EspañolThe scatter plot shows the number of years of experience, x, and the amount charged per hour, y, for each of 23 dog sitters in Michigan.(a) Write an approximate equation of the line of best fit for the data. It doesn't have to be the exact line of best fit.(b) Using your equation from part (a), predict the amount charged per hour by a dog sitter with 10 years of experience.Note that you can use the graphing tools to help you approximate the line.22-20-18-(a) Write an approximate equation of the line of best fit.16-14-Amount charged12(in dollars(per hour)(b) Using your equation from part (a), predict the amount10-charged per hour by a dog sitter with 10 years of experience.8-10 12 14 16 18 20 22YearsexperienceExplanationCheckO 2022 McGraw Hill LLC. All Rights Reserved. Terms of Use Privacy Center Accessibility

From the given scatterplot, we can approximate as : y intercept: 7 Let us consider two points (0…

Answered: The scatter plot shows the number of…

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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biologist was recording various temperatures (in degrees Celsius) and predicting the amount of glucose produced by a plant (in milligrams). Suppose the linear equation was the following: ?̂ = 12.7 − 0.223? a) Interpret the slope of the regression line.
An economist wants to predict the GDP per person measured in 2011 US dollars in Afghanistan based on Austria's GDP per person. She used data from 2000 to 2018. The regression equation was yhat= -5123.932 +0.152x Interpret the slope. For each additional dollar growth in GDP per person in Austria's, Afghanistan GDP per capita decrease on average by 0.152. O For each additional dollar growth in GDP per person in Austria, Afghanistan's GDP per capita increased on average by 0.152 O For each additional dollar growth in GDP per person in Afghanistan, Austria's GDP per capita increased on average by 0.152. For each additional dollar growth in GDP per person in Afghanistan, Austria's GDP per capita decreased on average by 0.152
Height Weight GPA Study TV Computer FamilySz Floors stairs friends female Acctg Mgmt Mktg Fin IntBus Other 67 138 4 6 2 1. 2 26 6. 1 73 175 3.47 5 4 26 1 62 112 4 6 2 1. 4 34 3 1 1. 64.5 122 3.5 3.4 1. 3 47 4 1 1. 69 135 4 4 2 4 13 1. 61 129 3.8 4 2 3 5 17 1. 1. 64 135 3.4 3 2 5 1. 1 1. 68 150 4 10 1 4 2 31 4 1 1 70 130 3.8 4 1 2 4 2 26 8. 1 65 172 3.6 2.5 1.5 0.5 4 2 26 8. 1 74 235 2.6 1. 1. 1. 4 3 52 1. 69 150 3 2 3 3 4 3 26 1. 72 152 2.8 2.5 0.5 3 5 3 30 10 1. 73 155 3 1. 2 4 2 26 1 74 170 3.75 1.5 1.5 2 15 5 1. 68 150 3 2.5 1. 4 3 26 10 1 63 115 2.5 3 2.5 3 2 13 8 1. 1 71 180 2.1 1 3 3 4 13 10 1 72 167 2.7 2. 4 4 2 18 3 72 180 3.8 2 1.5 0.5 1. 300 68.575 152.6 3.341 3.395 1.55 1.75 4.1 2.2 38.25 5.8 s= 4.146257 28.65842 0.584798 2.137257 0.759155 1.409554 0.640723 0.695852 62.7215 2.587419 ------ ---- ----- ----- ----- ----- ---- ----- ----- ----- ----- ------ ----- ---- ---- --
How would I approach this problem?
How would I approach this problem?
A survey was distributed to a group of people asking them how many times they had ordered takeout in the past week. They were also asked how much money they saved that week. The scatter plot displays the relationship between the data collected. the trend line goes through the points (1,230) and (3,190). What is the equation of the trend line. The equation should be in slope form. interpret the slope and y intercept in this context. predict the amount of money a person save when they order takeout 9 times.
How would I approach this problem?
Using the weights (lb) and highway fuel consumption amounts (mi/gal) of the 48 cars listed in the accompanying data set, one gets this regression equation: y = 58.9-0.00749x, where x represents weight. Complete parts (a) through (d). Click the icon to view the car data. The site to your captio V.UVITU. D. The slope is -0.00749 and the y-intercept is 58.9. c. What is the predictor variable? ... OA. The predictor variable is highway fuel consumption, which is represented by x. OB. The predictor variable is highway fuel consumption, which is represented by y. C. The predictor variable is weight, which is represented by x. OD. The predictor variable is weight, which is represented by y. d. Assuming that there is a significant linear correlation between weight and highway fuel consumption, what is the best predicted value for a car that weighs 2994 lb? The best predicted value of highway fuel consumption of a car that weighs 2994 lb is (Round to one decimal place as needed.) mi/gal.
Range of ankle motion is a contributing factor to falls among the elderly. Suppose a team of researchers is studying how compression hosiery, typical shoes, and medical shoes affect range of ankle motion. In particular, note the variables Barefoot and Footwear2. Barefoot represents a subject's range of ankle motion (in degrees) while barefoot, and Footwear2 represents their range of ankle motion (in degrees) while wearing medical shoes. Use this data and your preferred software to calculate the equation of the least-squares linear regression line to predict a subject's range of ankle motion while wearing medical shoes, ?̂ , based on their range of ankle motion while barefoot, ? . Round your coefficients to two decimal places of precision. ?̂ = A physical therapist determines that her patient Jan has a range of ankle motion of 7.26°7.26° while barefoot. Predict Jan's range of ankle motion while wearing medical shoes, ?̂ . Round your answer to two decimal places. ?̂ = Suppose Jan's…
The scatter plot shows the time spent watching TV, x, and the time spent doing homework, y, by each of 23 students last week. (a) Write an approximate equation of the line of best fit for the data. It doesn't have to be the exact line of best fit. (b) Using your equation from part (a), predict the time spent doing homework for a student who spends 15 hours watching TV. Note that you can use the graphing tools to help you approximate the line. Time spent doing homework (in hours) 32 28 24 20 16+ 12 8 4 0 y XX 4 X X X X X X X X 8 12 X X 16 X X X X -X X 20 24 Time spent watching TV (in hours) X X X 28 32 X X Ś (a) Write an approximate equation of the line of best fit. y = 0 (b) Using your equation from part (a), predict the time spent doing homework for a student who spends 15 hours watching TV. hours X S
The scatter plot shows the time spent watching TV, x, and the time spent doing homework, y, by each of 23 students last week. (a) Write an approximate equation of the line of best fit for the data. It doesn't have to be the exact line of best fit. (b) Using your equation from part (a), predict the time spent doing homework for a student who spends 15 hours watching TV. Note that you can use the graphing tools to help you approximate the line. Time spent doing homework Kin hours) 32- 28- Y 244 20- 124 F 44 B * K 12 x 14 20 24 26 Time spent watching TV (in hours) 32 X (a) Write an approximate equation of the line of best fit. = 0 (b) Using your equation from part (a), predict the time spent doing homework for a student who spends 15 hours watching TV. hours
The scatter plot shows the average monthly temperature, x, and a family's monthly heating cost, y, for 25 different months. (a) Write an approximate equation of the line of best fit for the data. It doesn't have to be the exact line of best fit. (b) Using your equation from part (a), predict the monthly heating cost for a month with an average temperature of 25 °F. Note that you can use the graphing tools to help you approximate the line. 100- y 90- x 80- × × xx 70- Monthly x x heating cost 60- × (in dollars) 50- × 40- 30- 20- 10- × x x x x x 0 10 20 30 40 50 60 70 80 90 100 Average monthly temperature (in °F) (a) Write an approximate equation of the line of best fit. y = (b) Using your equation from part (a), predict the monthly heating cost for a month with an average temperature of 25 °F. $ ☐ G

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