Send data to ExcelAfter deciding on the appropriateness of a linear model relating coffee sales and maximum temperature, the managers calculate the equation of the least-squares regression line to be y =2493.30-10.32x. This is the line shown in Figure 1.Answer the following:1. Fill in the blank: For these data, temperature values that aregreater than the mean of the temperature values tend to be pairedwith coffee sales values that arevalues.Choose onethe mean of the coffee sales2. Fill in the blank: According to the regression equation, for anincrease of one degree in temperature, there is a correspondingChoose one vof 10.32 dollars in coffee sales.3. What was the observed coffee sales value (in dollars) when thetemperature was 74.0 degrees Fahrenheit?4. From the regression equation, what is the predicted coffee salesvalue (in dollars) when the temperature is 74.0 degrees Fahrenheit?(Round your answer to at least one decimal place.)ContinueSubmit AssO 2021 McGraw-Hill Education. All Rights Reserved. Terms of Use | PrivacyIP Type here to search99+ Managers of an outdoor coffee stand in Coast City are examining the relationship between (hot) coffee sales and daily temperature, hoping to be able to predicta day's total coffee sales from the maximum temperature that day. The bivariate data values for the coffee sales (denoted by y, in dollars) and the maximumtemperature (denoted by x, in degrees Fahrenheit) for each of sixteen randomly selected days during the past year are given below. These data are plotted inthe scatter plot in Figure 1.Temperature, x(in degreesFahrenheit)Coffee sales, y(in dollars)71.51970.958.71953.953.71791.12400+83.01570.32200+62.81852.774.61633.62000-**40.41973.91800-51.52250.944.61808.51600-45.51977.31400-45.62190.5120069.41789.155.01598.25070809040.62272.074.01937.0Figure 176.61547.1Send data to ExcelContinueSubmit AssignmentO 2021 McGraw-Hill Education. All Rights Reserved. Terms of Use Privacy Accessibilityhere to search99+54.

After deciding on the appropriateness of a linear model relating coffee sales and maximum temperature, the managers calculate the equation of the least- squares regression line to be y =2493.30- 10.32x. This is the line shown in Figure 1. Answer the following: 1. Fill in the blank: For these data, temperature values that are greater than the mean of the temperature values tend to be paired with coffee sales values that are values. Choose one the mean of the coffee sales 2. Fill in the blank: According to the regression equation, for an increase of one degree in temperature, there is a corresponding of 10.32 dollars in coffee sales. Choose one v 3. What was the observed coffee sales value (in dollars) when the temperature was 74.0 degrees Fahrenheit? 4. From the regression equation, what is the predicted coffee sales value (in dollars) when the temperature is 74.0 degrees Fahrenheit? (Round your answer to at least one decimal place.)

After deciding on the appropriateness of a linear model relating coffee sales and maximum temperature, the managers calculate the equation of the least- squares regression line to be y =2493.30- 10.32x. This is the line shown in Figure 1. Answer the following: 1. Fill in the blank: For these data, temperature values that are greater than the mean of the temperature values tend to be paired with coffee sales values that are values. Choose one the mean of the coffee sales 2. Fill in the blank: According to the regression equation, for an increase of one degree in temperature, there is a corresponding of 10.32 dollars in coffee sales. Choose one v 3. What was the observed coffee sales value (in dollars) when the temperature was 74.0 degrees Fahrenheit? 4. From the regression equation, what is the predicted coffee sales value (in dollars) when the temperature is 74.0 degrees Fahrenheit? (Round your answer to at least one decimal place.)

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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Researchers used the simple linear regression model to study the relationship between social media screen time during a week and end-of-week quiz results. As one of the researchers, find the predicted quiz score if a student spends 18.46 hours during the next week using social media. Use the data in the table below to build the estimated regression equation and then use this equation to find the predicted quiz score: Social media weekly time, hours 14.5 3.7 30.4 34.23 2.7 16.8 13.6 4,7 20,6 4.9 Round your answer to 2 decimals. Less I Quiz results, out of 100 76.8 98.45 57.6 45.5 74.78 75.7 93.67 57.8 35.7 94.8
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.
Please answer parts d, e and f.
Please help me better understand this problem. Researchers want to determine if heavier cars use more gasoline? To answer this question, a researcher randomly selected 15 cars. He collected their weight (in hundreds of pounds) and the mileage (MPG) for each car. From a scatterplot made with the data, a linear model seemed appropriate and the following output was obtained in photo below. Question: Determine the proportion of the variation in mileage is accounted for by the linear relationship with the weight of the car and also determine the estimate of the regression standard deviation.
A researcher wanted to predict the sodium content (in milligrams) of beef hot dogs by looking at the calorie content. His findings from a sample of 10 beef hot dogs are summarized below. The regression equation for his data is ŷ= -299.48 + 4.35 x Calories 186 181 176 149 184 190 158 139 175 148 Sodium 495 477 425 322 482 587 370 322 479 375 Interpret the slope in terms of this problem. If appropriate, find the sodium content if the calorie content is 170. If not, why? If appropriate, find the sodium content if the calorie content is 136. If not, why?
Brandon works as a statistician for the Toronto Blue Jays, and wants to analyze the relationship between a player's age and how many strikeouts they accumulate in a season. He takes a sample of 8 Blue Jays players with age between 25 and 34 and finds there is a linear relationship between their ages and the number of strikeouts they had in the 2015 season. Here are the numerical summaries for age and the number of strikeouts: r = 0.67, age = 28.4, Sage = 3.96, strikeout= 102.9, S strikeout = 7.7 (a) What is the value of b₁, i.e. the fitted slope? (Round your answer to 3 decimal places) Answer: (b) What the value of bo, i.e. the fitted intercept? (Round your answer to 3 decimal places.) Answer: (c) What is the percent of variation of the number of strikeouts that is explained by age using a linear regression? (Round your answer to 2 decimal places.) Answer: % (d) Can we use this linear regression to predict the number of strikeouts for a player age 38? Answer: O No, because the…
Put the following steps, for finding a linear model for a set of data using a TI-84 calculator, in the correct order. 1 [Choose] [Choose] Enter your data in STAT-->EDIT with inputs in L1 and outputs in L2 Press STAT-->CALC to access the menu of possible models. Read off your answer, being careful to match the slope to the coefficient of x. Be sure XList: L1, YList: L2, Freg List: (empty) and select "Calculate" Choose LinReg(ax+b) or LinReg(a+bx) [Choose] 3. 4. [Choose] [Choose ]
The datasetBody.xlsgives the percent of weight made up of body fat for 100 men as well as other variables such as Age, Weight (lb), Height (in), and circumference (cm) measurements for the Neck, Chest, Abdomen, Ankle, Biceps, and Wrist. We are interested in predicting body fat based on abdomen circumference. Find the equation of the regression line relating to body fat and abdomen circumference. Make a scatter-plot with a regression line. What body fat percent does the line predict for a person with an abdomen circumference of 110 cm? One of the men in the study had an abdomen circumference of 92.4 cm and a body fat of 22.5 percent. Find the residual that corresponds to this observation. Bodyfat Abdomen 32.3 115.6 22.5 92.4 22 86 12.3 85.2 20.5 95.6 22.6 100 28.7 103.1 21.3 89.6 29.9 110.3 21.3 100.5 29.9 100.5 20.4 98.9 16.9 90.3 14.7 83.3 10.8 73.7 26.7 94.9 11.3 86.7 18.1 87.5 8.8 82.8 11.8 83.3 11 83.6 14.9 87 31.9 108.5 17.3…
The following data show the brand, price ($), and the overall score for six stereo headphones that were tested by a certain magazine. The overall score is based on sound quality and effectiveness of ambient noise reduction. Scores range from 0 (lowest) to 100 (highest). The estimated regression equation for these data is ý = 22.525 + 0.325x, where x = price ($) and y overall score. %3D Brand Price ($) Score 180 78 B. 150 71 C 95 59 70 54 E 70 40 35 28 (a) Compute SST, SSR, and SSE. (Round your answers to three decimal places.) SST SSR %3D SSE = (b) Compute the coefficient of determination . (Round your answer to three decimal places.) 1 = Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) O The least squares line provided a good fit as a large proportion of the variability in y has been explained by the least squares line. O The least squares line did not provide a good fit as a large proportion of the variability in y…
An analyst hired by the multinational company to study the sales data of its more than 75 stores worldwide. Used regression analysis to predict $ sales (y) by using $ advertising (x1) and $ salary of sales representatives (x2) across all the branches. You obtained the following regression function: y = 7800 + 8.5x1-1.6x2. If the advertising budgets of one of the branches of the corporation is now $45,000 (which is 10% more than before) and the salary of sales representatives is now $8,500 (which is 20% less than before), then the predicted sales will a. increase by more than 10% b. increase by less than 10% c. decrease by less than 10% d. be the same
A linear relationship between EmployeeSalary (Dependent) and degree(independent) has the following equation : Salary = 400+0.2 (Degree). SST= 736, SSR= 385. Calculate and interpret the coefficient of determination (r2) : Select one: O a. 0.48 , 47.69 percent of the variability in employee salary can be explained by the simple linear regression equation Ob. 0.52,52.31 percent of the variability in employee salary can be explained by the simple linear regression equation Oc. 0.48, 47.69 percent of the variability in the degree earned can be explained by the simple linear regression equation F Od. 0.52, 52.31 percent of the variability in the degree earned can be explained by the simple linear regression equation Next page JUN 2 12 étv W Ps Lr
9 students were surveyed to see what their age is and what their income level is. Find the equation of the line using linear regression. We want to predict their age using their income. age 18 24 38 22 19 35 28 19 27 income 456 786 835 855 645 244 587 1400 975 Just a side note, the 19 year old is making 1400, would be considered an outlier since they are making way more than everyone else. (y=-.0061x+34.9874 4 decimals) y=30.3709-.0064