Consider the following model of the Philips curve. In this model inflation is regressedupon unemployment using a sample of 56 observations, where INF denotes ameasure of the inflation rate and UNEM denotes a measure of the unemployment rate.An initial analysis of this model produced the results reported in Tables Two and Three.Table TwoDependent Variable: INFMethod: Least SquaresSample: 1948 2003Included observations: 56Coeficient Sid. Emor -Statistic Prob.VariableUNEM1.053565 1.547957 0.680617 0.49900.502378 0.265562 1.891752 00639Schwarz criterion 5.123418Akaike info criterion 5051084Table ThreeBreusch-Godtrey Serial Correlation LM TestFatatisticObs'R-squared31.52897 Prob. F(1,53)20.88778 Prob. Chi-Square(1)0.00000.0000Further analysis produced the results in Tables Four and Five.Table FourDependent Variable: INFMethod: Least SquaresSample (aqusted) 1950 2003Included observations: 54 afer adjustmentsVariableCoefficient Sid. Emor-StatistieProb.INF(-1)UNEMUNEM-1)UNEM-2)1.40901108331230.42144102037121.139049 1.2368200.104146 7.9005340.314574 1.3397220.3500920.22200.00000.18650.57310.05650.5672960.50403802580681.953120Akake info criterion 4.178055Schwarz oriterion 4362220Table FiveBreusch-Godtey Serial Correlation LM TestFatatstieObs"R-aquared2325230 Prob. F(1,48)2495029 Prob. Ch-Square(1)0.13300.1142c) Compare the two models presented in Tables Two and Four on the basis of the calculatedvalues of the information criteria considered. Using appropriate justification, explain whichmodel you prefer.d) A new ARDL (2,3) model of INF is estimated, with no serial autocorrelation detectedfor this model. The calculated values of the Schwarz Information Criterion and AkaikeInformation criterion for this model are given as:The Schwarz Information Criterion = 4.443694The Akaike Information Criterion = 4.183467Compare this model with those presented in Tables Two and Four. Which of the threemodels do you prefer? Explain your answer.

c) The model with lowest value of AIC (Akaike info criterion) and lowest schwarz criterion is…

Answered: Consider the following model of the…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

4. We would like to determine how a man's weight can be used to predict his systolic blood pressure. We measure the weights (in pounds) and the systolic blood pressures of a sample of 17 men. The data are plotted on a scatterplot and it can be seen that a linear relationship is a reasonable assumption. The least squares regression line is calculated to be ŷ = 125.561 + 0.145x. The ANOVA table (with some values missing) is shown below: |Source of Variation | df | Sum of Squares Mean Square F Regression Error Total 26.46 617.82 (a) We would like to test whether a linear relationship exists between a man's weight and his systolic blood pressure. We will conduct an analysis of variance to test Ho: B1 = 0 vs. Ha: B1 + 0 at the 5% level of significance. Enter all missing values in the ANOVA table. Calculate the value of the test statistic, find the P-value of the test and provide a properly worded conclusion. (b) What is the value of the correlation between weight and systolic blood pressure…
Add to MyRegistry 9 Mail - sgorham@a.. Like father, like son: In 1906, the statistician Karl Pearson measured the heights of 1078 pairs of fathers and sons. The following table presents a sample of 6 pairs, with height measured in inches, simulated from the distribution specified by Pearson. Compute the least-squares regression line for predicting son's height (v) from father's height (x). Round the slope and y-intercept values to at least four decimal places. Father's Son's height height 73.6 74.9 66.7 68.8 70.1 73.3 72.3 71.9 73.6 76.5 69.3 71.4 Send data to Excel Regression line equation: Save For Later Submit Assignment Check Answer © 2021 McGraw Hill LLC. AlI Rights Reserved. Terms of Use Privacy Center
A weight-loss program wants to test how well their program is working. The company selects a simple random sample of 51 individual that have been using their program for 15 months. For each individual person, the company records the individual's weight when they started the program 15 months ago as an x-value. The subject's current weight is recorded as a y-value. Therefore, a data point such as (205, 190) would be for a specific person and it would indicate that the individual started the program weighing 205 pounds and currently weighs 190 pounds. In other words, they lost 15 pounds. When the company performed a regression analysis, they found a correlation coefficient of r = 0.707. This clearly shows there is strong correlation, which got the company excited. However, when they showed their data to a statistics professor, the professor pointed out that correlation was not the right tool to show that their program was effective. Correlation will NOT show whether or not there is…
Suppose a doctor measures the height, x, and head circumference, y, of 8 children and obtains the data below. Thecorrelation coefficient is 0.944 and the least squares regression line is y = 0.199x + 11.982. Complete parts (a) and (b)below.Height, x27.5 25.5 26.25 25.25 27.5 26.25 26 27.25 27.25 27 27.25 ФHead Circumference, # 17.5 17.0 17.2 17.0 17.5 17.3 17.2 17.4 17.3 17.3 17.4(a) Compute the coefficient of determination, R?R?.% (Round to one decimal place as needed.)(b) Interpret the coefficient of determination and comment on the adequacy of the linear model.Approximately % of the variation inis explained by the least-squares regression model.According to the residual plot, the linear model appears to be (Round to one decimal place as needed.)
16. A scatterplot of the monthly salary (in thousands of pesos) versus years of education for seven full-time workers is displayed below. The least- squares regression line is drawn on the plot. Six of the data points are plotted with open circles and the seventh is plotted with a solid circle. The data point plotted with a solid circle to 20 Ne stynan eftdutatien A. has a large residual and is influential. B. has a small residual and is influential. C. has a large residual and is not infiluential. D. has a small residual and is not influential. 00 09 oad jo umroy s
The following output is from a multiple regression analysis that was run on the variables FEARDTH (fear of death) IMPORTRE (importance of religion), AVOIDDTH (avoidance of death), LAS (meaning in life), and MATRLSM (materialistic attitudes). In the regression analysis, FEARDTH is the criterion variable (Y) and IMPORTRE,AVOIDDTH, LAS, and MATRLSM are the predictors (Xs). The SPSS output is provided below, followed by a number of questions. Descriptive Statistics Mean Std. Deviation N feardth 27.0798 8.08365 163 importre 5.8282 2.46104 163 avoiddth 18.5460 6.97633 163 Las 70.1288 9.89460 163 matrlsm 53.5552 10.21860 163 Model Variables Entered Variables Removed Method 1 matrlsm, avoiddth, importre, lasa . Enter a. All requested variables entered. b. Dependent Variable: feardth Model Summary Model R R Square Adjusted R Square…
A data set is given below. (a) Draw a scatter diagram. Comment on the type of relation that appears to exist between x and y. (b) Given that x = 3.6667, sx =2.4221, y = 3.8667, sy = 1.8435, andr= -0.9525, determine the least-squares regression line. (c) Graph the least-squares regression line on the scatter diagram drawn in part (a). D O A. (a) Choose the correct graph below. Ay 6- X y 0 0 2 3 5 6 6 5.8 5.7 5.0 2.8 1.7 2.2 O G B. Ay 6- Q (...) C. 0- ● Q D. Ау 0- X 6
Espan Interpreting technology: The following display from the TI-84 Plus calculator presents the least-squares regression line for predicting the price of a certain stock (y) from the prime interest rate in percent (x). LinReg y=a+bx a = 2.39562641 do b=0.37929688 2=0.4130321711 r=0.64267579 Part: 0 /3 Part 1 of 3 Write the equation of the least-squares regression line. Use the full accuracy shown in the calculator output (do not round your answers). Regression line equation: y = Part 2 of 3 What is the correlation between the interest rate and the yield of the stock? The correlation coefficient is Part: 2 /3 Part 3 of 3 Predict the price when the prime interest rate is 6%. Round the answer to at least four decimal places. When the prime interest rate is 6%, the price is predicted to be
A data set is given below. (a) Draw a scatter diagram. Comment on the type of relation that appears to exist between x and y. (b) Given that x=3.8333, sx=2.4014, y=3.6500, sy=1.7942, and r=−0.9539, determine the least-squares regression line. (c) Graph the least-squares regression line on the scatter diagram drawn in part (a).
QUESTION 14 CardioGood Fitness Treadmill Products Data The market research team at CardioGood Fitness retail store wants to investigate whether there are differences across the product lines of treadmills sold; TM195 (low-end model), TM498 (medium grade model) and TM798 (high-end model) with respect to customer characteristics. The team decides to collect data on individuals who purchased a treadmill at a CardioGood Fitness retail store during the past three months. One question the team is interested in is if there is a difference in the miles of intended use of the treadmill between males and Females. t-Test: Two-Sample Assuming Unequal Variances MaleMiles Female Miles Mean 112.81 89.99 Variance 2991.43 2006.76 Observations 104 76 Hypothesized Mean Difference df 176 t Stat 3.0732 P(T<=t) one-tail 0.0012 t Critical one-tail 1.6536 P(T<=t) two-tail 0.0025 t Critical two-tail 1.9735 In the essay box, clearly answer questions 1-5 below: 1. Is this conducting a one-sided test or a…
2
Given the following data set, let x be the explanatory variable and y be the response variable. x75 1 8812 y36102398 (a) If a least squares line was fitted to this data, what percentage of the variation in the y would be explained by the regression line? (Enter your answer as a percent.) ANSWER: % (b) Compute the correlation coefficient: r =

SEE MORE QUESTIONS

Recommended textbooks for you

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

Elementary Statistics: Picturing the World (7th E…

Statistics

ISBN:

9780134683416

Author:

Ron Larson, Betsy Farber

Publisher:

PEARSON

The Basic Practice of Statistics

Statistics

ISBN:

9781319042578

Author:

David S. Moore, William I. Notz, Michael A. Fligner

Publisher:

W. H. Freeman

Introduction to the Practice of Statistics

Statistics

ISBN:

9781319013387

Author:

David S. Moore, George P. McCabe, Bruce A. Craig

Publisher:

W. H. Freeman

SEE MORE TEXTBOOKS