Assume you gathered the historical weekly excess returns for IBM and for a S&P 500 portfoliousing data over the last 2 years and plotted them as shown in the scatter plot below. Assume thetrend line shown on the plot is from a CAPM motivated regression of the IBM excess returns onthe market excess returns.Question: Which of the statements below best describes how the beta and alpha regressioncoefficients would be related to the figure?Excess returns on IBM0.100.00-0.10-0.200O-0.20CAPM regression for IBM0Oe-0.10°ob9.00O0.0Excess returns on MARKET20800.1OO The beta is the slope of the regression trend line. The alpha is the intercept of the line.O The alpha is the slope of the regression trend line. The beta is the intercept of the line.O The beta is proportion of variation in the Y-axis variable explained by the model.O Beta is the measure of total risk.

The scatterplot based on the regression of Excess returns of IBM vs Excess return on MARKET is…

Answered: Assume you gathered the historical…

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

Identify the correct statement concerning, r the correlation coefficie between X and Y and the linear regression, Y = aX+b. A. Both can be used to show cause and effect. B. Both quantify the strength of relationship between X and Y. C. X and Y are interchangeable in both relationships. D. Both can be used to make predictions.
Statistics Canada produces consumer price indexes for several different categories. Shown here are the percentage changes in consumer price indexes over a period of 22 years for durable goods, semidurable goods, and nondurable goods. Also displayed are the percentage changes in total goods price index. Use these data and multiple regression to develop a model that attempts to predict the total goods index by the other three variables. Comment on the result of this analysis. Year TotalGoods DurableGoods SemidurableGoods NondurableGoods 1 85.7 89.4 91.9 83.2 2 86.4 90.4 92.5 83.8 3 87.8 92.5 93.4 85.2 4 86.8 96.0 94.2 81.6 5 88.4 99.0 94.9 82.9 6 89.9 100.8 95.4 84.4 7 91.2 101.6 97.0 86.0 8 91.4 101.5 97.7 86.1 9 93.1 101.5 99.2 88.4 10 96.0 100.8 99.6 93.3 11 98.4 100.1 100.3 97.4 12 100 100 100 100 13 101.9 99.2…
Illustrate the Regression Discontinuity Estimators?
I recently asked this question and was wondering if you could show me how to do it, not just putting it in a caculator or excel but written out equations and seeing my information plugged in so I can understand. Thank you. Calculate the simple linear regression to determine if the amount of time spent on homework can be predicted by amount of sleep. Graph the relationship and determine, numerically, if there are any outliers. Interpret all results in a paragraph citing the appropriate statisitcs.
what is the regression equation for all predictors? I
The following chart shows the actual sales for the last 12 months for a given company. Assume that sales are best fit by a linear trend and you can use single linear regression to set up a forecasting model. Using the sales data answer below questions (justify your answers): A.What would be the typical linear regression equation for the number of sales? B.Make the sales forecast for period 15 of next year. C. Make the sales forecast for period 17 of next year. D. What is the standard error for the data?
The following gives the number of accidents that occurred on Florida State Highway 101 during the last 4 months: Month Number of Accidents Jan 25 Feb 40 Mar 60 Apr 90 Using the least-squares regression method, the trend equation for forecasting is (round your responses to two decimal places): ŷ-0-0x Using least-squares regression, the forecast for the number of accidents that will occur in the month of May = accidents (enter your response as a whole number).
Suppose a researcher collects data on houses that have been sold in a particular neighbourhood over the past year, and obtains the regressions results in the table shown below. A family purchases a 2000 square foot home and plans to make extensions totalling 500 square feet. The house currently has a pool, and a real estate agent has reported that the house is in excellent condition. However, the house does not have a view, and this will not change as a result of the extensions. According to the results in column (1), what is the expected DOLLAR increase in the price of the home due to the planned extensions?

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