Amazon RevenueThe annual revenue of Amazon is given in the table below (source ).Year20202019201820172016201520142013201220112010Amazon AnnualRevenue(Billions of US dollars)386.064280.522232.887177.866135.987107.00688.98874.45261.09348.07734.204See the Regression page in the Chapter 1: Linear Equations and Graphs module for details oncreating a regression model.Apply linear regression to the data in the table to find a modely = mx + b,where y is Amazon's annual revenue in billions of US dollars, and is the number of years since2010. In what year will the estimated revenue of Amazon be 1,040 billion US dollars? Note: I am asking forthe year, not the number of years since 2010. This may happen between two years. Round to thenearest year.

Answered: The annual revenue of Amazon is given…

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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Regression and correlation analysis both describe the strength of linear relationships between variables. Consider the concepts of education and income. Many people would say these two variables are related in a linear fashion. As education increases, income usually increases (although not necessarily at the same rate). Can you think of two variables that are related in such a way that their relationship changes over their range of possible values (i.e., in a curvilinear fashion)? How would you analyze the relationship between two such variables?
You estimate a regression equation that estimates individuals’ yearly income as a function of age (measured in years), years of education, and their score on a standardized intelligence exam (IQ test). The estimated coefficients are: I = 26.3 + 4.21*Age + 7.21*Education + .23 * IQ Where I is income measured in thousands of dollars. Based on the regression results, how much more should an individual earn after attending graduate school for two years? Explain your reasoning.
A linear regression equation has a slope of 4 and an intercept of 2. What is the predicted value of Y for an X of 8? Edit View Insert Format Tools Table
Develop an estimated regression equation with annual income and household size as the independent variables. Can you explain what to put into excel to generate regression, using the data analysis tool Below is the data Income($1000s) HouseholdSize AmountCharged ($) 54 3 4,016 30 2 3,159 32 4 5,100 50 5 4,742 31 2 1,864 55 2 4,070 37 1 2,731 40 2 3,348 66 4 4,764 51 3 4,110 25 3 4,208 48 4 4,219 27 1 2,477 33 2 2,514 65 3 4,214 63 4 4,965 42 6 4,412 21 2 2,448 44 1 2,995 37 5 4,171 62 6 5,678 21 3 3,623 55 7 5,301 42 2 3,020 41 7 4,828 54 6 5,573 30 1 2,583 48 2 3,866 34 5 3,586 67 4 5,037 50 2 3,605 67 5 5,345 55 6 5,370 52 2 3,890 62 3 4,705 64 2 4,157 22 3 3,579 29 4 3,890 39 2 2,972 35 1 3,121 39 4 4,183 54 3 3,730 23 6 4,127 27 2 2,921 26 7 4,603 61 2 4,273 30 2 3,067 22 4 3,074 46 5 4,820 66 4 5,149
A. run a simple regression- dependent variable is Weeks, independent variable is Age. B. run a multiple regression with dependent variable weeks and independent variable-age, married, head, manager and sales. C. Create the regular and standardized residual plots for both. Please show the tables when entering values of the regression for both the outputs and the scatter plots.
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Amazon Revenue The annual revenue of Amazon is given in the table below (source ). Year 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 Amazon Annual Revenue (Billions of US dollars) 386.064 280.522 232.887 177.866 135.987 107.006 88.988 74.452 61.093 48.077 34.204 See the Regression page in the Chapter 1: Linear Equations and Graphs module for details on creating a regression model. Apply linear regression to the data in the table to find a model y = mx + b, where y is Amazon's annual revenue in billions of US dollars, and is the number of years since 2010.
I need help with this math problrm in the photo!
XLMiner Analysis ToolPak A В D G K M N P 1 Salesperson Years of Experience Annual Sales ($1000s) 78 SUMMARY OUTPUT 2 3 Anova: Single Factor Regression Statistics Multiple R R Square Adjusted R Square Standard Error 3 3 96 4 3 5 88 0.907961759 Anova: Two-Factor With Replication 4 4 107 0.824394555 6 5 6 105 0.802443875 Anova: Two-Factor Without Replication 7 7 116 8.072755139 8 7 10 123 Observations 10 Correlation 9 8 117 Covariance 10 9 10 122 ANOVA 11 10 14 139 df SS MS F Significance F Regression Descriptive Statistics 12 1 2447.544996 2447.544996 37.55667406 0.000280582 13 Residual 8 521.3550043 65.16937553 Total 9 2968.9 Exponential Smoothing 14 15 F-Test Two-Sample for Variances 16 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95% Upper 95% Intercept Years of Experience 4.575705731 0.746645624 6.128350028 0.000280582 2.853937836 6.297473627 2.853937836 6.297473627 17 76.61248931 5.883832818 13.02084741 1.14813E-06 63.04434651 90.18063211 63.04434651…
The value of a sports franchise is directly related to the amount of revenue that a franchise can generate. The following data represents the value in 2014 (in $millions) and the annual revenue (in $millions) for the 30 Major League Baseball franchises. Suppose you want to develop a simple linear regression model to predict franchise value based on annual revenue generated. Team Revenue Value Baltimore 245 1000 Boston 370 2100 Chicago White Sox 227 975 Cleveland 207 825 Detroit 254 1125 Houston 175 800 Kansas City 231 700 Los Angeles Angels 304 1300 Minnesota 223 895 New York Yankees 508 3200 Oakland 202 725 Seattle 250 1100 Tampa Bay 188 625 Texas 266 1220 Toronto 226 870 Arizona 211 840 Atlanta 267 1150 Chicago Cubs 302 1800 Cincinnati 227 885 Colorado…

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