(b) Write the equation of the least-squares regression line for your data. Then on the scatter plot for your data, graph this regression equation by plotting twopoints and then drawing the line through them. Round each coordinate to three decimal places.Regression equation:2220-High temperature(in °C)(c) Use your regression equation to predict the amount of hot chocolate sold on a day with a high temperature of 17 °C. Round your answer to the nearestwhole number.Predicted amount of hot chocolate sold: litersAmount of hot chocolate sold(In liters)8图 x EspañYou are the owner of Fast Break, a popular local place that sells drinks, snacks, and sandwiches. For inventory management purposes, you are examining howthe weather affects the amount of hot chocolate sold in a day. You are going to gather a random sample of 9 days showing that day's high temperature (denotedby x, in °C) and the amount of hot chocolate sold that day (denoted by y, in liters). You will also note the productx.y of the temperature and amount of hotchocolate sold for each day. (These products are written in the row labeled "xy").(a) Click on "Take Sample" to see the results for your random sample.High temperature, x2215633283156.11(in °C)Amount of hotTake Samplechocolate sold, y(in liters)71217101415xy1321057299565115084165Send data to calculatorBased on the data from your sample, enter the indicated values in the column on the left below. Round decimal values to three decimal places. When youare done, select "Compute". (In the table below, n is the sample size and the symbol E xy means the sum of the values xy.)n: ISample correlation coefficient (r):y: 0Slope (b;):Ixy: 0y-intercept (6):Compute(b) Write the equation of the least-squares regression line for your data. Then on the scatter plot for your data, graph this regression equation by plotting twopoints and then drawing the line through them. Round each coordinate to three decimal places.Regression equation: y =3.

Answered: Image /qna-images/answer/f46b74e9-d217-4625-bab4-715af223ea01.jpg

Answered: (b) Write the equation of the…

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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We would like to study the relationship between the number of family members in a household a and number of meals they eat together y. A regression equation based on a sample from the study is shown below. ŷ = = 1.47 – 0.08x Estimate the number of meals shared by a family with 6 members. Select one: O a. 3 O b. 0 O c. 1 O d. 2
Use the time/tip data from the table below, which includes data from New York City taxi rides. (The distances are in miles, the times are in minutes, the fares are in dollars, and the tips are in dollars.) Find the regression equation, letting time be the predictor (x) variable. Find the best predicted tip for a ride that takes 20 minutes. How does the result compare to the actual tip amount of $4.55? Distance 0.68 2.47 12.71 1.80 1.02 8.51 1.32 1.40 6.00 18.00 27.00 25.00 8.00 31.00 8.00 18.00 6.30 14.30 36.80 16.30 7.80 31.75 7.80 12.30 1.89 4.29 0.00 1.50 2.34 2.98 0.00 2.46 Time Fare Tip The regression equation is y=+ X (Round the y-intercept to two decimal places as needed. Round the slope to four decimal places as needed.) The best predicted tip for a ride that takes 20 minutes is $ (Round to the nearest cent as needed.) How does the result compare to the actual tip amount of $4.55? O A. The best predicted value is close to the actual tip of $4.55. O B. The best predicted value…
Marina, Elena, Paloma, Alejandra and Rocío are 2, 3, 5, 7 and 8 years old and weigh 14, 20, 30, 42 and 44 kg respectively. With these data:(a) Find the equation of the regression line of age on weight.b) Estimate the approximate "weight" for a 6-year-old girl.
You are conducting a multiple regression analysis to determine if there is a linear relationship between y, the number of applications to in-state universities and two factors: ×1, the state's unemployment rate and ×2, the average grant provided to in-state students. Ten observations are available. The analysis produced the estimated regression equation below. y = 6.5 + 2.2×1 + 4.6×2 The standard error for the intercept, unemployment rate are 9.34, and 1.23, respectively. Is the coefficient for the unemployment rate variable statistically significant at the 5% significance level? Explain. A) No, since stat = 1.39 for this coefficient, which is less than 2.365. B) No, since the p-value for this coefficient is .072, which is greater than 05 C) None of these D) No, since the p-value for this coefficient is .072, which is greater than .05
The relationship between number of TikToks watched while studying and average grade on an exam is defined by the following linear regression equation: Ŷ= -0.3X + 70 Interpret the equation by answering the following: a. What is the slope? b. What does the slope tell us in this example?
Input 'X' Family Median Percentiles OutPut 'Y' Percent Of Children Attending College 100 94 1 25 Find The Linear equation using the above data.
The relationship between number of TikToks watched while studying and average grade on an exam is defined by the following linear regression equation: Y=-0.3X+70 Interpret the equation by answering the following: a. What is the slope? b. What does the slope tell us in this example?
The table and scatter plot show the time spent texting, x, and the time spent exercising, y, by each of 9 students last week. The equation of the line of best fit is y =-0.4x+6.99. Time spent Time spent 11+y texting, x exercising, y (in hours) 10 (in hours) 1.9 6.54 Time spent 2.2 5.48 exercising 2.8 6.94 (in hours) 3.5 5.16 4.1 4.50 4.6 4.30 4.9 6.50 5.5 3.36 Time spent texting 5.6 5.97 (in hours) Use the equation of the line of best fit to fill in the blanks below. Give exact answers, not rounded approximations. Observed time spent exercising (in hours) Predicted time spent exercising (in hours) Residual Time spent texting (in hours) (in hours) 4.1 4.9
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.
Flying twice as farA flight from Los Angeles to ABQ is about 2 hours but is 670.2 miles.A flight from San Jose to Chicago is 4 hours but is 1859.0 miles.Can anyone explain why the travel time from San Jose to Chicago is notlonger and closer to 5.75 hours?If the distance increase by 2, shouldnt the time increase by a factorof 2 as well?1. Write a linear model for this question. Takeoff and landing will take a fixed amount of time. Actualtravel in the air will take time proportional to the distance traveled. Think about which of thevariables (time and distance) is the independent variable, and identify the slope and intercept withtheir units.2. Use the data in the quotation to estimate the two constants in your linear model.
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 ------ ---- ----- ----- ----- ----- ---- ----- ----- ----- ----- ------ ----- ---- ---- --

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