In the following time series linear regression equation, t is time in years and y issales in millions of dollars:ýt =Interpret the regression coefficient.= 11. 602 + 3.46tFor every one year increase in time, sales increase by $3.46 million on average.For every one year increase in time, sales decrease by $3.46 million on average.As time increases, sales fluctuate around a constant mean of $3.46 million.For every one year increase in time, sales decrease by $346 million on average.

Given time series linear regression equation, yt^=11.602+3.46t where 't' is time in years and 'y'…

Answered: In the following time series linear…

Advanced Engineering Mathematics

10th Edition

ISBN:9780470458365

Author:Erwin Kreyszig

Publisher:Erwin Kreyszig

Chapter2: Second-order Linear Odes

Section: Chapter Questions

Problem 1RQ

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While collecting data on shoe sizes (y) compared to heights in inches (x) of male students in the class a student found that the shoe sizes ranged from size 7 to size 11 and the heights of the students ranged from 60 to 72 inches. The student ran a linear regression to get the function = 1 -x- 3 13 where y is the shoe size and is the height of the person in inches. What would be the shoe size for a student who was 73 inches tall? Shoe sizes only show up in incriments of 0.5 (ie. 7, 7.5, 8, 8.5, 9, 9.5, ....) so round to the nearest 1/2 shoe size. Show all your work.
Data from 147 colleges from 1995 to 2005 (Lee,2008) were tested to predict the endowments (in billions) to a college from the average SAT score of students attending the college. The resulting regression equation was Y = -20.46 + 4.06 (X). This regression indicates that: a. for every one-point increase in SAT scores, a college can expect 4.06 billion more in endowments. b. most colleges have very high endowments. c. for every one-point increase in SAT scores, a college can expect 20.46 billion fewer in endowments. d. for every one-dollar increase in endowments, the college can expect a half-point increase in SAT scores.
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
Bankers at a large financial institution created the linear regression model d = 0.37 – 0.0004s to predict the proportion of customers who would default on their loans, d, based on the customer's credit score, s. For a customer with a credit score of 700, which of the following is true? A The default proportion is predicted to be 0.09. В The default proportion will be 0.09. The default proportion is predicted to be approximately 1.75 million. The default proportion will be approximately 1.75 million. E The default proportion is predicted to be 0.28.
Use Excel to Run the Regressions:1. Wage and Age: Do you think a worker’s wage is related to his/her age? It is expected that wagesincrease with age up to certain age, after that wages go down. The “Wage Quadratic Data” fileposted contains data on the hourly wage (in $) for construction workers, workers’ ageand whether the worker has a bachelor’s degree or not (Graduate equals 1 if the worker has abachelor’s degree and 0 otherwise). Use the data file to answer the following questions:A) Build a model to predict the hourly wage of construction workers based on their education andage.B) Use the model to predict the hourly wage for a college graduate construction worker if his/herage is 30, 50, and 70c) Based on your model, calculate the age at which the hourly wage for construction workers ismaximized.
In linear regression analysis, the coefficient for the x-variable when the y-variable is regressed on the x-variable can be thought of as: Note: more than one answer may be correct Group of answer choices How much the value of the predicted y-variable will change when the x-variable changes by one unit. In the simple (two variable) linear regression model, the coefficient can be thought as the slope coefficient that measures the responsiveness of y to changes in x. The estimated coefficient will change if the sample containing x and y changes. The sample coefficient is an estimate of the population coefficient Before interpreting the coefficient for the x-variable, we should test whether the coefficient is statistically significant.
There is a linear relationship between the number of chirps made by the stiped ground cricket and the air temperature. It was determined that the linear regression model is: y = 25.2 + 3.3x where x is the number of chirps per minute and y is the estimated temperature in degrees Fahrenheit. What is the temperature if a cricket chirps 18 times? Round to the nearest degree. Di not include units.
A regression was run to determine if there is a relationship between hours of TV watched per day (xx) and number of situps a person can do (yy).The results of the regression were:y=ax+b a=-0.629 b=39.045 r2=0.806404 r=-0.898 Use this to predict the number of situps a person who watches 6.5 hour(s) of TV can do, and please round your answer to a whole number.
The average top ticket price for Broadway musicals has increased dramatically between 1975 and 2003. 1985 1998 2000 2003 72.83 118.97 130.15 44.99 Year Average Ticket Price 1975 13.63 a. Using the data in the table, find the linear regression equation that best fits the data. (Let x = 0 represent the number of years since 1970.) y = (Round values to three decimal places.) b. Predict the average top ticket price for Broadway musicals in the year 2012 if the price continues to increase at the same rate. (Let x represent the number of years since 1970.) (Round to two decimal places.) c. Find the rate at which the price is rising. per year (Round to two decimal places.)

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