The accountant at Bintong Chen Coal Distributors, Inc., in Newark, Delaware, notes that the demand for coal seems to be tied to an index of weather severity developed by the U.S. Weather Bureau. When weather was extremely cold in the U.S.over the past 5 years (and the index was thus high), coal sales were high. The accountant proposes that one good forecast of next year's coal demand could be made by developing a regression equation and then consulting the Farmer'sAlmanac to see how severe next year's winter would be. The data for coal sales are shown below:Coal Sales, y (in millions of tons)Weather Index, x4145214The least-squares regression equation that shows the best relationship between coal sales and weather index is (round your responses to one decimal place):ý = D+ x,where y = Coal Sales and x = Weather Index.The coefficient of correlation of the data, r, = (round your response to three decimal places).The standard error of the estimate =(round your response to two decimal places).O 5

For the given data find Regression equation is = ? Correlation ( r ) =? Standard error of estimate…

Answered: The accountant at Bintong Chen Coal…

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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The maintenance manager at a trucking company wants to build a regression model to forecast the time (in years) until the first engine overhaul based on four predictor variables: (1) annual miles driven (in 1,000s of miles), (2) average load weight (in tons), (3) average driving speed (in mph), and (4) oil change interval (in 1,000s of miles). Based on driver logs and onboard computers, data have been obtained for a sample of 25 trucks. A portion of the data is shown in the accompanying table. Time Miles Load Speed Oil 7.7 42.9 22.0 44.0 16.0 0.8 98.3 20.0 47.0 34.0 6.3 61.1 22.0 62.0 15.0 E Click here for the Excel Data File b. Estimate the regression model. (Negative values should be indicated by a minus sign. Round your answers to 2 decimal places.) Time = Miles Load Speed oil + + d. What is the predicted time before the first engine overhaul for a particular truck driven 60,000 miles per year with an average load of 25 tons, an average driving speed of 53 mph, and 21,000 miles…
Sometimes, the pattern of coefficients on the year dummy variables is itself of interest. For example, a demographer may be interested in the following question: After controlling for education, has the pattern of fertility among women over age 35 changed between 1972 and 1984? The following table illustrates how this question is simply answered by using multiple regression analysis with year dummy variables. The table below presents the results from a study carried out on the total number of kids born to a woman. The factors that are controlled for are years of education, age, race, region of the country where living at age 16, and living environment at age 16. Interpret the results of the estimated model provided above. and why can we not use first differences when we have independent cross sections in two years (as opposed to panel data)?
Hormone replacement therapy (HRT) is thought to increase the risk of breast cancer. The accompanying data on x = percent of women using HRT and y = breast cancer incidence (cases per 100,000 women) for a region in Germany for 5 years appeared in the paper "Decline in Breast Cancer Incidence after Decrease in Utilization of Hormone Replacement Therapy." The authors of the paper used a simple linear regression model to describe the relationship between HRT use and breast cancer incidence. + ŷ = HRT Use Breast Cancer Incidence 46.30 40.60 39.50 36.60 30.00 USE SALT 103.30 105.00 100.00 93.80 83.50 (a) What is the equation of the estimated regression line? (Round your numerical values to four decimal places.) (b) What is the estimated average change in breast cancer incidence (in cases per 100,000 women) associated with a 1 percentage point increase in HRT use? (Round your answer to four decimal places.) cases per 100,000 women (c) What breast cancer incidence (in cases per 100,000 women)…
A researcher collected statistics on the sales amount of a product in 120 different markets and the advertising budgets used in TV, radio and newspaper media channels for each of these markets. The sales amount are expressed in 1000 units, and the budgets are expressed in 1000 $. The researcher wants to create a simple linear regression model by choosing one among the TV, radio and newspaper advertising budgets to explain the amount of sales.a) Which variable should this researcher choose as an independent variable to the simple regression model? Explain your decision by providing its statistical basis.(use formula)b) Construct the simple linear regression model using the argument you chose and write the equation of the model. Comment b0 and b1.(not excel use formula please)c) Test whether there is a statistically significant, linear relationship between the independent variable and the dependent variable by establishing the relevant hypotheses at the level of α = 0.05 significance.…
Imagine that you, a social psychologist, study people living with HIV. You are interested in the relationship between viral load, the amount of HIV in a sample of blood, and perceived stigma, the amount people with HIV perceive others to hold stigmatizing views about their condition. You have 100 people with HIV complete a perceived stigma scale, as well as provide a sample of blood, so their viral load can be measured. You run a regression analysis using the data you’ve collected to see whether perceived stigma can be predicted based on viral load. The output from the analysis is presented below: How much does the estimate of perceived stigma change if viral load increases by one unit?
The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, yˆ=b0+b1^x, for predicting a woman's bone density based on her age. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Age Bone Density35 35043 34053 33954 32155 310 Step 3 of 6 : Determine if the statement "Not all points predicted by the linear model fall on the same line" is true or false.
Hormone replacement therapy (HRT) is thought to increase the risk of breast cancer. The accompanying data on x = percent of women using HRT and y = breast cancer incidence (cases per 100,000 women) for a region in Germany for 5 years appeared in the paper "Decline in Breast Cancer Incidence after Decrease in Utilization of Hormone Replacement Therapy." The authors of the paper used a simple linear regression model to describe the relationship between HRT use and breast cancer incidence. t HRT Use Breast Cancer Incidence 46.30 40.60 39.50 36.60 30.00 103.30 105.00 100.00 93.80 83.50 (a) What is the equation of the estimated regression line? (Round your numerical values to four decimal places.) ŷ = (b) What is the estimated average change in breast cancer incidence (in cases per 100,000 women) associated with a 1 percentage point increase in HRT use? (Round your answer to four decimal places.) cases per 100,000 women (c) What breast cancer incidence (in cases per 100,000 women) would be…
An oil exploration company wants to develop a statistical model to predict the cost of drilling a new well. One of the many variables thought to be an important predictor of the cost is the number of feet in depth that the must be drilled to create the well. Consequently, the company decided to fit the simple linear regression model, where y = cost of drilling the new well (in $thousands) and x = number of feet drilled to create the well. Using data collected for a sample of n=83 wells, the following results were obtained: = 10.5 + 16.20x Give a practical interpretation of the estimate of the slope of the least squares line. An oil exploration company wants to develop a statistical model to predict the cost of drilling a new well. One of the many variables thought to be an important predictor of the cost is the number of feet in depth that the must be drilled to create the well. Consequently, the company decided to fit the simple linear regression model, where y =…
Suppose a researcher collects data on houses that have been sold in a particular neighbourhood over the past year, and obtains the regressions results in the table shown below. A family purchases a 2000 square foot home and plans to make extensions totalling 500 square feet. The house currently has a pool, and a real estate agent has reported that the house is in excellent condition. However, the house does not have a view, and this will not change as a result of the extensions. According to the results in column (1), what is the expected DOLLAR increase in the price of the home due to the planned extensions?
A retail company wants to understand the factors that impact its sales revenue. The company has collected data on the following variables for the past 5 months: Total sales revenue (Y), Average store foot traffic (XI), and Marketing budget (X3). Develop a multiple linear regression model to predict that, what is the impact of average store foot traffic, and marketing budget on total sales revenue for the retail company. The data is summarized in Table 4. Table 4 Average Store foot traffic (X1) Sales Revenue (Y) 50$ 5 130S 45$ 48$ 7 6 120$ 200$ 60$ 708 5 4 Marketing Budget (X2) 130$ 250$ Note: Average Store foot traffic is the average number of people enter in the store per minute
The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, yˆ=b0+b1^x, for predicting a woman's bone density based on her age. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Age Bone Density35 35043 34053 33954 32155 310 Step 4 of 6 : Determine the value of the dependent variable yˆ at x=0.
Suppose the following data were collected from a sample of 1515 CEOs relating annual salary to years of experience and the economic sector their company belongs to. Use statistical software to find the following regression equation: SALARYi=b0+b1EXPERIENCEi+b2SERVICEi+b3INDUSTRIALi+eiSALARY�=�0+�1EXPERIENCE�+�2SERVICE�+�3INDUSTRIAL�+��. Is there enough evidence to support the claim that on average, CEOs in the service sector have lower salaries than CEOs in the financial sector at the 0.010.01 level of significance? If yes, write the regression equation in the spaces provided with answers rounded to two decimal places. Else, select "There is not enough evidence." Copy Data CEO Salaries Salary Experience Service (1 if service sector, 0 otherwise) Industrial (1 if industrial sector, 0 otherwise) Financial (1 if financial sector, 0 otherwise) 144225144225 1010 11 00 00 187765187765 2020 00 00 11 142500142500 66 11 00 00 169650169650 2828 11 00 00 167250167250 3131 00…

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