You may need to use the appropriate appendix table or technology to answer this question.The below data on the production volume x and total cost y (in dollars) for a particular manufacturing operation were used to develop the estimated regression equation ŷ = 1,213.33 + 7.6x.Production Volume Total Cost(units)($)4004505506007007504,000X4,9005,3006,0006,4006,900(a) The company's production schedule shows that 650 units must be produced next month. What is the point estimate of the total cost (in dollars) for next month? (Round your answer to thenearest cent.)$1(b) Develop a 99% prediction interval for the total cost (in dollars) for next month. (Round your answers to the nearest cent.)$1X to $1X(c) If an accounting cost report at the end of next month shows that the actual production cost during the month was $7,000, should managers be concerned about incurring such a high total costfor the month? Discuss.Since $7,000 is outside of ⒸX the prediction interval, managers should not beconcerned about incurring such a high total cost for one month.

Given: The production volume is denoted by 'x' and the total cost ($) is denoted by 'y'. Regression…

Answered: You may need to use the appropriate…

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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Yummy Lunch Restaurant needs to decide the most profitable location for their business expansion. Marketing manager plans to use a multiple regression model to achieve their target. His model considers yearly revenue as the dependent variable. He found that number of people within 2KM (People), Mean household income(income), no of competitors and price as explanatory variables of company yearly revenue. The following is the descriptive statistics and regression output from Excel. Revenue People Income Competitors Price Mean 343965.68 5970.26 41522.96 2.8 5.68 Standard Error 5307.89863 139.0845281 582.1376385 0.142857 0.051030203 Median 345166.5 6032 41339.5 3 5.75 Mode #N/A 5917 #N/A 3 6 Standard Deviation 37532.51115 983.47613 4116.334718 1.010153 0.360838027 Sample Variance 1408689393 967225.2984 16944211.51 1.020408 0.130204082 Sum 17198284 298513 2076148…
In R there is a dataset called diamonds that contains measurements of about 500 diamonds sold in the US. There are three variables present: price (price in US dollar), carat (weight of the diamond), and table (width of top of diamond relative to the widest point). The attached image is a screenshot of the R dataset with the regression table and all that. ANSWER THIS QUESTION IN WORDS: Discuss the regression between the variables table and price. You should address the explanatory variable, response variable, correlation, and sign. You should interpret the slope, the t and p-value, and how much is explained by the response.
You have gathered data from a random sample of fast-food sandwiches in order to better understand how the amount of fat in these sandwiches relates to the amount of carbohydrates in the sandwiches. Your ultimate goal is to construct a regression equation to predict amount of carbohydrates based on amount of fat. If this is your goal, which variable should you put on the vertical axis (or y-axis) of a scatterplot of this data? O When conducting a regression analysis, it makes no difference which variable is on which axis. O Amount of fat, because it is the explanatory variable. O Amount of carbohydrates, because it is the explanatory variable. Amount of carbohydrates, because it is the response variable. O Amount of fat, because it is the response variable.
9 students were surveyed to see what their age is and what their income level is. Find the equation of the line using linear regression. We want to predict their age using their income. age 18 24 38 22 19 35 28 19 27 income 456 786 835 855 645 244 587 1400 975 Just a side note, the 19 year old is making 1400, would be considered an outlier since they are making way more than everyone else. (y=-.0061x+34.9874 4 decimals) y=30.3709-.0064
The City of Bellmore’s police chief believes that maintenance costs on high-mileage police vehicles are much higher than those costs for low-mileage vehicles. If high-mileage vehicles are costing too much, it may be more economical to purchase more vehicles. An analyst in the department regresses yearly maintenance costs (Y) for a sample of 200 police vehicles on each vehicle’s total mileage for the year (X). The regression equation finds: Y = $50 + .030X with a r2 of .90 What is the IV? What is the DV? If the mileage increases by one mile, what is the predicted increase in maintenance costs? If a vehicle’s mileage for the year is 50,000, what is its predicted maintenance costs? What does an r2 of .90 tell us? Is this a strong or weak correlation? How can you tell?
Suppose that a simple linear regression model is appropriate for describing the relationship between y = house price (in dollars) and x = house size (in square feet) for houses in a large city. The population regression line is y = 22,500 + 43x and ?e = 4,000. b) Approximately what proportion of 2,000 sq ft homes would be priced over $110,000? (You may need to use a table. Round your answer to four decimal places.) Approximately what proportion of 2,000 sq ft homes would be priced under $100,000? (You may need to use a table. Round your answer to four decimal places.)
I just need the d section
The police chief believes that maintenance costs on high-mileage police vehicles are much higher than those costs for low-mileage vehicles. If high-mileage vehicles are costing too much, it may be more economical to purchase more vehicles. An analyst in the department regresses yearly maintenance costs (Y) for a sample of 200 police vehicles on each vehicle’s total mileage for the year (X). The regression equation finds: Y = $50 + .030X with a r2 of .90 If a vehicle’s mileage for the year is 50,000, what is its predicted maintenance costs? What does an r2 of .90 tell us? Is this a strong or weak correlation? How can you tell?
The following table shows the approximate amount of trash produced in an industrialized country from 1980 to 2000. Let x represent the year after 1980 (1980 is year 0) and y represent the amount of trash (millions of tons). Year Million Tons 1980 150 1990 202 2000 220 (a) Draw the graph for this data. (b) Find the equation of the regression line for the data. (Round your answers to one decimal place.)y = x + (c) Use the equation to predict the amount of trash y that will be produced in 2010 and 2015. (Round your answers to one decimal place.) 2010: _______million tons 2015: _______ million tons

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