Researchers are interested in predicting the height of a child based on the heights of their motherand father. Data were collected, which included height of the child (height ), height of the mother (mothersheight ), and height of the father ( fathersheight ). The initial analysis used the heights of theparents to predict the height of the child (all units are inches). The results of the analysis, a multipleregression, are presented below..regress height mothersheight fathersheightSourceModelResidualTotalheightmothersheightfathersheight_consSSdf208.008457314.295372 37522.3038292 104.0042288.49446952MS39 13.3924059Coef. Std. Err..6579529 .1474763.2003584 .13822379.804327 12.39987Calculate the test statistic that is labeled "C" in the output.tP>|t|4.46 0.000C 0.1560.79 0.434Number of obs =F( 2,Prob > F37) =R-squaredAdj R-squared =Root MSE4012.240.0001= 0.39830.36572.9145.3591375-.0797093-15.32021=[95% Conf. Intervall.9567683.480426134.92886

The given multiple regression output is,

Answered: Researchers are interested in…

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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Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (The pair of variables have a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The table below shows the heights (in feet) and the numbe of stories of six notable buildings in a city. Height, x Stories, y 778 621 519 510 494 473 (a) x = 500 feet (c) x = 318 feet (b) x = 649 feet (d) x = 727 feet 51 47 45 41 38 36 Find the regression equation.
Find the regression equation, letting overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 2 cm. Can the prediction be correct? What is wrong with predicting the weight in this case? Use a significance level of 0.05. Overhead Width (cm) 7.9 8.4 9.7 7.4 9.6 7.1 Weight (kg) 162 214 263 140 254 155 Click the icon to view the critical values of the Pearson correlation coefficient r. The regression equation is y = + x. (Round to one decimal place as needed.) The best predicted weight for an overhead width of 2 cm is kg. (Round to one decimal place as needed.) Can the prediction be correct? What is wrong with predicting the weight in this case? O A. The prediction cannot be correct because a negative weight does not make sense. The width in this case is beyond the scope of the available sample data. O B. The prediction cannot be correct because there is not sufficient evidence of a linear correlation. The…
-by-Side We expect a car's highway gas milcage to be related to its city gas mileage (in miles per gallon, mpg). Data for all 1259 vehicles in the government's 2019 Fuel Ecomomy Guide give the regression line (b) What is the intercept? Give your answer to three decimal places. highway mpg - 8.720 + (0.914 x city mpg) intercept: mpg for predicting highway milcage from city mileage. Why is the value of the intercept not statistically meaningful? O The value of the intercept represents the predicted highway milcage for city gas mileage of 0 mpg. and such a prodiction would be invalid since O is outside the range of the data. O The value of the intercept represents the predicted city mileage for highway gas mileage of 0 mpg. and such a car does not exist. The value of the intercept is an average value calculated from a sample. The value of the intercept represents the predicted highway milcage for slope 0.
Box office totals: The data is the 50 highest grossing films(as of 2021.) Box office totals are measured in billions of dollars. -create a scatter plot with the explanatory variable be the Year and response the Revenue (in billions). Make sure to make a copy of the scatterplot in the word document. -find the equation of the least square's regression line. -interpret the correlation coefficient, and coefficient of determination (One sentence each) -Is year a good predictor of the box office revenue? Give a 1 to 2 sentence reason why you think so or not?
2
A regression modrel examining the amount of distance a long-ddistance runner runs (in miles) to predict the amount of fluid the runner drinks (ounces0 has a slope of 4.6. Which interpretation is appropiate? The correlation is needed to interpret this value. Arunner drinks minimum of 4.6 oz. We predict 4.6 miles for every ounce that drunk. We predict for every mile run, the runner drinks 4.6 more ounces. Each mile adds 4.6 more ounces.
Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (The pair of variables have a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The table below shows the heights (in feet) and the number of stories of six notable buildings in a city. Height, x Stories, y Un ne conteui yrapii veIUW. O A. Stories A 60- Height (feet) OO ( 800 (a) Predict the value of y for x = 503. Choose the correct answer below. A. 40 OB. 47 O C. 50 O D. not meaningful (b) Predict the value of y for x = 652. Choose the correct answer below. 774 51 OA. 54 O B. 47 OC. 40 O D. not meaningful (c) Predict the value of y for x = 810. Choose the correct answer below. OA. 54 625 47 OA. 54 OB. 50 OC. 47 O D. not meaningful (d) Predict the value of y for x = 725. Choose the correct answer below. B. 40 ΕΛ 521 45 O B. Stories 60+ 508 41 0 Height (feet) 800 497 38 477…
The data show the number of viewers for television stars with certain salaries. Find the regression equation, letting salary be the independent (x) variable. Find the best predicted number of viewers for a television star with a salary of $2 million. Is the result close to the actual number of viewers, 5.1 million? Use a significance level of 0.05. Salary (millions of $) Viewers (millions) 101 8 8 3 12 12 8 5.5 7.4 6.8 4.7 5.6 6.6 2.3 9.1 Click the icon to view the critical values of the Pearson correlation coefficient r. What is the regression equation? y=+x (Round to three decimal places as needed.) What is the best predicted number of viewers for a television star with a salary of $2 million? The best predicted number of viewers for a television star with a salary of $2 million is million. (Round to one decimal place as needed.) Is the result close to the actual number of viewers, 5.1 million? O A. The result is exactly the same as the actual number of viewers of 5.1 million. O B.…
10) Using advertised prices for used Ford Escorts a linear model for the relationship between a car's age and its price is found. The regression has an R2-87.4%. Write a sentence summarizing what R2 says about this regression. A) The age of the car explains 9.35% of the variation in price. B) The price of the car explains 12.6% of the variation in age. C) The age of the car explains 87.4% of the variation in price. D) The price of the car explains 87.4% of the variation in age. E) The age of the car explains 12.6% of the variation in price. Solve. Round to two decimal places if necessary. 11) At one college, 68% of students have credit cards. A credit card company, looking for new customers, sets up a booth at the college. If they pick students at random, how many students should they expect to talk to before finding one who does not have a credit card? A) 32 B) 3.13 D) 68 C) 0.32 E) 1.47
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The personnel director of a large hospital is interested in determining the relationship (if any) between an employee’s age and the number of sick days the employee takes per year. The director randomly selects ten employees and records their age and the number of sick days which they took in the previous year. Employee 1 2 3 4 5 6 7 8 9 10 Age 30 50 40 55 30 28 60 25 30 45 Sick Days 7 4 3 2 9 10 0 8 5 2 Copy Data The estimated regression line and the standard error are given. Sick Days=14.310162−0.2369(Age) se=1.682207se=1.682207 Find the 95% confidence interval for the average number of sick days an employee will take per year, given the employee is 26. Round your answer to two decimal places.
Find the equation of the regression line for given data

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