A regression was run to determine if there is a relationship between the happiness index (y) and lifeexpectancy in years of a given country (x).The results of the regression were:ŷ=a+bxa=-0.523b=0.177(a) Write the equation of the Least Squares Regression line of the formj= 0.177+| -0.523(b) Which is a possible value for the correlation coefficient, r?O 1.936O -0.632O-1.936O 0.632(C) If a country increases its life expectancy, the happiness index willO decreaseO increase(d) If the life expectancy is increased by 1 years in a certain country, how much will the happinessindex change? Round to two decimal places.(e) Use the regression line to predict the happiness index of a country with a life expectancy of 64years. Round to two decimal places.

In multiple linear regression, there will be more than one independent variable. In simple linear…

Answered: A regression was run to determine if…

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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A regression was run to determine if there is a relationship between the happiness index (y) and life expectancy in years of a given country (x). The results of the regression were: ý=a+bx a=0.137 b=0.082 (a) Write the equation of the Least Squares Regression line of the form (b) Which is a possible value for the correlation coefficient, r? O-1.338 O-0.84 O 1.338 O 0.84 (c) If a country increases its life expectancy, the happiness index will O increase O decrease
When is the coefficient of certainty obtained from the regression equation equal to the square of the coefficient of correlation between the variables?
When the heights (in inches) and shoe lengths (also in inches) were measured for a large random sample of individuals, it was found that r = 0.89, and a regression equation was constructed in order to further explore the relationship between shoe length and height, with height being the response variable. From this information, what can we conclude? The correlation coefficient should have no units. The regression equation relating shoe length to height must have a slope equal to 0.89. The regression equation relating shoe length to height must have a positive intercept. O Approximately 89% of the variability in height can be explained by the regression equation. Because the value of r is less than 1, we should characterize this relationship as being weak.
Two variables,X and Y, are related by a regression equation such that X is the independent variable and Y is the dependent variable.The following details are available: Coefficient of correlation between X and Y = 0.80 Standard deviation of X = 6 Standard deviation of Y = 12 The slope of the regression equation is?
Suppose that you are modelling cooling costs (y) as a function of the daily high temperature (x) for a building, using a simple linear regression model. You have data consisting of pairs of observations (x, y) over many years. Suppose that from this data you find a Pearson correlation between x and y of 0.5. If tomorrow the temperature is 3 standard deviations above the mean, how many standard deviations above the mean would you expect the cooling costs to be? If it is true that, tomorrow, the temperature is as given in the previous problem, then the expectation that you gave is the exact cooling cost that will occur. True O False
a) what is the regression equation? b) what is the predicted weight of the bear? c) is the result close to the actual weight of 528 pounds?
What does the square of the linear correlation coefficient measure (rxr)?
The least-squares regression equation is y = 743.3x+ 12,990 where y is the median income and x is the percentage of 25 years and older with at least a bachelor's degree in the region. The scatter diagram indicates a linear relation between the two variables with a correlation coefficient of 0.7921. Complete parts (a) through (d). 55000- 20000- 15 20 25 30 35 40 45 50 55 60 Bachelor's % (a) Predict the median income of a region in which 20% of adults 25 years and older have at least a bachelor's degree. $ (Round to the nearest dollar as needed.) Median Income
The least-squares regression equation is y=620.6x+16,624 where y is the median income and x is the percentage of 25 years and older with at least a bachelor's degree in the region. The scatter diagram indicates a linear relation between the two variables with a correlation coefficient of 0.7004. In a particular region, 28.3 percent of adults 25 years and older have at least a bachelor's degree. The median income in this region is $37,389. Is this income higher than what you would expect? Why?
The least-squares regression equation is y=761.7x+13,208 where y is the median income and x is the percentage of 25 years and older with at least a bachelor's degree in the region. The scatter diagram indicates a linear relation between the two variables with a correlation coefficient of 0.7483. Predict the median income of a region in which 20% of adults 25 years and older have at least a bachelor's degree.
Using the data, the mad scientist wishes to compute a regression equation to predict future Werewolf captures (he wants to do this regardless of whether the correlation above is significant—he is mad after all). Nights until the full moon (X) # of Werewolves caught (Y) 7 1 6 2 2 4 3 5 2 8 1 Calculate a regression equation to predict Werewolf captures (Ŷ) from number of nights until the full moon (X) 2 Use this regression equation to predict how many Werewolves will be caught 3 nights until the full moon. 3 Use this regression equation to predict how many Werewolves will be caught 6 nights until the full moon.
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.

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