1. The following table shows, a random survey of small group of students information on the ages and the number of books they had read in the past year. Age(years) Books read 16 5 15 3 18 8. 17 6. 16 4 15 4 14 5 17 15 a) Draw a scatter plot and determine the correlation coefficient using software (round to 3 places of decimals) and classify it. b) Determine the equation of the line of best fit by using software (round to 3 places of decimals). c) Identify the outlier (from the scatter plot) d) Determine the correlation coefficient and the equation of the line of best fit without the outlier, by using software. (round to 3 places of decimals). Classify the correlation coefficient

1. The following table shows, a random survey of small group of students information on the ages and the number of books they had read in the past year. Age(years) Books read 16 5 15 3 18 8. 17 6. 16 4 15 4 14 5 17 15 a) Draw a scatter plot and determine the correlation coefficient using software (round to 3 places of decimals) and classify it. b) Determine the equation of the line of best fit by using software (round to 3 places of decimals). c) Identify the outlier (from the scatter plot) d) Determine the correlation coefficient and the equation of the line of best fit without the outlier, by using software. (round to 3 places of decimals). Classify the correlation coefficient

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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Fifty-four wild bears were anesthetized, and then their weights and chest sizes were measured and listed in a data set. Results are shown in the accompanying display. Is there sufficient evidence to support the claim that there is a linear correlation between the weights of bears and their chest sizes? When measuring an anesthetized bear, is it easier to measure chest size than weight? If so, does it appear that a measured chest size can be used to predict the weight? Use a significance level of a = 0.05 Question: Determine the null and alternative hypotheses
1. A professor is interested in how attendance is related to grades her class. She goes through her records and determines each student’s final average and the number of days absent. The scores for her sample are given in the table below. a. Construct a scatter plot of the relationship between final averages and the number of days absent. b. Is there a significant relationship between final averages and the number of days absent? Calculate and interpret the correlation and report it in proper APA format. c. Compute r2 for the data. What percent of the variability in final averages is accounted for by the number days absent?
Do E) i ii & iii please
10. Describe the pattern of data that is observed when there is a strong negative correlation between X and Y.
A random sample of 7 countries gave the following figures for X = annual per capita income and Y = urbanization rate (percentage of people living in urban area): Country X (US dollars) Y (%) 1 3500 20 2 2700 24 3 2100 16 4 2400 19 5 3000 26 6 2000 19 7 1900 17 Calculate the correlation coefficient for the sample. Explain the results At the 5% significant level, test whether per capita income and urbanization rate are unrelated.
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The accompanying technology output was obtained by using the paired data consisting of foot lengths (cm) and heights (cm) of a sample of 40 people. Along with the paired sample data, the technologgy was also given a foot length of 10.8 cm to be used for predicting height. The technology found that there is a linear correlation between height and foot length. If someone has a foot length of 10.8 cm, what is the single value that is the best predicted height for that person? H Click the icon to view the technology output. The single value that is the best predicted height is cm. Technology Output (Round to the nearest whole number as needed.) The regression equation is Height - 67.5 +5.15 Foot Length Predictor Coef SE Coef Constant 67.52 11.07 6.10 0.000 Foot Length 5.1508 0.4817 10.69 0.000 S-5.50271 R-Sq = 73.18 R-Sq (adj) = 72.44 Predicted Values for New Observations New Obs Fit SE Fit 95 CI 954 PI 123.149 1.605 (119.732, 126.566) (111.996, 134.302) Values of Predictors for New…
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