I.PROBLEM - SOLVING: Analyze the given set of quantitative problem related toresearch. Solve for the necessary significant value of the Pearson - r. Provide thetable of values. Show the complete solution. Write your answers and solutions on thespaces provided below.1. Find the r - value for the correlation between the scores of 5 selected Grade 12students on the 25 - item Mathematical Assessment and 25 - item ScienceAssessment. Determine the type of correlation.23212122202320181917Formula:r=n(Exy) – (Ex) (Ev)V[n(Ex?)-(Ex)²] [n( Ev²) – (Ev)*]Solution:1. Pearson -r

given X Y 23 21 21 22 20 23 20 18 19 17 to find - correlation coefficient r by…

1. Find the r - value for the correlation between the scores of 5 selected Grade 12 students on the 25 - item Mathematical Assessment and 25 - item Science Assessment. Determine the type of correlation. 23 21 21 22 20 23 20 18 19 17 la: r= n(Exy) - (Ex) (Ey) V[n(Ex)-(Ex)°] [n( Ev*) – (Ev)³]

1. Find the r - value for the correlation between the scores of 5 selected Grade 12 students on the 25 - item Mathematical Assessment and 25 - item Science Assessment. Determine the type of correlation. 23 21 21 22 20 23 20 18 19 17 la: r= n(Exy) - (Ex) (Ey) V[n(Ex)-(Ex)°] [n( Ev*) – (Ev)³]

Advanced Engineering Mathematics

10th Edition

ISBN:9780470458365

Author:Erwin Kreyszig

Publisher:Erwin Kreyszig

Chapter2: Second-order Linear Odes

Section: Chapter Questions

Problem 1RQ

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QUESTION A10 (5 A research was conducted to observe the effect of sunlight exposure on vitamin D concentration among women with deficiency. The result obtained in the research was r = 0.843. (a) Interpret the value of product-moment correlation, r. (b) # Compute the coefficient of determination, r², and interpret the result.
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Q2) The following data represents the results of an experimental test, if the relation between variables take the form y=ae^(bvx) y = aeb√x y 4 13 7 10 12 9 15 7 24 4 the value of y (1 :when x=16 is 6.3278 6.2387 6.3728 6.2783 none of all above * the correlation coefficient (2 of the above relation 0.9927 0.9727 0.9772 0.9977 none of all above
For the data shown to the right, determine the linear correlation coefficient using the formulas shown below. Compare your answers in part (a) and (b). x | 3 |6|1|0 |0 y 6 700- 4 a. Use the definition r= Exy - (Ex) (E») n E - (Ex)°/n] [Ev - (E»)*/n] b. Use the computing formula r= a. Using the definition, r= (Round to three decimal places as needed.) b. Using the computing formula, r= (Round to three decimal places as needed.) The value in part (a) is V the value in part (b).
1:10 O b A O "Q. * a E (19 For numbers 1 to 3: The thermal condition in a cave is critical for sustaining a bat population. A sudden change in the temperature is related to deaths of bats in hundreds. An attempt to measure the degree of association between the temperature (in °C) and death toll (number of deaths) was done using 20 caves with reported cases of massive death among bats. Assume that temperature and death toll are bivariate normal. Given below is the R software output: > cor.test(temp , death) Pearson's product-moment correlation data: temp and death t - -1.4403, df - 148, p-value - 0.1519 alternative hypothesis: true correlation is not equal to e 95 percent confidence interval: -0.27269325 0.04351158 sample estimates: cor -0.1175698
The correlation between X and Y is r = 0.35. Based on thisr value, is this a good equation to use for predictions of other events within the range of the data? O Yes, the correlation is strong. cannot be determined O No, the correlation is too weak to give accurate estimates. O No, the correlation is too strong. O Yes, there is a positive correlation.
Book Pages (x) Price (y) A 500 $7.10 B 700 7.60 C 750 9.10 D 590 6.60 E 560 7.60 F 650 7.10 G 475 5.10 (a) Develop a least squares estimated regression line. (Round your numerical values to three decimal places.) (b) Compute the coefficient of determination. (Round your answer to four decimal places.) Explain its meaning. (Give your answer as a percent. Round your answer to two decimal places.) The value of the coefficient of determination tells us that % of the variability in |--?-- ☑ has been explained by the least squares regression equation.
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Suppose that Dr. Grandtete does some measurements and states that there is a correlation (r) of +1.2 between head circumference and 1.Q. What can you conclude from this result? That head circumference and IQ are very strongly and positively related That head circumference and IQ are weakly, but positively, related That there is a mistake because r cannot equal +1.2 That there is a mistake becauser can only be used to measure psychological variables, not physical ones like head circumference.
Standardising the variables before performing canonical correlation analysis affects which of the following? A. the coefficients to construct the variates (i.e., the a in Z₁ = a¹ X(1) and similarly for X (2)) B. the canonical correlations C. the conclusions of the tests for the significance of the canonical correlations More marks will be given the closer you are to the correct answer. Select one: O None O A only O B only O C only O A and B only O A and C only OB and C only O A, B, and C True or False? Unlike ordinary correlation, canonical correlation can detect and represent nonlinear relationships between variables. Select one: O True O False
4. Question Draw and upload two datasets that have the same correlation coefficients but different slopes. Use the graphs below as a template for your submission. * (Sext |0, 0.5, Y'I, ext(0.5, e, x'}1 * (Pext10, 0.5. Y'i, Text(0.5. o, x') 1.0 1.0 0.8 - 0.8 0.6 0.6 0.4 - 0.4 0.2- 0.2 0.0 0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 0.2 0.4 0.6 0.8 1.0 plot of chunk cor_slope