Problem 11a. Consider the tentative MA(2) model y₁= 8+ a₁ - 0₁ a₁-1-0₂ a₁-2 where a, is a random shock,distributed i.i.d. N(0,0²). The sample correlation coefficients were calculated as r₁ = 0.3 andr₂ = -0.2. By assuming +2 = 0.58, estimate 0₁ and 0₂.b. Consider the tentative AR(2) model yt = 20 +0.7 y₁-1-0.5 y2 +at where a, is a random shock,distributed i.i.d. N(0,0²). Estimate the mean value of the time series, i.e., μ.

The time series is the method of estimating the future observation which is called the forecast. The…

Answered: Problem 11 a. Consider the tentative…

Calculus For The Life Sciences

2nd Edition

ISBN:9780321964038

Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.

Publisher:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.

Chapter1: Functions

Section1.2: The Least Square Line

Problem 5E

See similar textbooks

Similar questions

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?
9. The final score (Y) of students in a course was correlated with score (CX) in entrance test. The relevant data in coded units are as follows: Y: 2 8 5n 4 2 X: 6. (a) Obtain the lincar regression equation of Y on .X. (b) Obtain the correlation coefficient and interpret it. (c) Predict Y when X = 4. If the predicted value is different from the observed value of 6. Explain the reasons for the difference.
84). With a = 45.6 and y = 42.8, you You run a regression analysis on a bivariate set of data (n = obtain the regression equation у — — 3.196х + 55.817 with a correlation coefficient of r = 0.183. You want to predict what value (on average) for the response variable will be obtained from a value of 180 as the explanatory variable. What is the predicted response value? y = (Report answer accurate to one decimal place.)
20). With a = 42.6 and j = 46.9, you You run a regression analysis on a bivariate set of data (n = obtain the regression equation y = - 1.249x + 32.208 0.647. You want to predict what value (on average) for the with a correlation coefficient of r = response variable will be obtained from a value of 70 as the explanatory variable. What is the predicted response value? y =
27 Given that the regression equations of Y on X and of X on Y are respectively Y about the origin is 2. Find (i) the correlation coefficient between X and Y and (ii) the standard deviation of Y. X and 4X- Y= 3 and that the second moment of X
Q3. Find out the regression coefficients of Y on X and of x on Y on the basis of following data: Ex = 50, X = 5, EY = 60, Y = 6, EXY = 350 Variance of X= 4, Variance of Y= 9
Suppose that n = 50, i.i.d. observations for (Yi, Xi) yield the followingregression results: Ŷ= 49.2 + 73.9X, SER = 13.4, R2 = 0.78. (23.5) (16.4)Another researcher is interested in the same regression, but makes an errorwhen entering the data into a regression program: The researcher enterseach observation twice, ending up with 100 observations (with observation1 entered twice, observation 2 entered twice and so forth).a. Using these 100 observations, what results will be produced by theregression program? (Hint: Write the “incorrect” values of the samplemeans, variances, and covariances of Y and X as functions of the “correct”values. Use these to determine the regression statistics.) Ŷ = ____ + ____X, SER = ____, R2 = ____. (____) (____)b. Which (if any) of the internal validity conditions are violated?
mman 34. Find out the regression coefficients 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. All nolesenpe
3. The equations of two regression lines obtained in a correlation analysis of 60 observations are 5x = 6y + 24 and 1000y = 76x – 3708. What is the correlation coefficient and what is its probable error ? Show that the ratio of the coefficient of variability of x to that of y is 24
Consider the model Y = B1 + B2X + e, which we estimate using a random sample with 12 observations. Let bj and b2 be the estimators for B1 and B2 and recall Eê, where ê; = Y; – bị – b2X;. Suppose the sample correlation between {X; }"_-1 and {Y;}"_, is 0.5 and E(Y; – Ỹ„)² = 100. What is ổ?? %3D n-2 Hint: (i) for simple regression, the regression R? is equal to the squared sample correlation between X and Y. (ii) R² = 1 – SSE where SSE = Eế and SST SST = O a. 7.5 O b. 8.5 О с. 9 O d. 10 O e. 8 O f. 7 Clear my choice
If x and y are independent random variable with E(xy)= 20 then the correlation coefficient is
The price X (dollars per pound) and consumption y (in pounds per capita) of beef were samples for 10 randomly selected years. The following data should be used to answer the question that follows. n = 10 Ex = 36.19 Ix² = 134.17 2.9 < x s 6.2 Ey = 774.7 Iy² = 60739.23 Exy = 2832.21 Using this data, a student calculated SSy = 28.43 SSx = 3.2 SSy= 717. Calculate the %3D %3D value of the standard error or regression, Se , and enter you answer accurate to the nearest hundredth (2 decimal places).

SEE MORE QUESTIONS

Recommended textbooks for you

Calculus For The Life Sciences

Calculus

ISBN:

9780321964038

Author:

GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.

Publisher:

Pearson Addison Wesley,

College Algebra

Algebra

ISBN:

9781305115545

Author:

James Stewart, Lothar Redlin, Saleem Watson

Publisher:

Cengage Learning

Calculus For The Life Sciences

Calculus

ISBN:

9780321964038

Author:

GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.

Publisher:

Pearson Addison Wesley,

College Algebra

Algebra

ISBN:

9781305115545

Author:

James Stewart, Lothar Redlin, Saleem Watson

Publisher:

Cengage Learning

SEE MORE TEXTBOOKS