15.20. On the basis of observations made on 30 cottonplants, the total correlation of yield of cotton (X,), number of balls i.e., seedvessels (X,), and height (X,) are found to ber12 = 0.8, r30.65 and r,= 0.7%3D%3D23Calculate the partial correlation between yield of cotton and the no. ofballs, eliminating the effect of height.

Answered: Image /qna-images/answer/3bcef0aa-61b5-40d8-afac-d36a17d2e203.jpg

Answered: 15.20. On the basis of observations…

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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A method of estimating earth temperature based on local precipitation is to build models based on 100s of years of earth at several different constant green house gas level (So constant global temperature for 100s of years) , and compare the correlation of statistical relationships between predicted precipitation patterns in a region, and observed precipitation patterns for a region in a few year time period.
There is a relationship in the form of Y = 1 / (a * x ^ b) (x ^ b: means x over b).a) Calculating the correlation coefficient and interpreting the degree of relationship?b) Estimating the y value for x = 4.3 and the x value for y = 0.90 by obtaining the regression equation
In an analysis of a sample of bivariate data concerning the soil acidity x (in pH) and germination time y (in days) for tomato seeds, a linear regression model is constructed and follows the equation y^=−1.22x+5y^=−1.22x+5 Select the most appropriate statement about the linear correlation exhibited. A. The data set exhibits strong positive linear correlation. B.We cannot determine anything about the strength or direction of linear correlation from this information alone. C. The data set exhibits strong negative linear correlation. D. The linear correlation coefficient is -1.22.
There is a relationship between the following variables as y = 1 / (a * x ^ b) (x ^ b: means x over b). a) Calculate the correlation coefficient and interpret the degree of the relationship? b) Estimate the y value for x = 4.3 and the x value for y = 0.90 by obtaining the regression equation. x 1.2 1.5 2.0 3.0 4.0 5.0 y 0.58 0.50 0.32 0.18 0.15 0.10 x 1.2 1.5 2.0 3.0 4.0 5.0 y 0.58 0.50 0.32 0.18 0.15 0.10
please answer number 29
Given the “data” determined by y = x^3 + (x-1)^2 with x = 0.1, 1.2, 2.3, and 2.9, calculate SSTO, SSR, and R^2. Then recalculate these using x = 0.3, 1, 2.45, and 2.8. Does where you collect your data (i.e., which values of x) appear to impact your interpretation of how good the linear model fits?.
I have modeled adult worker's salaries based upon their work experience. After surveying 200 adults about their salary and years in the workforce, I have found the following model: y=12000+5000*x where y is the predicted salary and x is the years in the labor force. R^2 = 0.63. What is the correlation for this model? (correlation between salary and years in the labor force)? a. 0 b. 0.79 c. 0.63 d. 0.50 e. 1
For 9 observations on supply (X) and price (Y) the following data are obtained∑(X-90) = -25 , ∑(X-90)2 = 301, ∑(Y-127) = 12, ∑(Y-127)2 = 1006∑(X-90) ∑(Y-127) = -469. Compute correlation coefficient?
A study of king penguins looked for a relationship between how deep the penguins dive in meters (x) and how long they stay underwater in minutes (y). The study reported that the regression equation was ˆy=−0.044x+4.937y^=-0.044x+4.937 and the coefficient of determination was 0.81What is the correlation coefficient for this data set (round your answer to 2 decimal places)?
Find the slope of regression line, y-intercept of regression line, coefficient of determination (r^2), and the linear correlation coefficient (r)
3. Wine Participant magazine has collected average price per bottle for the prestigious Chateau Le Thundebird bordeaux for different vintages (years). The data appears in the table below. year of bottling price a) draw the scatter diagram showing how wine price varies by vintage year b) use the most appropriate regression equation to determine the relationship between year of bottling (age) and price. c) what is the explanatory power (RSQ) of that equation d) determine the predicted price of a bottle of this wine for the 2017 vintage. 2009 36 2010 40 2011 51 2012 60 2013 68 2014 72 2015 70 2016 65 2018 51 2019 44 2020 39

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