On the basis of observations made on 39 cottonplants, tne total correlation of yield of cotton (x,), number of tollsi.e., seed vessels (x2) and height (x3) are found to ber1g=0-8, r13=0•65 and r23=0•7Compute the partial correlation between yield of cotton andthe number of bolls eliminating the effect of height.

Answered: Image /qna-images/answer/4a301bb8-ee15-4f58-9283-793f0b2d95bc.jpg

Answered: On the basis of observations made on 39…

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 regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y).The results of the regression were:ˆy=bx+ay^=bx+ab=−1.217b=-1.217a=39.762a=39.762R2=0.695556R2=0.695556a) Use this to predict the number of situps a person who watches 13 hours of TV can do (to 3 decimal places) b) What is the value of the correlation coefficient (to 3 decimal places) ?
The simple correlation coefficients between profits (X,), sales (X,) and advertising ex- = 0.45 and r,, = 0.58. Find the partial correlation coefficient penditure (X,) of a factory are r, = 0.69, r3 and r13.2 123 and interpret them.
Draw scatter diagrams for the different values of correlation co-efficient 'r' given below: (i) r = 1 (ii) r =-1 (iii) r=0 (iv) r= 0.90 (v) r = 0.10 (vi)r =-0.88 Explain the type of relationship you would expect between 'x' and 'y' in each of the above cases.
The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, y^=b0+b1x, for predicting a woman's bone density based on her age. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. age 34 37 44 63 71 bone density 342 333 330 325 312 Find the estimated slope. Round your answer to three decimal places.
The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, yˆ=b0+b1xy^=b0+b1x, for predicting a woman's bone density based on her age. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Find the estimated slope. Round your answer to three decimal places. age 35 41 52 56 66 bone density 358 350 348 332 321
The table below gives the list price and the number of bids received for five randomly selected items sold through online auctions. Using this data, consider the equation of the regression line, yˆ=b0+b1xy^=b0+b1x, for predicting the number of bids an item will receive based on the list price. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Price in Dollars 2323 2626 3737 4141 4747 Number of Bids 11 22 44 55 66 Table Step 1 of 6: Find the estimated slope. Round your answer to three decimal places. Step 2 of 6: Find the estimated y-intercept. Round your answer to three decimal places. Step 3 of 6: Determine if the statement "Not all points predicted by the linear model fall on the same line" is true or false. Step 4 of 6: Substitute the values you…
The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, yˆ=b0+b1xy^=b0+b1x, for predicting a woman's bone density based on her age. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Age 39 41 51 58 67 Bone Density 359 352 338 324 313 Table Copy Data Step 1 of 6 : Find the estimated slope. Round your answer to three decimal places.
You are asked to do quality control and quality assurance during the determination of lead concentration in a city’s tap water using ICP-AES. Plotting ICP -AES signal as a function of lead concentration (in ppb) gave you a linear regression equation of y = 1.97x + 1.50 with an R^2=0.99. The instrumental response (signal) had a standard deviation of 0.75. What is the limit of quantitation in terms of lead concentration (ppb) of this instrumental method?
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y).The results of the regression were: ˆy=b0+b1xy^=b0+b1xb0=33.611b0=33.611b1=−0.718b1=-0.718r=−0.735r=-0.735 Use this to predict the number of situps a person who watches 12 hours of TV can do (to one decimal place)
There is a relation 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
A paper† gave data on x = change in Body Mass Index (BMI in kilograms/meter2) and y = change in a measure of depression for patients suffering from depression who participated in a pulmonary rehabilitation program. JMP output for these data is shown below. A scatterplot titled "Bivariate Fit of Depression Score Schange by BMI Change" has 12 points and a line plotted on it. The horizontal axis is labeled "BMI change" and ranges from about −0.8 to about 1.8. The vertical axis is labeled "Depression score change" and ranges from about −2 to 20. The points are plotted from left to right in an upward, diagonal direction starting from the middle left of the diagram. The points are very scattered and are between approximately −0.5 to 1.5 on the horizontal axis and between approximately −1 to 18 on the vertical axis. A line with positive slope titled "Linear Fit" is drawn across the plot to approximate the trend of the points. The line enters the viewing window at about (−0.8, 3) and exits at…
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

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