After deciding on the appropriateness of a linear model relating coffeesales and maximum temperature, the managers calculate the equationof the least-squares regression line to be p=2516.78 – 10.49.x. This is the lineshown 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 areChoose onethe mean of the coffee salesvalues.2. According to the regression equation, for an increase of one degreein temperature, there is a corresponding decrease of how manydollars in coffee sales?3. What was the observed coffee sales value (in dollars) when thetemperature was 68.3 degrees Fahrenheit?4. From the regression equation, what is the predicted coffee salesvalue (in dollars) when the temperature is 68.3 degrees Fahrenheit?(Round your answer to at least one decimal place.) Below are bivariate data giving birthrate and life expectancy information foreach of twelve countries. For each of the countries, both x, the number ofbirths per one thousand people in the population, and y, the female lifeexpectancy (in years), are given. Also shown are the scatter plot for thedata and the least-squares regression line. The equation for this line isy = 81.87– 0.46x.Birthrate, x(number ofbirths per 1000рop.)Female lifeexpectancy, y(in years)85-35.767.78041.563.931.963.375+メメ19.973.070-50.560.465-24.472.760-50.163.25513.872.55050.354.645.657.915.976.2Figure 126.671.9

1.From the regression equation we can see that the slope of the regression line is negative.…

аpproprie Bune sales and maximum temperature, the managers calculate the equation of the least-squares regression line to be =2516.78 – 10.49x. This is the line shown in Figure 1. Answer the following:

аpproprie Bune sales and maximum temperature, the managers calculate the equation of the least-squares regression line to be =2516.78 – 10.49x. This is the line shown in Figure 1. Answer the following:

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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The scatter plot below shows the average cost of a designer jacket in a sample of years between 2000 and 2015. The least squares regression line modeling this data is given by yˆ=−4815+3.765x. A scatterplot has a horizontal axis labeled Year from 2005 to 2015 in increments of 5 and a vertical axis labeled Price ($) from 2660 to 2780 in increments of 20. The following points are plotted: (2003, 2736); (2004, 2715); (2007, 2675); (2009, 2719); (2013, 270). All coordinates are approximate. Interpret the slope of the least squares regression line. Select the correct answer below: 1.The average cost of a designer jacket decreased by $3.765 each year between 2000 and 2015. 2.The average cost of a designer jacket increased by $3.765 each year between 2000 and 2015. 3.The average cost of a designer jacket decreased by $4815 each year between 2000 and 2015. 4. The average cost of a designer jacket increased by $4815 each year between 2000 and…
A real estate analyst has developed a multiple regression line, y = 60 + 0.068 x1 – 2.5 x2, to predict y = the market price of a home (in $1,000s), using independent variables, x1 = the total number of square feet of living space, and x2 = the age of the house in years. The regression coefficient of x2 suggests this: __________. If the square feet area of living space is kept constant, a 1 year increase in the age of the homes will result in a predicted drop of $2500 in the price of the homes If the square feet area of living space is kept constant, a 1 year increase in the age of the homes will result in a predicted increase of $2500 in the price of the homes Whatever be the square feet area of the living space, a 1 year increase in the age of the homes will result in a predicted increase of $2500 in the price of the homes Whatever be the square feet area of the living space, a 1 year increase in the age of the homes will result in a predicted drop of $2500 in the price of the homes
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Q5/ Use Linear Regression to fit the following data: X 1 2 4 5 6 Y 4 10 10 9 3
The table below shows the average weekly wages (in dollars) for state government employees and federal government employees for 10 years. Construct and interpret a 99% prediction interval for the average weekly wages of federal government employees when the average weekly wages of state government employees is $818. The equation of the regression line is y = 1.451x - 40.698. Wages (state), x Wages (federal), y 713 760 785 805 851 870 921 926 947 985 1,002 1,045 1,091 1,145 1,185 1,239 1,281 1,307 1,320 1,400 Construct and interpret a 99% prediction interval for the average weekly wages of federal government employees when the average weekly wages of state government employees is $818. Select the correct choice below and fill in the answer boxes to complete your choice. (Round to the nearest cent as needed.) O A. We can be 99% confident that when the average weekly wages of state government employees is $818, the average weekly wages of federal government employees will be between $ and…
The number of banks in a country has been dropping steadily since 1984, and the trend in recent years has been roughly linear. The annual data for the years 1999 through 2008 can be summarized as follows, where x represents the years since 1990 and y the number of banks, in thousands. n=10 Ex-235 ²-5605 Ey-77.564 Ey²=603.80424 Exy-1810.155 a. Find an equation for the least squares line. b. Use your result from part a to predict the number of banks in the year 2020. c. If this trend continues linearly, in what year will the number of banks drop below 6200? d. Find and interpret the correlation coefficient.
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
The table below shows the average weekly wages (in dollars) for state government employees and federal government employees for 10 years. Construct and interpret a 90% prediction interval for the average weekly wages of federal government employees when the average weekly wages of state government employees is $825. The equation of the regression line is y = 1.597x- 169.203. Wages (state), x Wages (federal), y 742 775 783 802 842 890 912 939 949 954 1,002 1,039 1,097 1,145 1,199 1,242 1,271 1,299 1,335 1,398 Construct and interpret a 90% prediction interval for the average weekly wages of federal government employees when the average weekly wages of state government employees is $825. Select the correct choice below and fill in the answer boxes to complete your choice. (Round to the nearest cent as needed.) O A. There is a 90% chance that the predicted average weekly wages of federal government employees is between S and S given a state average weekly wage of $825. O B. We can be 90%…
The managing director of a consulting group has the accompanying monthly data on total overhead costs and professional labor hours to bill to clients. Complete parts a through o. Click the icon to view the monthly data. a. Develop a simple linear regression model between billable hours and overhead costs. Overhead Costs OxBillable Hours (Round the constant to one decimal place as needed. Round the coefficient to four decimal places as needed. Do not include the $ symbol in your answers.) Monthly Overhead Costs and Billable Hours Data Overhead Costs $385,000 Billable Hours 3,000 $425,000 4,000 $445.000 5,000 $497,000 6,000 $570,000 7,000 $590,000 8,000