KThe table below shows the amounts of crude oil (in thousands of barrels per day) produced by a country and the amounts of crude oil (in thousands of barrels per day) imported by a country, for the lastseven years. Construct and interpret a 95% prediction interval for the amount of crude oil imported by the this country when the amount of crude oil produced by the country is 5,543 thousand barrels perday. The equation of the regression line is y=-1.085x+15,662.491.Oil produced, xOil imported, y5,8479,3415,6969,1375,6499,612www5,48910,0165,18010,1895,06510,1285,04210,013CConstruct and interpret a 95% prediction interval for the amount of crude oil imported when the amount of crude oil produced by the country is 5,543 thousand barrels per day. Select the correct choicebelow and fill in the answer boxes to complete your choice.(Round to two decimal places as needed.)OA. We can be 95% confident that when the amount of oil produced is 5,543 thousand barrels per day, the amount of oil importedwillbe between andthousand barrels per day.OB. There is a 95% chance that the predicted amount of oil imported is between and thousand barrels per day, when there are 5,543thousand barrels per day produced.

From the information, given thatLet X denotes the oil produced as independent variableLet Y denotes…

Answered: K The table below shows the amounts of…

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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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
A box office analyst seeks to predict opening weekend box office gross for movies. Toward this goal, the analyst plans to use online trailer views as a predictor. For each of the 66 movies, the number of online trailer views from the release of the trailer through the Saturday before a movie opens and the opening weekend box office gross (in millions of dollars) are collected and stored in the accompanying table. A linear regression was performed on these data, and the result is the linear regression equation Yi=−0.840+1.4108Xi. Determine the coefficient of determination,r2,and interpret its meaning. Determine the standard error of the estimate. How useful do you think this regression model is for predicting opening weekend box office gross? Can you think of other variables that might explain the variation in opening weekend box office gross?
Explain how to find se
A box office analyst seeks to predict opening weekend box office gross for movies. Toward this goal, the analyst plans to use online trailer views as a predictor. For each of the 66 movies, the number of online trailer views from the release of the trailer through the Saturday before a movie opens and the opening weekend box office gross (in millions of dollars) are collected and stored in the accompanying table. A linear regression was performed on these data, and the result is the linear regression equation Yi=−1.254+1.3968Xi. Complete parts (a) through (d). a. Determine the coefficient of determination,r2,and interpret its meaning. b. Determine the standard error of the estimate. c. How useful do you think this regression model is for predicting opening weekend box office gross? d. Can you think of other variables that might explain the variation in opening weekend box office gross?
A grocery store manager did a study to look at the relationship between the amount of time (in minutes) customers spend in the store and the amount of money (in dollars) they spend. The results of the survey are shown below. Time 10 8 6 13 19 18 27 10 7 Money 58 25 41 51 84 81 87 45 15 1. The equation of the linear regression line is: ˆyy^ = ______ +_____ xx (Please show your answers to two decimal places)
The table below shows the average weekly wages (in dollars) for state government employees and federal government employees for 10 years. Construct and interpret a 99% prediction interval for the average weekly wages of federal government employees when the average weekly wages of state government employees is $925. The equation of the regression line is
A financial website reported the beta value for a certain company was 0.86. Betas for individual stocks are determined by simple linear regression. The dependent variable is the total return for the stock, and the independent variable is the total return for the stock market, such as the return of a market index. The slope of this regression equation is referred to as the stock's beta. Many financial analysts prefer to measure the risk of a stock by computing the stock's beta value. Suppose the following data show the monthly percentage returns for the market index and the company for a recent year. Month Market Index% Return Company% Return August -3 4 September 8 7 October 0 1 November -2 1 December -5 0 January 0 0 February 7 7 March 0 -2 April 2 0 May -5 -1 a. Develop the least squares estimated regression equation. (Let x = Market Index % Return (as a %), and let y = Company % Return (as a %). Round your numerical values to four decimal places.)
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Give an equation for a linear regression model for these data to predict the weight from the fuel capacity, Identify the vanables in your equation. • Justify how well your linear equation fits the model by discussing a scatter plot of the date and the correlation coetlicient in assessing the fit of your linear model. • Use your model to predict the total weight of a small motorcycle that has a tuel capacity of 6 gallons. and discuss the usefulness of this prediction.

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