Problem 6. Our next attempt is a simple linear regression model using Income (in thousands of dollars)as the only predictor. Use the Parameter Estimates table (below) to answer all parts.Parameter EstimatesVariablet ValuePr >Parameter EstimateStandard Error655.64111.967654.7847(Intercept)1.81476e-771.811230.1881489.626605.46340e-16IncomeA.Write the equation used by this model to predict median school API from Income.t] Name three plots that we should look at to determine whether model assumptions are met.Construct (don't interpret) a 95% confidence interval for the population slope of the modelPredict the median school API for a neighborhood in which the median income is $40,000.D.E.With a margin of error 92.20867 and given these values and your answer to part D,construct and interpret a 90% prediction interval for the median school API in a neighborhoodwhose median income is $40,000

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Answered: Problem 6. Our next attempt is a simple…

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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1. Sue is conducting a regression analysis to predict the amount of salaries expense using the number of employees and the average rate of pay. This type of regression analysis is called a(n) independent regression. linear regression. multiple regression. dependent regression.
Please see problem with the solution: after reviewing the information below, assess the appropriateness and accuracy of using a linear regression model. Discuss the meaning of the standard error of the estimate and how it affects the predicted values of Y for that analysis. Many people are also using online shopping to avoid going to stores in person. I decided to use my own data. I am going to add how many Amazon transactions I have made each month from March- August. X= the month Y= amount of purchases Simple linear regression results: Dependent Variable: saleIndependent Variable: monthsale = 0.53333333 + 1.6 monthSample size: 6R (correlation coefficient) = 0.99410024R-sq = 0.98823529Estimate of error standard deviation: 0.36514837 Parameter estimates: Parameter Estimate Std. Err. Alternative DF T-Stat P-value Intercept 0.53333333 0.50269117 ≠ 0 4 1.0609562 0.3485 Slope 1.6 0.087287156 ≠ 0 4 18.330303 <0.0001 Analysis of…
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 y = 1,217.33 + 7.68x. Production Volume Total Cost ($) $ (units) 400 Submit Answer 450 550 600 700 750 4,100 4,900 5,400 5,900 6,500 (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 the nearest cent.) $6209.33 7,000 (b) Develop a 99% prediction interval for the total cost (in dollars) for next month. (Round your answers to the nearest cent.) X to $ X (c) If an accounting cost report at the end of next month shows that the actual production cost during the month was $6,700, should managers be concerned about incurring such a high total cost for the month? Discuss. Since $6,700 is within the prediction interval, managers should not be concerned about Incurring such a high total cost for one…
X Y Stress Frustration 2 1 2 2 3 4 3 4 3 5 Researcher wants to know if perception of stress (X) predicts a person's frustrationlevel (Y) and wants to create a regression equation. Calculate a (y-intercept)
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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.
The table below shows (lifetime) peptic ulcer rates (per 100 population), UU, for various family incomes, xx, as reported by the 1989 National Health Interview Survey. Income 4000 6000 8000 12000 16000 20000 30000 45000 60000 Ulcer rate 14.1 13.1 13.8 12.6 11.8 11.7 11.5 9.3 7.6 (a) Find the equation of the regression line. Ulcer rate, U(x)= . (b) Estimate the peptic ulcer rate for an income level of x0= 25000 according to the linear model in part (a). Ulcer rate, U(x0)= .

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