An economist wants to determine whether there is a linear relationshipbetween a country's gross domestic product (GDP) and carbon dioxideemissions. The data are shown in the table below.c. Compute and interpret the correlation coefficient.d. Compute and interpret the coefficient of determination.e. Test for the significance of the linear relationship. Use a 0.05 level of significance. State yourconclusion.Hint: Your conclusion is either of the following.• There is a significant linear relationship between a country's gross domestic product (GDP) andcarbon dioxide emissions.• There is no significant linear relationship between a country's gross domestic product (GDP)and carbon dioxide emissions.GDP1.6 3.64.91.10.9 2.9 2.72.31.61.5(trillion dollars)Carbon DioxideEmissions428.2 828.8 1214.2 444.6 264 415.3 571.8 454.9 358.7 573.5(millions ofmetric tons)

The question is about correlation Given :

Answered: An economist wants to determine whether…

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. Give an example for each of the following: a. Two variables in which you have enough reasons to explain their cause-and-effect relationship. State the independent (x) and dependent variable (y). Provide also description of the cause-and-effect relationship. b. Two variables from which a considerable possible nature of linear correlation may exist. Explain briefly the possible nature of the relationship. State both variables and give a short description of their relationship.
Police sometimes measure shoe prints at crime scenes so that they can learn something about criminals. Listed below are shoe print lengths and heights of males. Construct a scatterplot, find the value of the linear correlation coefficient r, and find the P-value of r. Determine whether there is sufficient evidence to support a claim of linear correlation between the two variables. Use a significance level of a = 0.01. Click to view the data on shoe print lengths and heights. Construct a scatterplot. Choose the correct graf Shoe Print Lengths versus Heights of Males O B. OD. 210- 200- 190- 180- 170- 160- 20 24 28 32 36 40 Shoe Print Length (cm) 210 200 190 180 170 160 2101 200- Shoe Print 190- Length (cm) Height (cm) 176.0 180- 170- 160- 20 24 28 32 36 40 31.4 28.9 181.2 31.5 194.5 Shoe Print Length (cm) 32.3 172.9 30.4 174.0 The linear correlation coefficient is r= 32.9 190.5 (Round to three decimal places as needed.) 31.0 176.6 30.8 170.0 30.5 171.9 32.3 175.5 30.5 200.1 32.6 199.3…
A group of Economics students wish to know whether a student's spending is related to his/her parents' monthly income. Which measure must be used? Group of answer choices A. regression analysis B. correlation analysis C. descriptive statistics D. partial correlation analysis
. Determine the regression equation using values you create for x and y for at least 10 pairs of data. Show the regression equation, correlation coefficient, and coefficient of determination. Then switch the x and y values for each data point. Based on that, again show the regression equation, correlation coefficient, and coefficient of determination. Discuss the similarities and differences between the results.
Statistics Question
Jim and Larry run a soup stand. The data below represents the sales of soup that they had on 8 randomly selected days, along with the high temperature on that day. a) Create a scatter diagram for this data set and comment on whether there is a positive or negative relationship between these variables.b) Calculate the correlation coefficient between temperature and sales.c) Does a linear relationship exist between these variables? Explain to me why or why not this is the case.d) Using excel, calculate the least squares regression equation for this data set. e) Using your equation, predict the soup sales on a day where the temperature is 29 degrees.f) Interpret the slope of this equation.g) Can we use this data set and regression equation to predict the sales on a day when the average temperature is 102 degrees? Why or why not?h) What is the R-squared for this equation? How do you interpret that R-squared?
a. Calculate the sample covariance.X Y-3 -913 95 12b. Calculate the sample correlation.c. Describe in words the information given to us by the sample covariance and by the sample correlation.Explain how the correlation gives us more precise information.d. Explain why the covariance is not a good measure of the strength of the linear relationship between the twovariables.e. Sketch a scatterplot of a bivariate dataset with a correlation of 0.9f. Sketch a scatterplot of a bivariate dataset with a correlation of -0.3.
Statistical technique used to determine the degree to which two variables are related is known as a. Regression b. Correlation c. None of these d. Dispersion
UnattemptedQuestion 1Expand PreviousNext The data below was collected from manufacturer advertisements of their vehicles' horsepower and highway gas mileage (mpg.). Use this data to answer the following questions. x 158 250 340 350 390 190 220 y 33 28 15 17 11 35 42 1. Find the p-value to determine if there is a linear correlation between horsepower and highway gas mileage (mpg). If there is a linear correlation, write the correlation coefficient below if there is a linear correlation, write the regression equation below. Using the data shown above, predict the the highway gas mileage (mpg) for a car that has a horsepower of 225 If there is a linear correlation, what percentage of variation in highway gas mileage (mpg) can be explained by horsepower?
Assume that you have paired values consisting of heights (in inches) and weights (in lb) from 40 randomly selected men. The linear correlation coefficient r is 0.594. Find the value of the coefficient of determination. What practical information does the coefficient of determination provide? Choose the correct answer below. O A. The coefficient of determination is 0.353. 64.7% of the variation is explained by the linear correlation, and 35.3% is explained by other factors. O B. The coefficient of determination is 0.647. 35.3% of the variation is explained by the linear correlation, and 64.7% is explained by other factors. O C. The coefficient of determination is 0.647. 64.7% of the variation is explained by the linear correlation, and 35.3% is explained by other factors. O D. The coefficient of determination is 0.353. 35.3% of the variation is explained by the linear correlation, and 64.7% is explained by other factors.
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