correlation coefficient is the same as the slope of the regression line.
  • Misinterpreting the sign of the slope as indicating the strength of the relationship rather than the direction.
Explanation:

In the context of regression analysis, the least squares regression line is a statistical tool used to predict the value of one variable (y) based on the value of another variable (x). The line is determined by minimizing the sum of the squared differences between the observed values and the predicted values. The equation of the least squares regression line is typically written as \( y = mx + b \), where \( m \) is the slope and \( b \) is the y-intercept.

The slope \( m \) of the regression line is crucial in understanding the relationship between the two variables. A positive slope indicates a positive correlation, meaning that as the value of \( x \) (arm span) increases, the value of \( y \) (height) also tends to increase. Conversely, a negative slope would indicate a negative correlation, where an increase in \( x \) is associated with a decrease in \( y \).

In this specific case, the least squares regression line for predicting height from arm span in young children in Mumbai has a positive slope. This positive slope directly implies that there is a positive correlation between height and arm span. Therefore, the correlation between height and arm span is greater than zero.

It is important to note that while the slope of the regression line indicates the direction of the relationship, it does not provide information about the strength of the relationship. The strength of the relationship is measured by the correlation coefficient, which ranges from -1 to 1. A correlation coefficient of 1 indicates a perfect positive linear relationship, while a correlation coefficient of -1 indicates a perfect negative linear relationship. A correlation coefficient of 0 indicates no linear relationship.

In summary, the least squares regression line with a positive slope indicates a positive correlation between height and arm span, making the correct answer that the correlation is greater than zero. This concept is fundamental in understanding the relationship between two variables and is often tested in various competitive examinations in India, including UPSC, SSC, and MBA entrance exams.

Option Analysis:
  • Option A: This option is correct. The least squares regression line has a positive slope, which indicates a positive correlation between height and arm span. This means that as arm span increases, height also tends to increase, and vice versa. The positive slope suggests that the two variables move in the same direction, hence the correlation is greater than zero.
  • Option B: This option is incorrect. A negative correlation would imply that as one variable increases, the other decreases. Since the least squares regression line has a positive slope, this indicates a positive relationship, not a negative one. Therefore, the correlation cannot be less than zero.
  • Option C: This option is incorrect. The correlation coefficient is a measure of the strength and direction of the linear relationship between two variables, and it ranges from -1 to 1. The value 0.93 is a specific correlation coefficient, but the question does not provide enough information to determine the exact value of the correlation coefficient. The question only asks about the sign of the correlation, which is positive based on the positive slope of the regression line.
  • Option D: This option is incorrect. The value 6.4 does not represent a correlation coefficient, as correlation coefficients must be between -1 and 1. This value could potentially be the slope of the regression line, but it does not indicate the sign of the correlation. The question is specifically asking about the sign of the correlation, which is positive based on the positive slope of the regression line.
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