Interest rates for home mortgages have, in general, declined during recent months. With the apparent favorable influence fornew-home building, there seems to be a clear relationship between x = the prevailing mortgage interest rate and y = the numberof new houses being built per month in a Midwestern city over a period of 18 months. A scatterplot of the data collected showsthat the linear model is appropriate. The equation of the least-squares regression line is Number of new houses = 672.89-30.65 x Interest rate and ² = 0.49.What is the correlation coefficient between Interest rate and Number of new houses being built? (Give your answer to onedecimal place.)

Answered: Interest rates for home mortgages have,…

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 owners of a chain of ice cream stores have the business objective of improving the forecast of daily sales so that staffing shortages can be minimized during the summer season. As a starting point, the owners decide to develop a simple linear regression model to predict daily sales based on atmospheric temperature. They select a sample of 15 consecutive days. The results are shown in the accompanying table. Complete parts (a) through (f) below. Click the icon to view the data table. E Click the icon to view a table of critical values of the Durbin-Watson statistic. a. Assuming a linear relationship, use the least-squares method to compute the regression coefficients b, and b,. b, = - 2.7630 (Round to four decimal places as needed.) %3D Ice cream sales b, = (Round to four decimal places as needed.) %3D Day Sales ($1,000) Temperature (°F) 63 1 2 1.52 1.68 71 75 3 1.92 1.79 74 76 4 2.02 2.36 6. 81 2.16 2.69 81 8 84 2.91 88 10 3.17 90 11 12 3.02 3.27 90 93 99 13 3.42 2.84 88 83 14 15…
We wants to assess the relationship between overall GPA (4.0 scale) and amount of time spent weekly on school work of 50 college students. Simple linear regression model is used and some results for the model is given: a) Is it plausible that the true intercept for the model is 2.0? Why or why not? (hint: using interval for intercept) b) If 2.0 is the real intercept for the model, does it make contextual sense? Explain why it is or why it is not. c) Using the model, calculate the number of hours needed to achieve a GPA of 4.0. Then explain why this estimate number of hours is biased. (hint: think about Y).
A study of king penguins looked for a relationship between how deep the penguins dive to seek food and how long they stay underwater. For all but the shallowest dives, there is a linear relationship that is different for different penguins. The study report gives a scatterplot for one penguin titled " The relation of dive duration (DD) to depth (D)." Duration DD is measured in minutes and depth D is in meters. The report then says, " The regression equation for this bird is: DD = 2.3 + 0.0021 D. %3D (a) What is the slope of the regression line?. ANSWER minutes per meter. (b) According to the regression line, how long does a typical dive to a depth of 400 meters last? ANSWER minutes.
A study of king penguins looked for a relationship between how deep the penguins dive to seek food and how long they stay underwater. For all but the shallowest dives, there is a linear relationship that is different for different penguins. The study report gives a scatterplot for one penguin titled, “The relation of dive duration (DD) to depth (D).” Duration DD is measured in minutes, and depth D is in meters. The report then says, “The regression equation for this bird is DD=2.69+0.0138D. (a) What is the slope of the regression line? (Enter your answer rounded to four decimal places.) slope = (b) On average, if the depth of the dive increases by one meter, what is the increase in the diving duration? (Enter your answer rounded to four decimal places.) increase in duration = minutes (c) According to the regression line, how long does a typical dive to a depth of 168 meters last? (Enter your answer rounded to three decimal places.) length…
The table shows a part of an output of a linear regression model predicting the average fare on different flight routes. Data Table Regression Table Coefficient Constant 95.80976147 COUPON −9.61654124 DISTANCE 0.080733811 PAX −0.000167343 What is the difference in prediction of the following two routes? Route A that is 3,000 miles, with COUPON=1.5 and PAX=6,000 Route B that is 3,000 miles, with COUPON=1.2 and PAX=6,000.
Consumers are often interested in the fuel efficiency of the vehicles they choose to buy, so much so that they will research the various models they consider buying. Fuel efficiency can depend on a variety of variables. In this analysis, there are 73 automobiles that are popular with consumers. A regression analysis has been performed; the dependent variable is CityMPG (EPA miles per gallon in city driving), and independent variables are Length (vehicle length in inches), Width (vehicle width in inches), Weight (vehicle weight in pounds), and ManTran (1 if manual shift transmission, 0 otherwise). The level of significance is 0.05. Use the following MegaStat output to answer questions about this regression analysis. a. State the regression equation. b. How would CityMPG be affected if the width of a vehicle increased by an inch? c. Estimate the CityMPG for a vehicle with a length of 190 inches, a width of 75 inches, a weight of 4100 pounds, and a manual. Round your answer to the nearest…
A study of king penguins looked for a relationship between how deep the penguins dive to seek food and how long they stay underwater. For all but the shallowest dives, there is a linear relationship that is different for different penguins. The study report gives a scatterplot for one penguin titled " The relation of dive duration (DD) to depth (D)." Duration DD is measured in minutes and depth D is in meters. The report then says, " The regression equation for this bird is: DD = 2.02 + 0.0081 D. (a) What is the slope of the regression line?. ANSWER minutes per meter. (b) According to the regression line, how long does a typical dive to a depth of 400 meters last? ANSWER minutes. Speak
A major brokerage company has an office in Miami, Florida. The manager of the office is evaluated based on the number of new clients generated each quarter. Data were collected that show the number of new customers added during each quarter between 2015 and 2018. A multiple regression model was developed with the number of new customers as the dependent and the following four independent variables: Period (1, …, 16): A variable that measures the trend; Q1 = 1 for first quarter, Q1 = 0 otherwise; Q2 = 1 for second quarter, Q2 = 0 otherwise; Q3 = 1 for third quarter, Q3 = 0 otherwise. Questions: 1. Explain each of the four slopes (Period, Q1, Q2, Q3). 2. How many new customers would you expect in the second quarter of the following year (2019)?
why the correct answer is letter b?
Laetisaric acid is a compound that holds promise for control of fungus diseases in crop plants. Below is the least-squares regression equation to predict fungus growth (mm) from laetisaric acid concentration (µG/ml): ŷ =31.8 -0.712x Which of the following statements is correct? A. Above-average values of laetisaric acid concentration tend to accompany above-average values of fungus growth. B. From the given regression equation, we know the correlation is negative and we can say what the exact value of that correlation is. C. When fungus growth increases by 1 mm, the laetisaric acid concentration decreases by 0.712 µG/ml. D. None of the above.
During the 1950's and 1960's the average weight of vehicles sold in the U.S. was well over 4,000 pounds. There was a dip in the average weights in the 1970's and 1980's, due possibly to both higher demand for better gas mileage and a world-wide shortage of crude oil. Then in the 1990's and early 2000's the average weight of vehicles had a steady increase. A regression analysis was completed on the average weight of the 10 most commonly sold vehicles in the U.S. from the years 2012 through the year 2020 and yielded the following results, where the independent variable is the year and the predicted variable is the average weight of the 10 most popular vehicles. Correlation of "Average Weight" and "Year" = r = 0.9283 The regression equation is "Average Weight" = –124,960.73 + 63.82(Year) Predict the Average Weight to the nearest pound for the 2022 Year. Group of answer choices A. 4057 pounds B. 4015 pounds C. This value of Year is beyond the scope of the…
According to an article, one may be able to predict an individual's level of support for ecology based on demographic and ideological characteristics. The multiple regression model proposed by the authors was the following. y = 3.60-.01x₁+.01.₂-.07x3+.12x4+.02xs-.04x6-01-.04.xg-.02.xg+c The variables are defined as follows. y = ecology score (higher values indicate a greater concern for ecology) X₁ = age times 10 x₂ = income (in thousands of dollars) x3 = gender (1 = male, 0 = female) X4 = race (1 = white, 0 = nonwhite) X5 = education (in years) x6 = ideology (4 = conservative, 3 = right of center, 2 = middle of the road, 1 = left of center, and 0 = liberal) X7 = social class (4 = upper, 3 = upper middle, 2 = middle, 1 = lower middle, 0 = lower) xg = postmaterialist (1 if postmaterialist, 0 otherwise) x9 = materialist (1 if materialist, 0 otherwise) (a) Suppose you knew a person with the following characteristics: a 30 year old, white female with a college degree (20 years of…

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