### Linear Regression and Market Correlation: Understanding Beta#### Question:After running a linear regression with a 1-factor model using the market premium, if beta is negative, then?#### Options:- **The stock and the market are positively correlated** (move in the same direction)- **The stock and the market are negatively correlated** (move in the opposite direction)- **No meaning**- **Not enough information**- **You must construct additional Pylons**#### Explanation:This question explores the relationship between a stock and the market as determined by a linear regression model. In this context:- **Negative Beta**: Indicates a **negative correlation** between the stock and the market. This means they move in opposite directions. For example, when the market goes up, the stock tends to go down, and vice versa. The option "You must construct additional Pylons" is likely a humorous reference without relevance to the financial context.

Answered: After running a linear regression with…

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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A researcher wants to include the variable "skill" in a regression, but cannot find a proper metric to measure it, so they just decide not to include it in the model. You know how to measure skill and run the regression with it included and find that some of the original coefficients change. What would explain this? Oa. The covariance between skill and each of the other regressors is zero. O b. The B for skill is zero. Oc. The covariance between skill and each of the other regressors is not zero. O d. The variance of skill is increasing. What are the forecasts for years 7,-8 and 9 using the following data and a second-order autoregressive model where = -3.06, $1 = 0.86 and $2 = 0.94. Year Data 4. 6. 8. 13 16 19 3. 4. O a. =11.980 8=14.800 9 = 23.561 Ob. 7= 28.320 8 39.155 g 57.234 O c. 7=31.140 f8 = 47.916 Models in which all the error terms have the same variance are said to exhibit... O a. Multicollinearity. O b. Homoscedasticity O C. Heteroscedasticity. O d. Autocorrelation. Clear…
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Example The marketing manager of Cadbury wants to test the effect of price on sales. Regression model: Sales = β0+ β1Price + error 1) In addition to price, the marketing researcher would like to find out whether there is a systematic trend in the sales. In other words, are sales increasing or decreasing over time? In order to test whether there is a systematic time trend in sales, in addition to the effects of price, please: Tip: the dependent variable is still sales. The independent variables would include price and weeknr (to capture time trend). Write the regression model for question 1 use the examle above to guide you?
Which of the following is false? If the correlation coefficient is 1, then the slope must be 1 as well. If the correlation between two variables is close to 0.01, then there is a very weak linear relation between them. Correlation coefficient and the slope always have the same sign (positive or negative). Correlation measures the strength of linear association between two numerical variables.
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Sam had 3 caffeinated drinks on april 24 Use linear regression to predict the number non-caffeinated drink he had on April 24 and calculate the error of estimate for your prediction.
Need help with d-h
Need help answering A, B and C. B) Perform the linear regression calculation and provide the linear regression equation that describes the relationship between Y (number of air conditioning units sold) and X (outside temperature in degrees Fahrenheit. Calculate: Coefficient of determination (r2) and the coefficient of correlation (r).  SST, SSE and SSR for this linear regression.  Estimate for the variance (σ2) and the standard deviation for the linear regression model you have developed. C) Using the linear regression equation that you developed in topic (b), calculate the estimated sales for a day that will reach 72 degrees F and for a day that will reach 94 F and for both temperature levels calculate the error “e” when comparing the estimated value against the actual data provided. At which of the two temperatures, is your model more accurate? Justify.
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