MATLAB: An Introduction with Applications
6th Edition
ISBN: 9781119256830
Author: Amos Gilat
Publisher: John Wiley & Sons Inc
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- 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?arrow_forwardDoes a major league baseball team's record during spring training indicate how the team will play during the regular season? Over a six-year period, the correlation coefficient between a team's winning percentage in spring training and its winning percentage in the regular season is . Shown are the winning percentages for the American League teams during a season. Team SpringTraining RegularSeason Team SpringTraining RegularSeason Baltimore Orioles 0.409 0.424 Minnesota Twins 0.492 0.542 Boston Red Sox 0.426 0.588 New York Yankees 0.579 0.559 Chicago White Sox 0.411 0.535 Oakland A's 0.690 0.468 Cleveland Indians 0.578 0.492 Seattle Mariners 0.492 0.379 Detroit Tigers 0.578 0.459 Tampa Bay Rays 0.722 0.601 Kansas City Royals 0.540 0.461 Texas Rangers 0.640 0.491 Los Angeles Angels 0.724 0.619 Toronto Blue Jays 0.442 0.533 a. What is the correlation coefficient between the spring training and the regular season winning percentages…arrow_forwardAn economist wants to determine whether there is a linear relationship between a country's gross domestic product (GDP) and carbon dioxide emissions. 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 your conclusion. Hint: Your conclusion is either of the following. • There is a significant linear relationship between a country's gross domestic product (GDP) and carbon dioxide emissions. • There is no significant linear relationship between a country's gross domestic product (GDP) and carbon dioxide emissions. GDP 1.6 3.6 4.9 1.1 0.9 2.9 2.7 2.3 1.6 1.5 (trillion dollars) Carbon Dioxide Emissions 428.2 828.8 1214.2 444.6 264 415.3 571.8 454.9 358.7 573.5 (millions of metric tons)arrow_forward
- Thanks!arrow_forwardPlease explain each step clearly, and no excel formula should be used for solving this problemarrow_forwardListed below are amounts of court income and salaries paid to the town justices. All amounts are in thousands of dollars. Construct a scatterplot, find the value of the linear correlation coefficient r, and find the P-value using a= 0.05. Is there sufficient evidence to conclude that there is a linear correlation between court incomes and justice salaries? Based on the results, does it appear that justices might profit by levying larger fines? Court Income Justice Salary 65.0 406.0 1566.0 1131.0 274.0 253.0 110.0 152.0 31.0 e 29 45 94 58 46 61 25 26 19 The linear correlation coefficient is r= (Round to three decimal places as needed.) The test statistic is t=. (Round to three decimal places as needed.) The P-value is (Round to three decimal places as needed.) V than the significance level 0.05, there court incomes and justice salaries for a significance level of a = 0.05. Because the P-value is V sufficient evidence to support the claim that there is a linear correlation between Based…arrow_forward
- After gathering data about the number of starfish and measuring the pollution in areas of the ocean you find a negative linear correlation between pollution levels and number of starfish. What can you conclude based on this information? a. There is a confounding variable that is affecting both pollution and starfish. b. As pollution rises the number of starfish falls c. That pollution is causing starfish to die, leading to the negative correlation d. That pollution is supporting starfish, leading to the negative correlationarrow_forwardCompute and interpret the coefficient of multiple correlation. Number 4 answer is missing.arrow_forwardBased on the scatter plot below on data from Central Harlem, would you estimate the correlation coefficient to be positive, negative, or close to 0? Select the correct correlation and corresponding explanation. Household Income vs. Serious Crime Rate (per 1,000 residents) CentralHarlem Data Scatter Plot: 22- 21- 20- 19- 18- 17 16- 15 32 34 36 38 40 42 Household_Income (thousands) Positive; As the household income increases, the serious crime rate decreases. A. Negative; As the household income increases, the serious crime rate decreases. B. Positive; As the household income increases, the serious crime rate also increases. OC. Negative; As the household income increases, the serious crime rate also increases. D. Close to 0; There is no linear correlation between household income and serious crime rate. E. A Moving to another question will save this response. Question 24 of 25 MacBook Air Serious crime_ratearrow_forward
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