Classify the relationships in the following situations. a) The rate of a chemical reaction increases with temperature. b) Leadership ability has a positive correlation with academic achievement. c) The prices of butter and motorcycles have a strong positive cor-relation over many years. d) Sales of cellular telephones had a strong negative correlation with ozone levels in the atmosphere over the last decade. e) Traffic congestion has a strong correlation with the number of urbanexpressways

Classify the relationships in the following situations. a) The rate of a chemical reaction increases with temperature. b) Leadership ability has a positive correlation with academic achievement. c) The prices of butter and motorcycles have a strong positive cor-relation over many years. d) Sales of cellular telephones had a strong negative correlation with ozone levels in the atmosphere over the last decade. e) Traffic congestion has a strong correlation with the number of urbanexpressways

A First Course in Probability (10th Edition)

10th Edition

ISBN:9780134753119

Author:Sheldon Ross

Publisher:Sheldon Ross

Chapter1: Combinatorial Analysis

Section: Chapter Questions

Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...

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Activity No. 2: Read and Answer the following questions. 1. A psychologist found in her study that there is no correlation between a person's happiness arnd the amount of money he or she has. Explain this finding. 2. JC Watts said, "There is a positive correlation between education and stable families." What does this mean? 3. A researcher found out that the correlation coeffic ernt between the width of the road and the number of accidents recorded is -0.80. What does this result imply?
(a) A researcher reports that there is no consistent relationship between grade point average and the number of hours spent studying for college students. The correlation between grade point average and the number of hours studying is an example of a) a positive correlation.b) a negative correlation. c) a correlation near zero.d) a correlation near one. (b) A positive value for a correlation indicates _____. a) increases in X tend to be accompanied by increases in Yb) increases in X tend to be accompanied by decreases in Y c) a much stronger relationship than if the correlation were negatived) a much weaker relationship than if the correlation were negative
Past data on household income and expenditure reveal that: (a) The average absolute increase in income in relation to increase in expenditure is $ 1.5 crore and (b) the average absolute increase in expenditure in relation to increase in income is $ 0.50 crore. Find the co-efficient of correlation between household income and expenditure.
The authors of a paper presented a correlation analysis to investigate the relationship between maximal lactate level x and muscular endurance y. The accompanying data was read from a plot in the paper. X 390 740 760 y 3.90 r 3.90 5.00 5.30 4.10 3.60 6.20 OH₂: P = 0 Ha: P 0 1,465 1,470 1,495 2,190 t = P-value = 6.98 7.45 4.85 7.70 4.55 6.70 8.80 OH₂: P = 0 H₂: P = 0 Compute the value of the sample correlation coefficient, r. (Round your answer to four decimal places.) Calculate the test statistic and determine the P-value. (Round your test statistic to one decimal place and your P-value to three decimal places.) State the conclusion in the problem context. O Fail to reject Ho. A positive correlation exists between maximum lactate level and muscular endurance. Reject Ho. A positive correlation exists between maximum lactate level and muscular endurance. O Fail to reject Ho. A positive correlation does not exist between maximum lactate level and muscular endurance. O Reject Ho. A…
Table #10.1.6 contains the value of the house and the amount of rental income in a year that the house brings in ("Capital and rental," 2013). Find the correlation coefficient of determination and then interpret both Table #10.1.6: Data of House Value versus Rental Value Rental Value Rental Value Rental Rental Value 67500 81000 6656 77000 4576 75000 7280 6864 95000 7904 94000 8736 90000 6240 85000 7072 121000 12064 115000 7904 110000 7072 104000 7904 135000 8320 130000 9776 126000 6240 125000 7904 145000 8320 140000 9152 7488 9568 140000 135000 7488 165000 13312 165000 8528 155000 148000 8320 178000 11856 174000 10400 170000 9568 170000 12688 匯 190000 200000 225000 8320 12272 8528 200000 200000 10608 194000 11232 8320 10400 11648 10192 12272 10400 200000 240000 214000 208000 240000 10192 240000 12064 12480 244500 11232 12896 12480 289000 11648 270000 262000 325000 12480 310000 303000 300000 12480
1945 is paired with cancer rates for 1975. The cigarette consumption data is from 1945 to 1975 in 5-year increments. There is a strong positive linear association between annual cigarette consumption and lung cancer rates. The correlation is 0.81. If we use the line to make predictions of the number of lung cancer deaths per 100,000 people, which of the following is an example of extrapolation? a. Predict Y when cigarette consumption is 3,500 cigarettes per person. b. Predict Y when cigarette consumption is 3,800 cigarettes per person. c. Predict Y when cigarette consumption is 4,600 cigarettes per person.
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GRC often rely heavily on raising money for an "annual fund" to support operations. Alumni are typically solicited for donations to the annual fund. Studies suggest that the graduate's annual income is a good predictor of the amount of money he or she would be willing to donate, and there is a reasonably strong, positive, linear relationship between these variables. In the studies described: O a. annual income is the predictor variable O b. the correlation between annual income and the size of the donation is negative. O c. the size of the donation to the annual fund is the predictor variable O d. All of the above O e. None of the above
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The following are the monthly figures of advertising expenditure and sales of a firm. It is generally found that advertising expenditure has its impact on sales after 2 months. Allowing for this time lag, calculate coefficient of correlation. Advertising Expenditure ('000) Sales Months Advertising Expenditure ('000) Months Sales 1,200 July Aug. Jan. 50 140 2,400 Feb. 60 1,500 160 2,600 March 70 1,600 Sep. 170 2,800 90 2,000 Oct. 190 2,900 April May 120 2,200 Nov. 200 3,100 June 150 2,500 Dec. 250 3,900
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