Answered: Both OM 0.287 0.147 0.716 Neither 0.144

Math

Statistics

Both OM 0.287 0.147 0.716 Neither 0.144

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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c) Show that the coefficient of determination, R², can also be obtained as the squared correlation between actual Y values and the Y values estimated from the regression model where Y is the dependent variable. Note that the coefficient of correlation between Y and X is Eyixi r = And also that ỹ = ŷ (18.75)
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.
Data on alcohol content and wine quality was collected from variants of a particular wine. From a sample of 50 wines, a model was created using the percentages of alcohol to predict wine quality. The data are modeled by Yi=−0.384+0.5332Xi, where Xi is the alcohol content (%) and Yi is the rated quality of the wine. For these data, SYX=0.9431, X=10.19, and hi=0.029612 when X=11. a. Construct a 90% confidence interval estimate of the mean wine quality rating for all wines that have 11% alcohol. _____≤μY|X=11≤_______ (Type integers or decimals. Round to three decimal places as needed. Use ascending order.)
A highway department is studying the relationship between traffic flow and speed. The following model has been hypothesized: y = B, + B,x + E where y = traffic flow in vehicles per hour x = vehicle speed in miles per hour. The following data were collected during rush hour for six highways leading out of the city. Traffic Flow (y) Vehicle Speed (x) 1,257 35 1,329 40 1,227 30 1,335 45 1,351 50 1,123 25 In working further with this problem, statisticians suggested the use of the following curvilinear estimated regression equation. ý = bo + b,x + b,x? (a) Develop an estimated regression equation for the data of the form ý = b, + b,x + b,x2. (Round b, to the nearest integer and b, to two decimal places and b, to three decimal places.)
D. Only OLS assumption #2 is satisfied. The estimated regression is Y = 39+0.45X; Compute the estimated regression's prediction for the average score of students given 99, 129, or 155 minutes to complete the exam. Given 99 minutes, the estimated regression's prediction for the average score of students is Given 129 minutes, the estimated regression's prediction for the average score of students is Given 155 minutes, the estimated regression's prediction for the average score of students is (Round your responses to two decimal places.) Compute the estimated gain in score for a student who is given an additional 19 minutes on the exam. The estimated gain in score for a student who is given an additional 19 minutes on the exam is (Round your response to two decimal places.) Click to select your answer(s). W (DELL] o search PROU II
Consider the simple linear regression model Wage = Bo + B1*Age + U. The error term U can capture the followings, with the exception of O A. possible measurement error in Wage. O B. the temperature in London Ontario tomorrow. O C. possible model misspecification, such as the nonlinear effect of Age on Wage. O D. other variables that affect the dependent variable, such as previous work experience.
The City of Bellmore’s police chief believes that maintenance costs on high-mileage police vehicles are much higher than those costs for low-mileage vehicles. If high-mileage vehicles are costing too much, it may be more economical to purchase more vehicles. An analyst in the department regresses yearly maintenance costs (Y) for a sample of 200 police vehicles on each vehicle’s total mileage for the year (X). The regression equation finds: Y = $50 + .030X with a r2 of .90 What is the IV? What is the DV? If the mileage increases by one mile, what is the predicted increase in maintenance costs? If a vehicle’s mileage for the year is 50,000, what is its predicted maintenance costs? What does an r2 of .90 tell us? Is this a strong or weak correlation? How can you tell?
The marketing research department of a large company knows that the company's monthly sales are influenced by the way in which it spends money on marketing. For example, monthly expenditures such as the ones listed below are known to have an effect on y, the company's total monthly sales (in millions of dollars). x = money spent on television advertising (in 1000's of dollars) X2 = money spent on promotion (i.e., free samples) x3 = money spent on newspaper advertising (in 1000's of dollars) X = average discounts offered to retail outlets (in %) Using data from the previous 26 months, the company decides to collect data on 3 of the independent variables to use in a multiple regression model for estimating monthly sales. If the R´ for this model is 0.5, fill in the missing entries in the ANOVA table associated with this model. Do all calculations to at least three decimal places. Source of variation Degrees of freedom Sums of Mean F statistic squares squares Regression Error Total 4+
An advertising firm wishes to demonstrate to potential clients the effectiveness of the advertising campaigns it has conducted. The firm is presenting data from 12 recent campaigns, with the data indicating an increase in sales for an increase in the amount of money spent on advertising. In particular, the least-squares regression equation relating the two variables cost of advertising campaign (denoted by x and written in millions of dollars) and resulting percentage increase in sales (denoted by y) for the 12 campaigns is y = 6.18 +0.14x, and the standard error of the slope of this least-squares regression line is approximately 0.10. Using this information, test for a significant linear relationship between these two variables by doing a hypothesis test regarding the population slope B₁. (Assume that the variable y follows a normal distribution for each value of x and that the other regression assumptions are satisfied.) Use the 0.10 level of significance, and perform a two-tailed…
Q 3: Al Matra LLC has recently hired Mr. Musab, a professional market researcher. Mr. Musab is interested to find out relationship between cost incurred and revenue earned by the company. He collected the data of last 7 years from the accounts department, but he is confused about the method which can find out the relationship. Revenue Cost Incurred Earned Year (OMR in (OMR in Millions) Millions) 2013 20 2014 10 22 2015 9 22 2016 18 2017 11 20 2018 12 24 2019 10 25 a) Find out the correlation value and give interpretation of the result b) If correlation value is 1.25, discuss the result analytically.
Assume there is a positive linear correlation between the variable R (Return rate in percent of a financial investment) and the variable t (age in years of the investment) given by the regression equation R= 2.3t + 4.8 A. Without further information, can we assume there is a cause-and-effect relationship between the return rate and the age of the investment? B. If the investment continues to grow at a constant rate, what is the expeted return rate when the investment is 7 years old? C. If the investment continues to grow at a constant rate, how old is the investment when the return rate is 30%?