;AUTO. Complete the following table and calculate the correlation coefficient, regression equation, and coefficient of determination, RSunlighthours, xSection IV- Regression & CorrelationyхуYield (flowers), y0.3110.6180.9291.2331.543ΣΣa.Correlation coefficient, r-b.Write down the regression equation:c. Use the regression equation to predict the flower yield if the amount of sunlight is 1.1 hours..CancelDone中。

Answered: Image /qna-images/answer/c330af4d-3276-45b3-918b-067e622eb82d.jpg

Answered: Complete the following table and…

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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A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=ax+b a=-1.307 b=32.392 r²=0.675684 r=-0.822 Use this to predict the number of situps a person who watches 12.5 hours of TV can do (to one decimal place)
The regression equation is: ŷ = 67.16 + 8.417x where ŷ is the miles traveled, and x is the MPG. The sample size used was all 110 MPG records. The correlation coefficient r = 0.620. Use the information to obtain an estimate of my mileage if my MPG is 22. Is it option: a.) cannot estimate ŷ rcrit = 0.195; the correlation IS NOT significant b.) ŷ = 252.33 rcrit = 0.195; the correlation IS significant c.) ŷ = 252.33 rcrit = 0.187; the correlation IS significant d.) cannot estimate ŷ rcrit = 0.187; the correlation IS NOT significant
Run a regression analysis on the following data set, where y is the final grade in a math class and x is the average number of hours the student spent working on math each week. hours/week Grade y 4 56.6 8 64.2 8 61.2 10 72 11 79.4 17 98.8 18 96.2 19 94.6 19 95.6 20 100 State the regression equation with constants accurate to 2 decimal places. = X + %3D What is the predicted value for the final grade when a student spends an average of 9 hours each week on math? Round to 2 decimal places.
The ages (in years) of five men and their systolic pressures. (You can use technology) Age, x 16 25 39 45 49 Systolic blood pressure, y 109 122 143 132 199 a. calculate the correlation coefficient, r b. calculate the critical r c. Is there a significant linear relationship between x and y? d. find the equation of regression line e Interpret the slope and the y-intercept f. Predict the systolic blood pressure for a man who is 40 years old.
Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (The pair of variables have a significant correlation.) Then use the regression equation to predict the valuo of v for each of the given x-values, if meaningful. The table below shows the heights (in feet) and the number of stories of six notable buildings in a city. Height, x Stories, y 762 621 508 480 (b) x = 641 feet (d) x = 726 feet 515 491 (a) x= 498 feet (c) x = 810 feet 51 46 44 42 38 37 Find the regression equation. ý=x+ (D (Round the slope to three decimal places as needed. Round the y-intercept to two decimal places as needed.) Choose the correct graph below. OA. OB O B. Oc. OD. 60- 604 60+ 800 B00 G 800 Height (feet) 800 Height (feet) 800 Height (feet) Height (feet) (a) Predict the value of y for x = 498. Choose the correct answer below. OA 40 ОВ. 50 O C. 47 O D. not meaningful (b) Predict the value of y for x 641. Choose the correct answer…
Consider the bivariate data below. Find the linear regression equation, round to one decimal place. Use regression equation from (1) to estimate y when x = 5. x 2 4 1 3 6 8 y 8 -2 15 6 -8 -15
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=a+bx b=-0.993 a=29.135 r2=0.463761 r=-0.681 Use this to predict the number of situps a person who watches 6 hours of TV can do. Round to one decimal place.
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y = a x + b a = -1.043 b = 29.088 %3D %3! r2 = 0.390625 r = -0.625 Use this to predict the number of situps a person who watches 6.5 hours of TV can do. situps : %3D [one decimal accuracy1
Which of the following is the most appropriate equation to model the data? ŷ = 1.1x + 1.467 ŷ = 1.467x + 1.1 ŷ = 1.1(1.467)x ŷ = 1.467(1.1)x

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