Wrinkle recovery angle and tensile strength are the two most important characteristics for evaluating the performance of crosslinked cotton fabric. An increase in the degree of crosslinking, as determined by ester carboxyl band absorbance, improves the wrinkleresistance of the fabric (at the expense of reducing mechanical strength). The accompanying data on x = absorbance and y = wrinkle resistance angle was read from a graph in the paper "Predicting the Performance of Durable Press Finished Cotton Fabric withInfrared Spectroscopy".†x 0.115 0.126 0.183 0.246 0.282 0.344 0.355 0.452 0.491 0.554 0.651y 334 342 355 363365 372 381 392400 412 420Here is regression output from Minitab:PredictorConstantabsorbS = 3.60498Coef321.878156.711SOURCERegressionResidual ErrorTotalSE Coef2.4836.464R-Sq = 98.5%DF1910SS7639.0117.07756.0T129.6424.240.0000.000R-Sq (adj) = 98.3%MS7639.013.0FP587.81(a) Does the simple linear regression model appear to be appropriate? Explain.Yes, a scatter plot of the data shows a strong, linear pattern, and r² = 98.5%.Yes, a scatter plot of the data shows a weak, linear pattern, and ² = 98.5%.O No, a scatter plot of the data shows a strong, linear pattern, and r² = 98.5%.O No, a scatter plot of the data shows a weak, non-linear pattern, and 2 = 98.5%.P0.000(b) What wrinkle resistance angle would you predict for a fabric specimen having an absorbance of 0.315? (Round your answer to two decimal places.)(c) What would be the estimate of the expected wrinkle resistance angle when absorbance is 0.315? (Round your answer to two decimal places.)

(a) Draw the scatterplot by using MINITAB. MINITAB Procedure: Step 1: Choose Graph > Scatter…

Answered: (a) Does the simple linear regression…

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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Give an example of a problem dealing with two possibly related variables (Y and X) for which a linear regression model would not be appropriate. For example, the relationship could be curved instead of linear, or there may be no significant correlation at all. What is the impact of using a linear regression model in this case? What options, other than linear regression, can you see?
A. Interpret the slope B. Interpret the y-intercept C. How many absences could the student have and still pass the course? Assume that 75% is passing
Subject: statistics
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Because colas tend to replace healthier beverages and colas contain caffeine and phosphoric acid, researchers wanted to know whether cola consumption is associated with lower bone mineral density in women. The accompanying data lists the typical number of cans of cola consumed in a week and the femoral neck bone mineral density for a sample of 15 women. Complete parts (a) through (d) below. Click the icon to view the women's data. (a) Find the regression equation treating cola consumption per week as the x-variable. y=x+ (Round to three decimal places as needed.) (b) Interpret the slope. Select the correct choice below and, if necessary, fill in the answer box to complete your choice. OA. For 0 colas consumed in a week, the bone density is predicted to be (Round to three decimal places as needed.) g/cm³. OB. For every cola consumed per week, the bone density decreases by (Round to three decimal places as needed.) g/cm³, on average. OC. For a bone density of 0 g/cm³, the number of colas…
Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (The pair of variables have a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The table below shows the heights (in feet) and the number of stories of six notable buildings in a city. Height, x Stories, y 519 (a) x = 500 feet (c) x = 810 feet (b) x = 649 feet (d) x = 732 feet 775 619 508 491 474 36 53 47 44 43 37 Find the regression equation. y=x+ O (Round the slope to three decimal places as needed. Round the y-intercept to two decimal places as needed.) Choose the correct graph below. OA. 60- 60- 60- 60- 800 Height (feet) 800 Height (feet) 800 Height (feet) Height (feet) (a) Predict the value of y for x = 500. Choose the correct answer below. A. 52 В. 40 C. 48 D. not meaningful (b) Predict the value of y for x = 649. Choose the correct answer below. A. 56 В. 48 O C. 40…
7. The table below shows the round-trip fare for flights from one city on major airlines. These were the lowest prices found online in early 2010. The airlines varied, but the travel dates were all the same. Determine the linear regression for the cost of a round-trip ticket given the distance traveled, use your regression to estimate the cost of a 1500-mi flight, and state the correlation coefficient, r, and describe the trend, shape, and strength for the data contained in this problem. Distance (mi.) Cost ($) 205 183 968 180 3097 598 1939 277 1976 313 1033 318 2977 448 1752 408 1143 335
c. What are the 2 main pieces of information you get from the linear regression model? Should I go back to the data set and recalculate slope and y intercept if I already have determined the equation of the trend line?
In a simple linear regression analysis, the correlation coefficient (r) and the slope (b) ___________ have the same sign?
The calculator screen shows the linear regression for the data in the table. What typeof correlation does the correlation coefficient indicate? X|1,3,4,5,6,8,9 Y|17,12,12,10,8,3,1 A strong positive correlation A weak positive correlation A strong negative correlation A weak negative correlation
You generate a scatter plot using Excel. You then have Excel plot the trend line and report the equation and the r2r2 value. The regression equation is reported asy=55.82x+32.43y=55.82x+32.43and the r2=0.3136r2=0.3136.What is the correlation coefficient for this data set?r =
You make the bold assumption that there is a linear relationship between number of ads and sales revenues. Find the intercept, b0, and the slope, b1, of the estimated regression equation that will minimize the sum of square residuals.

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