Find out the regression coeticients of Y on X and X on Y from the following data: ΣΧ= 50 , X= 5, Σ Υ 60, Y-6, Σ ΧΥ- 350. Variance of X=4, Variance of Y= 9.
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- There is a linear regression: Yi = B0+ B1(Xi^2)+ ei present, where Xi is squared. ei ∼ N(0,σ2). How would I derive LSE for B0 and B1 and their variance?The coefficients in a distributed lag regression of Y on X and its lags can be interpreted as the dynamic causal effects when the time path of X is determined randomly and independently of other factors that influence Y. Explain How?In running a regression of the retunrs of stock XYZ against the returns on the market, the Std for the returns of stock XYZ is 20% and that of the market returns is 15%. If the estimated beta is found to be 0.75 : What is the maximum possible value of beta given that the standar deivation of the returns of stock XYZ is 20% and those of the market is 15% ?
- For the regression model Yi = b0 + eI, derive the least squares estimator.If the correlation of the X's is equal to 1 or -1, then the regression cannot process. Why?I have some doubts regarding linear regression. if any 2 variables in X1, X2 AND Y have a positive correlation, then in the linear regression Y = b0 + b1X1 +b2X2 +e, will the sign of b1 and b2 both be positive? will the residuals that we get from linear regression will always be uncorrelated given X?
- A fitted linear regression model is (y=10+2x ). If x = 0 and the corresponding observed value of y = 9, the residual at this observation is:The least-squares regression line relating two statistical variables is given as = 24 + 5x. Compute the residual if the actual (observed) value for y is 38 when x is 2. 4 38 2It is required to use the data given in the table to estimate the parameters of the simple linear regression equation by any of the estimation methods: