6. The following chart shows the winning Olympic Men's High Jump distances.Height(inches)Year190076190471190875191276192074192478192876193277193679194878195280195683196088196491196885197281197689198082198492a) Create a scatter plot to display these data and describe the type of correlation between the height andtimeb) Use the regression to estimate the winning height in the year 1995.

On the basis of below graph and trendline we can say that there is high positive correlation between…

Answered: 6. The following chart shows the…

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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The ordered pairs below give the winning times (in seconds) of a 100-meter freestyle swim race at a regularly-held competition from 1984 through 2012. (1984, 54.22) (2000, 52.13) (1988, 53.23) (2004, 52.14) (1992, 52.94) (2008, 51.42) (1996, 52.80) (2012, 51.30) (a) Sketch a scatter plot of the data. Let y represent the winning time (in seconds) and let t = 84 represent 1984. (b) Sketch the line that you think best approximates the data and find an equation of the line. (Round the slope and y-intercept to one decimal place.) y = (c) Use the regression feature of a graphing utility to find the equation of the least squares regression line that fits the data. (Round the slope and y-intercept to two decimal places.) y =
The numbers of pass attempts and passing yards for seven professional quarterback for a recent year are listed in the table below. Round all answers to the nearest 1000th. Pass attempts (x) 449 565 528 197 670 351 218 Passing Yards (y) 3265 4018 3669 1141 5177 2362 1737 Calculate the sample correlation coefficient, r. Describe the type of correlation coefficient and interpret the correlation in the context of the data. Find the equation of the regression line for the data. Use the regression equation to predict the average number passing yards if the pass attempts are 250.
The following data represent the number of flash drives sold per day at a localcomputer shop and their prices.Price Units Sold34 336 432 635 530 938 240 1a. Develop the estimated regression equation that could be used to predict thequantity sold given the price. Interpret the slope.b. Did the estimated regression equation provide a good fit? Explain.c. Compute the sample correlation coefficient between the price and the number offlash drives sold. Use a= 0.01 to test the relationship between price and units sold.d. How many units can be sold per day if the price of flash drive is set to $28.
am. 13.
Listed below are paired data consisting of movie budget amounts and the amounts that the movies grossed. Find the regression equation, letting the budget be the predictor (x) variable. Find the best predicted amount that a movie will gross if its budget is $140 million. Use a significance level of a = 0.05. Budget ($)in Millions Gross ($) in Millions 39 22 118 67 77 49 124 66 12 64 128 19 6. 147 9. 118 6. 99 75 127 107 95 99 64 109 209 44 8 293 48 Click the icon to view the critical values of the Pearson correlation coefficient r. The regression equation is y =+x. (Round to one decimal place as needed.)
The ordered pairs below give the winning times (in seconds) of a 100-meter freestyle swim race at a regularly-held competition from 1984 through 2012. (1984, 55.12) (2000, 53.03) (1988, 54.13) (2004, 53.04) (1992, 53.84) (2008, 52.32) (1996, 53.70) (2012, 52.20) (a) Sketch a scatter plot of the data. Let y represent the winning time (in seconds) and let t = 84 represent 1984. (b) Sketch the line that you think best approximates the data and find an equation of the line. (Round the slope and y-intercept to one decimal place.) y = (c) Use the regression feature of a graphing utility to find the equation of the least squares regression line that fits the data. (Round the slope and y-intercept to two decimal places.) y = (d) Compare the linear model you found in part (b) with the linear model you found in part (c). The models are not similar.The models are similar.
Listed below are paired data consisting of movie budget amounts and the amounts that the movies grossed. Find the regression equation, letting the budget be the predictor (x) variable. Find the best predicted amount that a movie will gross if its budget is $135 million. Use a significance level of alpha equals 0.05 . Budget left parenthesis $ right parenthesis in Millions 45 24 115 74 72 48 117 67 4 60 124 24 6 152 8 Gross left parenthesis $ right parenthesis in Millions 115 10 95 69 127 112 93 101 50 102 223 26 18 282 56 The regression equation is ŷ = __ + __x. (Round to one decimal place as needed.)
The following table shows the values of the age and the job satisfaction of ice cream shop employees Employee Age Job Satisfaction16 918 920 722 824 426 528 626 524 722 8 d. Sketch a scatterplot of this data by hand (do NOT use/submit any plots created by a program).e. Does there appear to be a linear relationship between the two variables? f. Calculate the slope for this data.
Listed below are paired data consisting of movie budget amounts and the amounts that the movies grossed. Find the regression equation, letting the budget be the predictor (x) variable. Find the best predicted amount that a movie will gross if its budget is $105 million. Use a significance level of a = 0.05. Budget ($)in Millions Gross ($) in Millions 41 23 114 75 78 47 120 64 10 59 127 22 12 150 2 0 127 18 111 65 113 112 102 94 64 98 211 41 22 288 57 Click the icon to view the critical values of the Pearson correlation coefficient r. ..... x. (Round to one decimal place as needed.) The regression equation is = + The best predicted gross for a movie with a $105 million budget is $ million. (Round to one decimal place as needed.)
Life expectancy in the United States has been rising since the nineteenth century. The table shows the U.S. life expectancy in selected years. Year 1950 1960 1970 1980 1990 2000 Life expectancy at birth 68.2 69.7 70.8 73.7 75.4 77 a. Let t represent the number of years after 1950, and plot the data. Draw a line a best fit for the data points. 80+ 76- 5 10 15 20 25 30 35 40 45 50 Draw: 72+ 64- 60+ 56 52 48- 44 Clear All
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