1.Problem -top-down betaYou run a regression of monthly returns of Textron, an industrial conglomerate, on the S&P s00 Index andget, the following output. Assuming the risk-free rate of return is 2.00% and the equity risk premium is6.00%, answer the following questionsRegression StatisticsMultiple RR SquareAdj R SquareStandard Error0.78967030.62357910.61708910.0613324Observations60ANOVASignificancedfSSMSRegressionResidual0.361431 96.08285 6.47407E-140.00376210.361431580.218176Total590.579607StandardUpper 95%0.0092Errort StatP-valueLower 95%Coefficients-0.00710.008147-0.8703970.388-0.0234Intercept0.0001.35912.0561.70780.1742319.802186SP500What is Textron's beta? What does beta represent?a.beta is The prstatitity of market risk.proporcadb.What is the 99% confidence interval for Beta?

From the above outputs X (independent variable) - proportion of market risk Y(dependent variable) -…

Answered: 1. ( You run a regression of monthly…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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1. Sue is conducting a regression analysis to predict the amount of salaries expense using the number of employees and the average rate of pay. This type of regression analysis is called a(n) independent regression. linear regression. multiple regression. dependent regression.
Please see the attached picture. Thank you.
True/false (please explain). Suppose that a regression of Y on X₁ is unbiased, and the true slope coefficient is 2. Another variable X2 is correlated with Y, but it is uncorrelated with X₁. In expectation, the default t-statistic on B₁ will be larger in the multivariate regression that includes X₂ than in the bivariate regression that omits X₂.
We want to predict the probability of car accidents based on three risk factors: (i) average driving speed, (ii) weather, and (iii) user age. What is the most appropriate machine learning model for this case? Why? a. Linear regression b. Logistic regression c. K-means clustering
If the R-squared for a regression model relating the outcome y to an explanatory variable x is 0.9. This implies that there is a positive linear relationship between y and x. True or false?
The following is a 7-month-demand-history for a certain product. Assuming that linear trend appears, develop the equation of regression line. Round values of coefficients to two decimal places. Use the equation to prepare a forecast for December 2004.
9. A wildlife researcher is interested in predicting a mammal’s lifespan (in years) based on its average number of hours of sleep per night. Data was obtained from a large random sample of mammals, and the regression equation turned out as follows: Predicted lifespan = 36.93 – 1.64 (average hours of sleep) Which one of the following statements is a correct interpretation of this equation? 1. Approximately 1.64% of the variability in lifespan can be explained by the regression equation. 2. As lifespan increases by one year, average hours of sleep is predicted to decrease by 1.64 hours. 3. As lifespan increases by one year, average hours of sleep is predicted to increase by 36.93 hours. 4. As average hours of sleep increases by one hour, lifespan is predicted to increase by 36.93 years. 5. As average hours of sleep increases by one hour, lifespan is predicted to decrease by 1.64 years.
You are analyzing a dataset containing 379 datapoints, and want to use 13 predictor variables to create a multiple variable linear regression model of the data. You conduct an ANOVA analysis, and yield a R² of 38%. Using this information, what would be the F statistic of your analysis?
10
The beta of a stock has been estimated as 1.4 using regression analysis on a sample of historical returns. A commonly-used adjustment technique would provide an adjusted beta of A. 1.32. B. 1.13. C. 1.0. D. 1.27.
1. Explain the purpose or use of the following:a. Linear regression equationb. Correlation coefficient.
Q2 The two "experienced" operation managers have resisted the introduction of a computerized linear regression forecasting system, claiming that their current forecasting tool is "much better than any impersonal computer could do". Table Q2 shows the weekly demand for the previous period. Perform the following: (1) Develop a linear regression model using the actual demand, and make four(4)-week forecast of production; (ii) Determine the mean square error and standard error of the forecast developed in section (i) Table Q2 Actual Week Demand 1 3,000 2 4,400 3 4,200 4 3,100

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