Dependent Variable: INTEREST Method: Least Squares Date: 08/08/20 Time: 17:38 Sample: 2010M01 2019M12 Included observations: 120 Variable с UNEMP R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) Coefficient Std. Error 1-Statistic Prob. 9.586787 1.478492 6.484167 0.0000 -1.232859 0.267930 -4.601419 0.0000 0.152135 Mean dependent var 0.144949 S.D. dependent var 1.105708 Akaike info criterion 144.2656 Schwarz criterion -181.3225 Hannan-Quinn criter. 21.17306 Durbin-Watson stat 0.000011 2.799500 1.195761 3.055375 3.101833 3.074242 0.028137 The above is the regression result between the Australian monthly unemployment rate (UNEMP, independent variable) and monthly interest rate (INTEREST, dependent variable). In testing for the null hypothesis that the coefficient of UNEMP is 0, what is Type II error? Select one: O a. UNEMP has no effect on INTEREST, but the test accepts the null hypothesis Ob UNEMP has some effect on INTEREST, but the test does not reject the null hypothesis UNEMP has some effect on INTEREST, but the test rejects the null hypothesis O c. O d. UNEMP has no effect on INTEREST, but the test rejects the null hypothesis

Dependent Variable: INTEREST Method: Least Squares Date: 08/08/20 Time: 17:38 Sample: 2010M01 2019M12 Included observations: 120 Variable с UNEMP R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) Coefficient Std. Error 1-Statistic Prob. 9.586787 1.478492 6.484167 0.0000 -1.232859 0.267930 -4.601419 0.0000 0.152135 Mean dependent var 0.144949 S.D. dependent var 1.105708 Akaike info criterion 144.2656 Schwarz criterion -181.3225 Hannan-Quinn criter. 21.17306 Durbin-Watson stat 0.000011 2.799500 1.195761 3.055375 3.101833 3.074242 0.028137 The above is the regression result between the Australian monthly unemployment rate (UNEMP, independent variable) and monthly interest rate (INTEREST, dependent variable). In testing for the null hypothesis that the coefficient of UNEMP is 0, what is Type II error? Select one: O a. UNEMP has no effect on INTEREST, but the test accepts the null hypothesis Ob UNEMP has some effect on INTEREST, but the test does not reject the null hypothesis UNEMP has some effect on INTEREST, but the test rejects the null hypothesis O c. O d. UNEMP has no effect on INTEREST, but the test rejects the null hypothesis

Managerial Economics: Applications, Strategies and Tactics (MindTap Course List)

14th Edition

ISBN:9781305506381

Author:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Publisher:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Chapter4A: Problems In Applying The Linear Regression Model

Section: Chapter Questions

Problem 2E

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DEPENDENT VARIABLE Qc R- SQUARE P- VALUE ON F 64 0.8093 0.0001 INDEPENDENTVARIABLE PARAMETER ESTIMATE STANDARD ERROR T-RATIO P-VALUE INTERCEPT 8.20 4.01 2.04 0.0461 PC -3.54 1.64 -2.16 0.0357 M 0.64287 0.19 3.38 0.0014 PA 0.7854 0.38 2.07 0.0439 10. Write the resulting regression equation. Q = f( P, M, PR) where Qc = demand for cement/month (in yards) Pc = the price of cement per yard, M = country’s tax revenues per capita, and PR = the price of asphalt per yard.
You estimated a regression with the following output. Source | SS df MS Number of obs = 289 -------------+---------------------------------- F(1, 287) = 41986.64 Model | 664544048 1 664544048 Prob > F = 0.0000 Residual | 4542496.25 287 15827.5131 R-squared = 0.9932 -------------+---------------------------------- Adj R-squared = 0.9932 Total | 669086544 288 2323217.17 Root MSE = 125.81 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 43.81013 .2138056 204.91 0.000 43.38931 44.23096 _cons | 49.31707 16.96222 2.91 0.004 15.93094 82.70319…
A multiple regression analysis produced the following output from Minitab.Regression Analysis: Y versus x and xPredictor Coef SE Coef T PConstant -0.0626 0.2034 -0.31 0.762x 1.1003 0.5441 2.02 0.058x -0.8960 0.5548 -1.61 0.124S = 0.179449 R-Sq = 89.0% R-Sq(adj) = 87.8%Analysis of VarianceSource DF SS MS F PRegression 2 4.7013 2.3506 73.00 0.000ResidualError18 0.5796 0.0322Total 20 5.2809These results indicate that____________
DEPENDENT VARIABLE Qc R- SQUARE P- VALUE ON F 64 0.8093 0.0001 INDEPENDENTVARIABLE PARAMETER ESTIMATE STANDARD ERROR T-RATIO P-VALUE INTERCEPT 8.20 4.01 2.04 0.0461 PC -3.54 1.64 -2.16 0.0357 M 0.64287 0.19 3.38 0.0014 PA 0.7854 0.38 2.07 0.0439 4. At the 5% level of significance, which variables are statistically significant?
The following data relate the sales figures of restaurant, to the number of customers registered that week: Week Customers Sales (SR) First 16 330 Second 12 270 Third 18 380 Fourth 14 300 a) Perform a linear regression that relates bar sales to guests (not to time). b) If the forecast is for 20 guests next week, what are the sales expected to be?
DEPENDENT VARIABLE Qc R- SQUARE P- VALUE ON F 64 0.8093 0.0001 INDEPENDENTVARIABLE PARAMETER ESTIMATE STANDARD ERROR T-RATIO P-VALUE INTERCEPT 8.20 4.01 2.04 0.0461 PC -3.54 1.64 -2.16 0.0357 M 0.64287 0.19 3.38 0.0014 PA 0.7854 0.38 2.07 0.0439 7. If the price of asphalt (PR) decreases by 15, what will happen to the estimated quantity of cement demanded? Q = f( P, M, PR) where Qc = demand for cement/month (in yards) Pc = the price of cement per yard, M = country’s tax revenues per capita, and PR = the price of asphalt per yard.
DEPENDENT VARIABLE Qc R- SQUARE P- VALUE ON F 64 0.8093 0.0001 INDEPENDENTVARIABLE PARAMETER ESTIMATE STANDARD ERROR T-RATIO P-VALUE INTERCEPT 8.20 4.01 2.04 0.0461 PC -3.54 1.64 -2.16 0.0357 M 0.64287 0.19 3.38 0.0014 PA 0.7854 0.38 2.07 0.0439 Explain the interceptand the coefficients of Pc, M, and PA.
4. From the regression output, report the coefficients, standard errors, t-statistics, probability and R-squared (report the results in a table). 5. Re-write the specified model in (a) with values from the regression results and interpret the coefficients.
Past class data has shown that the regression line relating the final exam score and the midterm exam score for students who take statistics from the College of Information Technology and Engineering from Dr. Kalaw is: final exam = 50 + 0.5 × midterm One interpretation of the slope is a. students only receive half as much credit (.5) for a correct answer on the final exam compared to a correct answer on the midterm exam. b. a student who scored 0 on the midterm would be predicted to score 50 on the final exam. c. a student who scored 10 points higher than another student on the midterm would be predicted to score 5 points higher than the other student on the final exam. d. a student who scored 0 on the final exam would be predicted to score 50 on the midterm exam.
Determine the mode choice (personal vehicle or bus system) for the following regression model: Utility Function: Umode – (8.333 x 10-4)*(Access time in sec) – (6.667 x 10-4)*(Wait Time in sec) – (5.00 x 10-4)*(Riding time in sec) – (1.40)*(Cost, $) PARAMETER PERSONAL VEHICLE CITY BUS SYSTEM MODE CONSTANT -0.01 -0.07 ACCESS TIME (SECS) 300 600 WAITING TIME (SECS) 0 900 RIDING TIME (SECS) 1,500 6,000 COST (DOLLARS) $1.50 $1.00
Econometrics: Given the following regressions: yt = 1.2yt-1 + Et and xt = 1.3xt-1 - 0.4xt-2 + Ct 1) Find he homogeneous equation and assess the implications of the roots.
Numerical Answer Only Type Question Enter the numerical value only for the correct answer in the blank box. If a decimal point appears, round it to two decimal places. Assume that the number of visits by a particular customer to a mall located in downtown Toronto is related to the distance from the customer's home. The following regression analysis shows the relationship between the number of times a customer visits(Y)per month and the distance(X, measured in km) from the customer's home to the mall. \[ Y=15-0.5 X \] A customer who lives30 kmaway from the mall will visi______ who lives10 km away. less times than a customer