To calculate a residual for the ith observation, we do not need the:
Actual value of Yi. |
|
Estimated intercept. |
|
Estimated slope. |
|
Standard error. |
Estimated value of Yi = Estimated intercept + Estimated slope * Xi
Residual for the ith observation = Actual value of Yi - Estimated value of Yi
= Actual value of Yi - (Estimated intercept + Estimated slope * Xi)
From above equation, we need Actual value of Yi, Estimated intercept and Estimated slope to calculate a residual for the ith observation.
So, we do not need Standard error for the calculation.
The answer is Standard error.
To calculate a residual for the ith observation, we do not need the: Actual value of...
(e) Let Wi denote the random response for the ith observation and denote the associated random error. Suppose the constant variance assumption has been violated such that Var(e) is proportional to (y) = H . That is, Var(€;) = Var(yi) = Chw where c is some constant. Consider a transformed response variable, g(x). We can write the first-order Taylor expansion of g() at Hy, as 9(y) (y) + (y) (yi - My), (1) where g'( ) is the first derivative...
Suppose observation 12 from a dataset has a predicted Y value of 12.2 and an actual Y value of 15.3. Then the value of the residual for observation 12 is 3.1 True False
4. We have n statistical units. For unit i, we have (x; yi), for i 1,2,...,n. We used the least squares line to obtain the estimated regression line bobi . (a) Show that the centroid (z, y) is a point on the least squares line, where x-(1/n) Σ-Χί and у-(1/ n) Σ|-1 yi. (Hint: Evaluate the line at x x.) (b) In the suggested exercises, we showed that e,-0 and where e is the ith residual, that is e -y...
4. We have n statistical units. For unit i, we have (xi; yi), for i-1,2,... ,n. We used the least squares line to obtain the estimated regression line у = bo +biz. (a) Show that the centroid (x, y) is a point on the least squares line, where x = (1/n) and у = (1/n) Σ¡ı yi. (Hint: E ) i-1 valuate the line at x = x. (b) In the suggested exercises, we showed that e,-0 and e-0, where...
The __________ is the standard deviation of the estimated value of the mean of the actual distribution around its true value; that is, it is the standard deviation of the average return. a. Standard return b. Standard error c. Estimate error d. Standard Predict error
1.Based on the table above, how to intepret this regression analysis? 2. When we need to look at the adjusted r2 and why? 3. How to conduct the hypothesis test? 0 Regression Statistics 1 Multiple R 2 R Square 3 Adjusted RS 0.853658537 0,97530483 0.951219512 4 Standard Err 0.191273014 5 Observation 6 7 ANOVA Significance F 0.220863052 df SS MS 0.713414634 0.356707 9 Regression 0 Residual 1 Total 2. 9.75 1 0.036585366 0.036585 0.75 2 Lower 95 % 3 Coefficients...
Consider a multiple regression model of the dependent variable y on independent variables x1, x2, and x3: Using data with n = 12 observations for each of the variables, a researcher obtains the following estimated regression equation for the above model y0.5216 + 1.2419x1 + 0.3049x2 - 0.0217x3 The standard error of estimate for this equation is s0.6489 The table below gives the values for the independent and dependent variables and their corresponding predicted values, residuals, and leverage Predicted Value...
what exacty is residual income? How do we calculate it and what does it tell us?
How do I calculate UCL/LCL (95%) from the following available data; Coefficient, Standard Error, T-Stat, P-Value. Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.9613 0.9241 0.9222 1425.3397 200 ANOVA Significance df MS Regression Residual Total 479410417802.47 95882083560.49 472.4892 39368362197.53 518778780000.00 0.00 194 199 202929702.05 Upper 95% CoefficientsStandard Erro t Stat P-value Lower 95% 45482.366 10383.543 11.088 738.388 0.014 2.546 19403.8863 3153.7202 10.4859 175.8223 0.0023 1.2209 2.340.0201 0.0012 0.2916 0.0000 0.0000 0.0383 Intercept V1 v2 3.29...
Some research has indicated that as people age they need less sleep. A random sample of adults was selected and the number of hours that they slept last night was recorded. Simple regression analysis was performed on this sample and the results are attached below. Which of the following statements is true using a 0.10? 囲Click the icon to view the regression results O A. Because the p-value for the slope is 0 065, we can reject the null hypothesis...