The following is a portion of a classic data set called the "pilot plot data" in FittingEquations to Data by Daniel and Wood, published in 1971. The response y is the acidcontent of material produced by titration, whereas the regressor x is the organic acidcontent produced by extraction and weighing.a. Fit a simple linear regression; estimate a slope and intercept.YX7612362556610058758815970109374882138881644328

The independent variable is organic acid content. The dependent variable is acid content of material…

Answered: The following is a portion of a classic…

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 following table shows the hot dogs bought from a street vendor over the course of eight days ("Demand"). Also shown is the temperature for each day in degrees Celsius. Complete parts a and b below. Temperature (°c) 22 25 19 8 11 17 20 Demand 46 28 35 42 18 25 43 35 a. Calculate the slope and y-intercept for the linear regression equation for these data. y = (Round to two decimal places as needed.)
Please provide solution and logic for questions attached, thanks!
A cafe company wants to determine how the money they spend on Google ads impacts their monthly revenue. Over 6 consecutive months, they vary the amount they spend on their Ads (in $) and record the associated revenue (in $) for each month. The data is shown below: l Revenue 50 427 75 472 100 467 125 529 150 518 175 543 A) Develop a regression equation for predicting monthly revenue based on the amount spent with Ads. What is the y-intercept? B) What is the sample correlation between these two variables? C) What is the slope of your regression equation? Give your answer to two decimal places. D) Using a 0.05 level of significance, does this regression equation appear to have any value for predicting revenue based on Ads?
please quickly thanks !!!!
Calculate the regression coefficient and obtain the lines of regression for the following data: USE MICROSOFT EXCEL
The invasive diatom species Didymosphenia geminata has the potential to inflict substantial ecological and economic damage in rivers. An article described an investigation of colonization behavior. One aspect of particular interest was whether y = colony density was related to x = rock surface area. The article contained a scatterplot and summary of a regression analysis. Here is representative data. x 50 71 55 50 33 58 79 26 y 157 1934 53 27 7 10 40 12 x 69 44 37 70 20 45 49 y 274 43 176 18 48 190 30 (a) Fit the simple linear regression model to this data. (Round your numerical values to three decimal places.) y = Predict colony density when surface area = 70 and calculate the corresponding residual. (Round your answers to the nearest whole number.) colony density corresponding residual Predict colony density when surface area = 71 and calculate the corresponding residual. (Round your answers to the nearest whole number.) colony…
Q.4. Obtain the equation of the line of regression of y on x from the following data and estimate the blood pressure when the age is 50 years. Age in years x Blood pressure y 50 142 40 120 71 162 35 110 60 140 45 125 52 155 47 140 36 111 40 135 65 150 59 153
At an online diamond trader site, the price for a diamond is dependent upon the weight of the diamond. The pricing information is given in the table below. x: Weight (in carats) 0.5 0.6 0.7 0.8 0.9 1.0 y: Price (in dollars) 2600 3000 3500 3950 4800 5700 a) Find the linear regression equation for this data where x is the weight of a diamond (in carats) and y is the corresponding price of the diamond (in $). The slope and y-intercept for the linear regression equation are as follows: slope = Blank 1. Fill in the blank, read surrounding text. y-intercept = Blank 2. Fill in the blank, read surrounding text. b) Use the linear regression equation to estimate the price of a diamond that weighs 0.65 carats. Blank 3. Fill in the blank, read surrounding text. dollars (Do not use the $ sign in the answer box.)
3. Find the equation of the regression line for the data and graph the scatter plot for the data No of ads (x) Sales (y) 2 5 8 8 10 12 2 4 7 9 10 Find yl at x = 47 ads
1. In a manufacturing process the assembly line speed (feet per minute) was thought to affect the number of defective parts found during the impaction process. To test this theory, managers devised a situation in which the same batch of parts was inspected visually at a variety of line speeds. They collected the following data. Line Speed (x) Number of Defective Parts Found (y) 20 21 20 19 40 15 30 16 60 14 40 17 Use Microsoft Excel to generate regression output for this problem and use the information in the output to answer the following questions.
Use the following table and information to answer questions 21 and 22: A company wants to build anew site with the number of bathrooms commensurate with the number of employees who will workthere. The analytics team decides to analyze a linear model of these data from extant sites to inform thisdecision before construction. Number of employees 650 730 810 900 102 107 1150 Number of bathrooms 40 50 54 61 82 110 121 21. The linear regression equation is:a. y-hat = 79.96 + 0.0094xb. y-hat = 0.0094 – 79.96xc. y-hat = 0.0094 – 79.96xd. y-hat = 79.96 – 0.0094x22. Predict how many bathrooms a new site with 1150 employees should have.a. 91b. 70c. 121d. The linear regression equation should not be used to make predictions.
A sales manager collected the following data on x = years of experience and y = annual sales ($1,000s). The estimated regression equation for these data is ý = 81 + 4x. Years of Annual Sales Salesperson Experience ($1,000s) 80 3 97 3 102 4 4 102 103 6. 8 101 10 119 8 10 123 9. 11 127 10 13 136 (a) Compute SST, SSR, and SSE. SST = SSR = SSE = (b) Compute the coefficient of determination r2. (Round your answer to three decimal places.) r2 = Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) O The least squares line did not provide a good fit as a small proportion of the variability in y has been explained by the least squares line. The least squares line provided a good fit as a large proportion of the variability in y has been explained by the least squares line. The least squares line provided a good fit as a small proportion of the variability in y has been explained by the least squares line. The least squares line did…

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