Do fast i will give like soon Question 2 of 6Let X and Y be random variables with E(Y) = 5,Var (Y) = 6, E(X) = 3, Var(X) = 4, andCov(X,Y)= 0.Suppose we do a linear regression of X on Y, whichgives us the resulting linear model Y = a + bx X.What is the value of a + b?

Let X and Y be random variables with E(Y) = 5, Var(Y) = 6, E(X) = 3, Var(X) = 4, and Cov(X, Y) = 0.…

Answered: Question 2 of 6 Let X and Y be random…

Elementary Linear Algebra (MindTap Course List)

8th Edition

ISBN:9781305658004

Author:Ron Larson

Publisher:Ron Larson

Chapter5: Inner Product Spaces

Section5.CR: Review Exercises

Problem 62CR

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Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
Repeat Example 5 when microphone A receives the sound 4 seconds before microphone B.
A) A linear regression has a =6 and b=5 what is y predicted as when x=9? B) A linear regression has b=3 and a=4.What is the predicted Y for x=7?
the following regression table where the dependent variable is the demandfor massage services in one city in the United States. Specifically, the dependent variable is the number of customers per hour (Models 1 and 2) or per day (Models 3 and 4). a) Explain why the coefficient for Population/1,000 in Model 2 is very different from the one in Model 4?
Two variable are found to have a strong negative linear correlation. Pick the regression equation that best fits this scenario. Oŷ = 0.32x 30 Oy = 0.82x - 30 Oy y = -0.32x + 30 -0.82x + 30
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?
Find the least linear regression of (1, 0), (3, 3), and (5, 6).
We have data on Lung Capacity of persons and we wish to build a multiple linear regression model that predicts Lung Capacity based on the predictors Age and Smoking Status. Age is a numeric variable whereas Smoke is a categorical variable (0 if non-smoker, 1 if smoker). Here is the partial result from STATISTICA. b* Std.Err. of b* Std.Err. N=725 of b Intercept Age Smoke 0.835543 -0.075120 1.085725 0.555396 0.182989 0.014378 0.021631 0.021631 -0.648588 0.186761 Which of the following statements is absolutely false? A. The expected lung capacity of a smoker is expected to be 0.648588 lower than that of a non-smoker. B. The predictor variables Age and Smoker both contribute significantly to the model. C. For every one year that a person gets older, the lung capacity is expected to increase by 0.555396 units, holding smoker status constant. D. For every one unit increase in smoker status, lung capacity is expected to decrease by 0.648588 units, holding age constant.
What is the equation for a simple linear regression model that predicts the dependent variable Y based on a single independent variable X?
Find the least-squares regression line ŷ bo+ b₁z through the points For what value of is y = 0? H= (-2,0), (1, 6), (4, 14), (8, 19), (10, 27).
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?
Here, mean of X is 3 and the mean of Y is 7. The regression line that predicts Y from X necessarily goes through the point (3,7). True False

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Question 2 of 6 Let X and Y be random variables with E(Y) = 5, Var(Y) = 6, E(X) = 3, Var(X) = 4, and Cov(X,Y)= 0. Suppose we do a linear regression of X on Y, which gives us the resulting linear model Y = a + b × X. What is the value of a + b?