A stock trader is monitoring the stock prices of two stocks: Stock A and Stock B. He wants toknow whether a relationship exist between the prices of the two stocks. A 10-dayobservation of the prices is given below:Stock A Stock B(X)(Y)9.5045.679.33 46.989.67 44.1610.12 42.8410.45 41.1711.50 40.9810.69 42.9411.01 42.359.36 44.868.8046.34 4. What is the equation of the regression line? [Select]5. Using the regression line model, predict the stock price of Stock B rounded off to twodecimal places when the stock price of Stock A is 14. [Select]

Use the Excel and Perform the Regression. Followings are the Steps to perform the Regression 1) Open…

Answered: 4. What is the equation 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

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Two variables have a positive linear correlation. Is the slope of the regression line for the variables positive or negative?
The regression model y = 5.5(1.025)* represents the number of items sold in millions (y) by a company each year (x) after 2010. Select all of the appropriate statements that apply to the model. O The number of items sold each year grows by 2.5%. O There were originally 550,000 items sold. O There were approximately 4.3 million items sold in 2000. O There will be approximately 7 million items sold in 2020. O There will be approximately 8 million items sold in 2030.
z score z score for each for each value of value of Zzły х х -0.278 0.536 -0.149 -0.089 0.696 -0.062 -1.253 -1 -1.652 2.070 -0.714 -0.928 0.663 2.461 1.473 1.671 0.536 0.371 0.199 -0.089 4 -0.278 0.025 Σ: ỹ = 3.857 S, = 3.078 = 5.207 T= 4.286 s, = 3.200
13/
6, 5. Kathy painted a picture and posted it on a social media site. The table shows the number of people who responded that they liked the picture since Kathy posted it. Time Since Picture Was Posted 5. 7. (days) Number of Likes 11 21 38 Write the regression equation that best models the data. a. y = 0.96(1. 55)* b. y = 2.07(1. 38)* c. y = 0.536x – 0.857x + 1.81 d. y = 1.38x´ + 0.30x + 0. 43
Develop a scatterplot and explore the correlation between customer age and net sales by each type of customer (regular/promotion). Use the horizontal axis for the customer age to graph. Find the linear regression line that models the data by each type of customer. Round the rate of changes (slopes) to two decimal places and interpret them in terms of the relation between the change in age and the change in net sales. What can you conclude? Hint: Rate of Change = Vertical Change / Horizontal Change = Change in y / Change in x
** Based on the regression results, answer the following questions ** A sample of data is collected (from 1999 and 2000) concerning the compensation of the executives (compensation is measured in 1000’s of $’s) of a number of public companies along with other firm-specific data. The dependent variable is total compensation, CEOANN is a dummy variable =1 for an individual who is a CEO and =0 for individuals who are not CEO’s, EMPL is total employees, MKTVAL is the natural logarithm of the market value of the firm, EPSIN is earnings per share, YEAR is a dummy variable = 1 for the year 2000 and =0 for year 1999, and ASSETS is the natural logarithm of the total assets of the company. Based on the regression results, answer the following questions b) What is the estimated regression equation? c) What percentage of the variation in income in explained by the regressors? d) What is the standard error of the error term in the regression equation?
M3
In a fisheries researchers experiment the correlation between the number of eggs in tge nest and the number of viable (surviving ) eggs for a sample of nests is r=0.67 the equation of the regression line for number of viable eggs y versus number of eggs in the nest x is y =0.72x + 17.07 for a nest with 140 eggs what is the predicted number of viable eggs ?
please answer the third and fourth part
The next four questions (29 to 32) refer to the following: To most Canadians, earthquakes are viewed as rare occurrences, but they are actually quite common. In just one month in 2001, Natural Resources Canada recorded 215 earthquakes that affected Canada from B.C. to Nunavut to Newfoundland. We would like to determine whether there is a linear relationship between the location of an earth- quake X (measured in degrees latitude north of the equator) and the magnitude of the earthquake Y (measured on the Richter scale). The explanatory and response variables are measured for a sample of 13 earthquakes. The equation of the least squares regression line is ŷ = -3.05+ 0.10x. The ANOVA table is showm below: ге Source of Variation df Sum of Squares Mean Square F Regression Error Total 1.40 25.10
The slope, b represents * O predicted value of Y when X = 0. O the estimated average change in Y per unit change in X. O variation around the line of regression. O the predicted value of Y.

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4. What is the equation of the regression line? [Select] 5. Using the regression line model, predict the stock price of Stock B rounded off to two Select] ✪ decimal places when the stock price of Stock A is 14.