The following data pertain to the marks in subjects A and B in a certainexamination.to XMean marks in A = 39.5Mean marks in B = 47.5%3DStandard deviation of marks in A = 10.8 noitsiva@ bisbsi2Standard deviation of marks in B = 16.8 0 neUsionno bnsCoefficient of correlation between marks in A and marks in B = •42Find the two regression lines. Find the marks in B for candidates who secured50 marks in A.

Answered: Image /qna-images/answer/7c76bf53-94b8-4c7c-9777-a82b25837b04.jpg

Answered: The following data pertain to the marks…

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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For a data set of brain volumes (cm°) and I scores of four males, the linear correlation coefficient is r= 0.968. Use the table available below to find the critica values of r. Based on a comparison of the linear correlation coefficient r and the critical values, what do you conclude about a linear correlation? Click the icon to view the table of critical values of r. The critical values are (Type integers or decimals. Do not round. Use a comma to separate answers as needed.) Since the correlation coefficient r is there V sufficient evidence to support the claim of a linear correlati
For a data set of chest sizes (distance around chest in inches) and weights (pounds) of seven anesthetized bears that were measured, the linear correlation coefficient is r = 0.715. Use the table available below to find the critical values of r. Based on a comparison of the linear correlation coefficient r and the critical values, what do you conclude about a linear correlation? Click the icon to view the table of critical values of r. The critical values are (Type integers or decimals. Do not round. Use a comma to separate answers as needed.) Since the correlation coefficient r is in the right tail above the positive critical value, there is sufficient evidence to support the claim of a linear correlation. Table of Critical Values of r - X Number of Pairs of Data n Critical Value of r 4 0.950 5 0.878 6 0.811 7 0.754 8 0.707 9 0.666 10 0.632 11 0.602 12 0.576 Print Done ×
A sample of 100 bears was treated like it was the whole population of Jellystone Park bears, and the mean weight was found to be 800 pounds with standard deviation 12 pounds, the mean blood pressure was 150 with standard deviation 6, and the sample correlation coefficient of weight with blood pressure was found to be .4. Based on this information, what is the regression slope for the SLR equation for predicting blood pressure using observed weight for Jellystone bears?
Data was collected from 60 students from the SAT, the average sat score was 909 with a standard deviation of 178. The average ACT score was 19 with a standard deviation of 3.5. The correlation between the two variables is 0.811. a) To predict the SAT score from the ACT score, what is the equation of the leasts-square regression line. b) What fraction of the variation in the values of the SAT scores is accounted for by the linear relationship between SAT and ACT scores. Explain
The table below lists weights (carats) and prices (dollars) of randomly selected diamonds. Find the (a) explained variation, (b) unexplained variation, and (c) indicated prediction interval. There is sufficient evidence o support a claim of a linear correlation, so it is reasonable to use the regression equation when making predictions. For the prediction interval, use a 95% confidence level with a diamond that weighs 0.8 carats. Weight Price 0.3 $508 a. Find the explained variation. 0.4 $1153 0.5 $1332 Round to the nearest whole number as needed.) . Find the unexplained variation. Round to the nearest whole number as needed.) c. Find the indicated prediction interval.
Ost watched Ani... Question 2 Y Part 1 of 4 A doctor in Cleveland wants to know whether the average life span for heart disease patients at four hospitals in the city differ. The data below represents the life span, in years, of heart disease patients from each hospital. Perform an ANOVA test with a 9% level of significance to test whether the average life span of heart disease patients in Cleveland differs depending on the hospital that treats them Life Span of Patients Treated at Hospital 1: 7.4, 7.8, 7.7, 7.5, 8, 8.2, 7.8, 8.6, 8, 7.8, 8.3, 8.3, 8, 7.6, 8.2, 7.9, 7.3, 8, 8.6, 7.3, 8.3, 8, 7.8, 8, 7.8, 8.1, 8.1, 8, 7.6, 7.6, 7.7, 7.4, 7.7, 7.8, 7.8 Life Span of Patients Treated at Hospital 2: 7.9, 7.9, 8.2, 8, 8.1, 8.5, 8.3, 8.4, 8, 8.2, 7.7, 8, 8, 7.8, 7.9, 8.1, 8.1, 7.8, 7.9, 8, 8.5, 8.3, 8.2, 8.3, 7.8, 7.9 Life Span of Patients Treated at Hospital 3: 8.2, 8.1, 7.4, 8.7, 8.6, 8.2, 7.9, 8.1, 8.1, 8.3, 8.3, 8, 7.6, 8, 7.4, 8.6, 8.2, 8.2, 7.9, 7.7, 8.1, 7.9, 8, 8.3 Life Span of…
For a data set of chest sizes (distance around chest in inches) and weights (pounds) of twelve anesthetized bears that were measured, the linear correlation coefficient is r= 0.276. Use the table available below to find the critical values of r. Based on a comparison of the linear correlation coefficient r and the critical values, what do you conclude about a linear correlation? Click the icon to view the table of critical values of r. The critical values are Table of Critical Values of r IT Number of Pairs of Data n Critical Value of r 4 0.950 0.878 0.811 7 0.754 0.707 0.666 10 0.632 11 0.602 12 0.576
a) Compute the value of the sample correlation coefficient, r. (Round your answer to four decimal places.) r = b) 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.) t = P- value = c) If a regression analysis were to be carried out to predict endurance from lactate level, what proportion of observed variation in endurance could be attributed to the approximate linear relationship? Answer the question without doing any regression calculations. (Round your answer to four decimal places.) ____________ d) If a regression analysis were to be carried out to predict lactate level from endurance, what proportion of observed variation in endurance could be attributed to the approximate linear relationship? Answer the question without doing any regression calculations. (Round your answer to four decimal places.) _____________
Consider the following computer output from a multiple regression analysis relating the cost of car insurance to the variables: number of car accidents, driver's credit score, and safety rating of the car. Intercept Car Accidents (In last 3 years) Credit Score Safety Rating Coefficients 1186 213.48 Coefficients - 130.46 294.11 Standard Error Does the sign of the coefficient for the variable safety rating make sense? 123.87 21.89 14.26 356.37 t Stat P-value 9.575 0.0000 9.752 0.0000 -9.149 0.0000 0.825 0.4128
Let's study the relationship between brand, camera resolution, and internal storage capacity on the price of smartphones. Use α = .05 to perform a regression analysis of the Smartphones01CS dataset, and then answer the following questions. When you copy and paste output from MegaStat to answer a question, remember to choose to "Keep Formatting" to paste the text. a. Did you find any evidence of multicollinearity and variance inflation among the predictors. Explain your answer using a VIF analysis. b. Copy and paste the normal probability plot for your analysis. Is there any evidence that the errors are not normally distributed? Explain. c. Copy and paste the Residuals vs. Predicted Y-values. Does the pattern support the null hypothesis of constant variance for the errors? Explain. d. Study the residuals analysis. Which observations, if any, have unusual residuals? e. Study the residuals analysis. Calculate the leverage statistic. Which observations, if any, are high leverage…
Consider a certain data set on shoulder girth and height of a group of individuals. The mean shoulder girth is 107.20 cm with a standard deviation of 10.37 cm. The mean height is 171.14 cm with a standard deviation of 9.41 cm. The correlation between height and shoulder girth is 0.67. Calculate R2 of the regression line for predicting height from shoulder girth, and interpret it in the context of the application.

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