How can I use the expression for a in this problem

In summary, the conversation is discussing how to show that if the regression line of y on x passes through the origin of its scatter diagram, then the equation for the line can be simplified to $$\bar y \sum^n_{r=1} x_r^2=\bar x\sum^n_{r=1} x_r y_r$$ where (\bar x, \bar y) is the mean point of the sample. The approach mentioned is to consider the expressions for the linear coefficients a and b, and show that if a=0, then the desired result can be obtained.
  • #1
Faiq
348
16

Homework Statement



A random sample of size ##n## from a bivariate distribution is denoted by ##(x_r,y_r), r=1,2,3,...,n##. Show that if the regression line of ##y## on ##x## passes through the origin of its scatter diagram then[/B]
$$\bar y \sum^n_{r=1} x_r^2=\bar x\sum^n_{r=1} x_r y_r$$ where ## (\bar x,\bar y)## is the mean point of the sample.

I don't really know how to begin. I am aware the line equation is $$b=\frac{y}{x}=\frac{\sum xy-\frac{\sum x\sum y}{n}}{\sum x^2-\frac{(\sum x)^2}{n}}$$

Not sure what to do next.
 
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  • #2
Faiq said:

Homework Statement



A random sample of size ##n## from a bivariate distribution is denoted by ##(x_r,y_r), r=1,2,3,...,n##. Show that if the regression line of ##y## on ##x## passes through the origin of its scatter diagram then[/B]
$$\bar y\sum^n_{r=1} x_r^2=\bar x\sum^n_{r=1} x_r y_r$$ where ## (\bar x,\ bar y)## is the mean point of the sample.

I don't really know how to begin. I am aware the line equation is $$b=\frac{y}{x}=\frac{\sum xy-\frac{\sum x\sum y}{n}}{\sum x^2-\frac{(\sum x)^2}{n}}$$

Not sure what to do next.
(1) Do not write ##\xbar##, write ##\bar{x}##. Right-click on the formula to see its TeX commands.
Mod note: Fixed the TeX in the original post and above.
(2) What can you say about the data if the least-squares line has intercept ##a## equal to zero?
 
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  • #3
Y is proportional to X
 
  • #4
Faiq said:
Y is proportional to X

That answer is not useful. Take the formula for ##a##, in terms of ##(x_i,y_i)##, and set it to zero. What do you get?
 
  • #5
Faiq said:

Homework Statement


[/B]
A random sample of size ##n## from a bi-variate distribution is denoted by ##(x_r,y_r), r=1,2,3,...,n##. Show that if the regression line of ##y## on ##x## passes through the origin of its scatter diagram then
$$\bar y \sum^n_{r=1} x_r^2=\bar x\sum^n_{r=1} x_r y_r$$ where ## (\bar x,\bar y)## is the mean point of the sample.

The Attempt at a Solution


I don't really know how to begin. I am aware the line equation is $$b=\frac{y}{x}=\frac{\sum xy-\frac{\sum x\sum y}{n}}{\sum x^2-\frac{(\sum x)^2}{n}}$$ Not sure what to do next.
I suppose the that the linear model you are working with is: ##\ \displaystyle y=a+bx\ ##.

You have the correct expression for finding the linear coefficient, ##\ b\,.\ ##( Leave out the ##\ \displaystyle \frac yx\ ## ).

It seems to me that you must also consider the expression for ##\ a\,.\ ## Then show that if ##\ a=0\,,\ ## then you obtain the desired result:
##\displaystyle \bar y \sum^n_{r=1} x_r^2=\bar x\sum^n_{r=1} x_r y_r ##​
.
 
Last edited:

1. What is a regression line on origin?

A regression line on origin is a straight line that passes through the origin point (0,0) on a scatter plot. It is used to show the relationship between two variables, with one variable being the independent variable and the other being the dependent variable.

2. How is a regression line on origin calculated?

A regression line on origin is calculated by finding the slope of the line using the formula: m = (sum of (x * y)) / (sum of (x * x)) where x is the independent variable and y is the dependent variable. The intercept of the line is always 0 since it passes through the origin.

3. What is the significance of the slope of a regression line on origin?

The slope of a regression line on origin represents the rate of change between the two variables being compared. It shows the direction and strength of the relationship between the variables. A positive slope indicates a positive correlation, while a negative slope indicates a negative correlation.

4. How is the accuracy of a regression line on origin determined?

The accuracy of a regression line on origin is determined by the coefficient of determination, also known as R-squared. This value ranges from 0 to 1, with 1 indicating a perfect fit and 0 indicating no relationship between the variables. The closer the R-squared value is to 1, the more accurate the regression line is in predicting the dependent variable.

5. Can a regression line on origin be used for prediction?

Yes, a regression line on origin can be used for prediction as long as the relationship between the two variables remains the same. However, it is important to note that the regression line on origin may not accurately predict values that fall outside of the range of the data used to create the line.

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