3. LINEAR REGRESSION AND CORRELATION COEFFICIENT:Consider the following data set:X-7-5 -1y1121-4(a) Use your calculator to find the linear regression equation for the data setabove.(b) Find the correlation coefficient for the data set.Calculator Tips: It's probably good to remember how to find the line of bestfit and the correlation coefficient by hand. But such computations can be verytedious if the data set is big.Your graphing calculator can find the linear regression equation and the cor-relation coefficient. Click here or here for video tutorials on performing linearregression and finding the correlation coefficient, r, using a calculator. The twolinked videos use different versions of the TI-89 calculator. The course textbookalso has instructions at the end of section 1.3 (pg. 80-82).

Answered: Image /qna-images/answer/0e64b1d6-29e1-477b-8cc9-e9f1d6b37d11.jpg

Answered: 3. LINEAR REGRESSION AND CORRELATION…

Calculus: Early Transcendentals

8th Edition

ISBN:9781285741550

Author:James Stewart

Publisher:James Stewart

Chapter1: Functions And Models

Section: Chapter Questions

Problem 1RCC: (a) What is a function? What are its domain and range? (b) What is the graph of a function? (c) How...

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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…
Part a: Make a scatter plot and determine which type of model best fits the data.Part b: Find the regression equation.Part c: Use the equation from Part b to determine y when x = 1.5.
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.
Below is the actual data on the speed a person walks or runs in miles/hour and the calories the person burns per lap. Find the linear equation of best fit for this data. Compare the model to the data. Does the model seem to fit the data? Explain. What is the significance of the y-intercept? What is the slope and what does it mean? What is the x-intercept and what does it mean? What is the correlation coefficient and what does it mean? Speed (mph) Calories/lap 1 42 2 31 3 27 4 41 5 40 6 40 7 39 8 38 r answers to the nearest hundredth. The y-intercept means that if a person runs at ----------mph they will burn__________ per lap. The slope means that for each mph you increase your speed, you will burn an additional------------calories pay lap
4) How to conduct multiple regression in Excel (Provide the steps)? Driving Experience (years) Monthly Auto Insurance Premium 5 $64 2 87 12 50 9 71 15 44 6 56 25 42 16 60 Does the insurance premium depend on the driving experience or does the driving experience depend on the insurance premium? Do you expect a positive or a negative relationship between these two variables?
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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