Question

if the val of the samp. coeff of correlation is neg, the estimated slope parameter must...

if the val of the samp. coeff of correlation is neg, the estimated slope parameter must be positve. T or F

The total sum of square(s) in a regresssion mod will neverr exceed the regresssion sum of squares T or F

Math   Statistics-And-Probability   
0 0
Add a commentTranscribed image text
Next >

Homework Answers

Answer #1

1) False

Slope represents the direction of the regression equation line, if it is positive then association between the variables is also positive. If slope is negative then there is negative correlation exists between the variables.

2) False

SST = SST + SSE

Total sum of square = regression sum of square + error sum of square

Since total sum of square is addition of regression sum of square and error sum of square , it obvious that it is greater than regression sum of square.

0 0
Know the answer?
Your Answer:

Post as a guest

Your Name:

What's your source?

Earn Coins

Coins can be redeemed for fabulous gifts.

Log In

Sign Up

Not the answer you're looking for?
Ask your own homework help question
Similar Questions
Serial correlation (or autocorrelation) causes estimates of the slope parameter β to be overestimated on average....
Serial correlation (or autocorrelation) causes estimates of the slope parameter β to be overestimated on average. estimates of the standard errors to be understated on average. estimates of the T values of be understated on everage. slope parameter β to be understated on average.
X Y 1 50 8 57 11 43 16 18 20 18 Use the estimated regression...
X Y 1 50 8 57 11 43 16 18 20 18 Use the estimated regression equation is y = 61.5 – 2.21x. A). Compute the Mean Square Error Using Equation. S^2 = MSE = SSE/(n-2) B). Compute The Standard Error of the estimate using equation. S = SQRT(MSE) = SQRT[SSE/(n-2)] C). Compute the estimated Standard Deviation of b1 using equation. Sb1 = S/SQRT[SUM(x-(x-bar))^2] D). Use the t-test to test the following hypothesis (α = 0.05) H0: β1 = 0...
__________ refers to the degree of correlation among independent variables in a regression model. a. Multicollinearity...
__________ refers to the degree of correlation among independent variables in a regression model. a. Multicollinearity b. Confidence level c. Rank d. Tolerance __________ is used to test the hypothesis that the values of the regression parameters ß1, ß2, ...ßq are all zero. a. Extrapolation b. A t test c. An F test d. The least squares method What would be the coefficient of determination if the total sum of squares (SST) is 23.29 and the sum of squares due...
Given are five observations for two variables, x and y. xi 1 2 3 4 5...
Given are five observations for two variables, x and y. xi 1 2 3 4 5 yi 4 7 6 11 14 The estimated regression equation is  = 1.2 + 2.4 x. Compute the mean square error using the following equation (to 3 decimals). Compute the standard error of the estimate using the following equation (to 3 decimals). Compute the estimated standard deviation b 1 using the following equation (to 3 decimals). Use the t test to test the following hypotheses...
f. Interpret the slope coefficient for one of the dummy variables included in your regression model....
f. Interpret the slope coefficient for one of the dummy variables included in your regression model. g. For the slope coefficient of the variable with the smallest slope coefficient (ignore sign, use absolute value), test to see if the “a priori” expectation from part (a) is confirmed. Use alpha = 0.05. h. Interpret the coefficient of determination in this situation. i. Test the explanatory power of the entire regression model. Please use alpha = 0.01. j. For the variable with...
The assumption of homoscedasticity requires the residuals (differences between observed and estimated values) to be relatively...
The assumption of homoscedasticity requires the residuals (differences between observed and estimated values) to be relatively similar (homogeneous) across different values of the predictor variables. (T/F) The assumption of normality relates to the distributions of the independent variables; they must be normally distributed. (T/F) If the distribution of residuals (actual value minus estimated value) is negatively skewed with a mean of 5 and a standard deviation of 1, this indicates that (a) the regression line is estimated below the majority...
Model Summary Model R R Square Adjusted R Square      Std. Error of the Estimate 1...
Model Summary Model R R Square Adjusted R Square      Std. Error of the Estimate 1 .816 .666 .629 1.23721 a. Predictors: (Constant),x         ANOVA     Model Sum of Squares df Mean Square F                       Sig Regression Residual Total 27.500 13.776 41.276 1 9 10 27.500 1.531 17.966                 .002b                    a. Dependent Variable: Y                    b. Predictors: (Constant), X Coefficients Model Understand Coefficients B               Std Error Standardized Coefficients      Beta t Sig 1 (Constant)        x 3.001             1.125 .500                 .118 .816 2.667...
Given are five observations for two variables, x and y . xi 1 2 3 4...
Given are five observations for two variables, x and y . xi 1 2 3 4 5 yi 53 58 47 21 11 Use the estimated regression equation is y-hat = 78.01 - 3.08x A.) Compute the mean square error using equation. s^2 = MSE = SSE / n -2 [     ]   (to 2 decimals) B.) Compute the standard error of the estimate using equation s = sqrtMSE = sqrt SSE / n - 2 [      ] (to 2 decimals)...
A firm estimated its short-run costs using an average variable cost function of the form AVC...
A firm estimated its short-run costs using an average variable cost function of the form AVC = a + bQ + cQ2 And obtained the following results. Total fixed cost is $1,500.         DEPENDENT VARIABLE: AVC R-SQUARE F-RATIO P-VALUE ON F OBSERVATIONS: 40 0.8273 88.65 0.0001 VARIABLE PARAMETER ESTIMATE STANDARD ERROR T-RATIO P-VALUE INTERCEPT 38.05 11.87 3.21 0.0028 Q -4.20 1.56 -2.69 0.106 Q2 0.30 0.09 3.33 0.0020 The estimated marginal cost function is: MC = 38.05 – 8.4Q + 0.9Q2...
The chief economist for Argus Corporation, a large appliance manufacturer, estimated the firm’s short- run cost...
The chief economist for Argus Corporation, a large appliance manufacturer, estimated the firm’s short- run cost function for vacuum cleaners using an average variable cost function of the form                                                 AVC = a + bQ + cQ2 where AVC dollars per vacuum cleaner and Q number of vacuum cleaners produced each month. Total fixed cost each month is $180,000. The following results were obtained: DEPENDENT VARIABLE: AVC R-SQUARE F-RATIO P-VALUE ON F OBSERVATIONS: 19           0.7360           ...
ADVERTISEMENT
Need Online Homework Help?

Get Answers For Free
Most questions answered within 1 hours.

Ask a Question
ADVERTISEMENT