The following scatterplot and table represent the mean monthly temperature andrainfall of a coastal city over a period of 10 months.325300275250225200rainfall (mm) 175150100755025Temp 18(°C)Rainfall 45(mm)15X10" 11" 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30Temperature "C2019Mean monthly temperature and rainfall502522 24X115 65X28 26 27 29100 300 190 175 2002.1 Determine the equation of the least squares regression line.2.2 Identify the outlier.2.3Sketch the equation of the regression line on the DIAGRAM provided atthe end of your paper.2.4 Determine the value of the correlation coefficient and comment on therelationship between the mean temperature and the mean rainfall.2.5 If the mean temperature recorded for a certain month is 14°C, calculatethe mean rainfall for that month.How reliable is your answer? Explain and motivate your answer.

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MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

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1.)fill in the blanks. Based on the physician's study, the predictor variable, X is_____and the response variable, Y, is________. 2.) As described in the article, the relation between age and peak heart is a______. - positive relation - negative relation - no relation 3.) Provide the regression line, ŷ=a+bx. Show steps/equations used to get answer. 4.) Suppose a 40 year old person is randomely selected. Use your Model to predict their peak heart rate. 5.) Based on your model, as a person ages one year, how much would you expect peak heart rate to change?
The image contains two graphs typical of the analysis of a simple linear regression model. It can be stated that: a. The variance of the errors seems constant b. The errors do not come from a normal distribution c. The linear model does not seem appropriate d. The residuals are close to the line and therefore there is normality in the data. Why? Thanks
4
Report the equation of the regression line and interpret it in the context of the problem
Computer output for fitting a simple linear model is given below. State the value of the sample slope for this model and give the null and alternative hypotheses for testing if the slope in the population is different from zero. Identify the p-value and use it (and a 5% significance level) to make a clear conclusion about the effectiveness of the model.The regression equation is Y⁢=81.1-0.0116X. Predictor Coef SE Coef T P Constant 81.14 11.64 6.97 0.000 X -0.01158 0.00965 -1.20 0.245 Sample slope: Enter your answer; sample slopep-value: Enter your answer; p-valueDoes X appear to be an effective predictor of the response variable Y?Choose the answer from the menu; Is the model effective? YesNo
The fish in my pond have mean length 10 inches and mean weight 4 pounds. The correlation coefficient of length and weight is .9. If the length of a particular randomly selected fish is reported to be 10 inches, then what should we predict for the weight of that fish using simple linear regression? 4 10 2.5 NOT ENOUGH INFORMATION GIVEN NONE OF THE OTHERS
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Five students visiting the student health center for a free dental examination during National Dental Hygiene Month were asked how many months had passed since their last visit to a dentist. Their responses were as follows. 6 16 10 20 27 Assuming that these five students can be considered a random sample of all students participating in the free checkup program, construct a 95% confidence interval for the mean number of months elapsed since the last visit to a dentist for the population of students participating in the program. (Give the answer to two decimal places.)
11. For temperature (x) and number of ice cream cones sold per hour (y). (65, 8), (70, 10), (75, 11), (80,13), (85, 12), (90, 16). Interpret the coefficient of determination. Optional Answers: 1. 88.2% of the variability in the number of cones sold is explained by the least-squares regression model. 2. 93.9% of the variability in the number of cones sold is explained by the least-squares regression model. 3. 88.2% of the variability in the temperature is explained by the least-squares regression model. 4. 93.9% of the variability in the temperature is explained by the least-squares regression model.
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Construct and interpret a 95% prediction interval for the proceeds when the number of issues is 658.
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2.1 Determine the equation of the least squares regression line. 2.2 Identify the outlier. 2.3 Sketch the equation of the regression line on the DIAGRAM provided at