2.2. The analyst created plots that have the following shapes. Below for example, the first plot issimply fitting a regression with say only bacteria B and its growth rate and the second plot is aresidual plot.بتنی برŶ-1.82 + .0435XHe then realises that a multiple regression of the first order will not fit the data appropriately.2.2.1. Provide the regression model, the regression function and the estimator (response function)for the model that he should use to fit the data. Provide the name of such a regression modeland point out the problems that can arise with these types of regression models. [Do notforget to centre]

Given information: The scatterplot and residual plot are given.

Answered: The analyst created plots that have the…

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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Answer the following questions. Use the same units as were used in the model. Be careful! What is the sample regression equation? Sales = + Population + Income As the number of women over the age of 60 increases by 1 million, sales of StrongBones is predicted to increase by $ million, holding income constant. Predict sales if a city has 2 million women over the age of 60 and their average income is $45,000. Answer in millions rounded to 2 decimal places. $ millions
please use chart attatched. a. Explain the meaning of the slope of the regression line in this context. b. What is the predicted cost of maintenance for someone who racks up about 2,000 miles per month on a sedan? c. Suppose one of the observations used to get the data table above was a sedan owner who reported he drove 980 miles this month and spent about $450 on maintenance costs in the same month. What is the residual of this observation?
The data from a study of orange juice produced at a juice manufacturing plant are in the table. The simple linear regression was used to predict the sweetness index (y) from the amount of pectin (x) in the orange juice. Complete parts a through c. view the table. xi yi8 23 37 22 61 71 63 5 Find the values of SSE, s2, and s for this regression. SSE=enter your response here (Round to four decimal places as needed.)
answer 6
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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.
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?

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The analyst created plots that have the following shapes. Below for example, the first plot is simply fitting a regression with say only bacteria B and its growth rate and the second plot is residual plot.