Solution-
c)
Coefficient of determination is 88.7%
So,
The least squares line explains 88.7% of variation in Length(response variable).
coefficient of determination means proportion of variation in 'y' explained by 'x'
The following data represent the time between eruptions and the length of eruption for 8 randomly...
The following data represent the time between eruptions and the length of eruption for 8 randomly selected geyser eruptions. Time, x Length, y 12.16 1.87 11.73 1.80 11.99 1.87 12.17 1.89 11.28 1.64 11.68 1.74 12.24 1.90 11.57 1.71 11.74 1.78 (a) What type of relation appears to exist between time between eruptions and length of eruption? A. Linear, negative association B. Linear, positive association Your answer is correct. C. A nonlinear pattern. D. No association. (b) Does the residual...
Length, y The following data represent the time between eruptions and the length of eruption for 8 randomly selected geyser eruptions. Complete parts (a) through (c) below Click here to view a scatter plot of the data, Click here to view a residual plot of the data Time, x 12.14 11.75 12.02 12.11 11.29 1.88 1.72 1.86 1.87 1.65 Time, 11.66 12.19 11.61 11.66 Length, y 1.69 1.87 1.71 1.75 (a) What type of relation appears to exist between time...
5. The data below represent duration times in seconds) of eruptions and time intervals (in minutes) to the next eruption for randomly selected eruptions of the Old Faithful geyser in Yellowstone National Park. Duration 242 255 227 251 262 207140 Interval After 91 81 91 92 102 94 91 a. Find the regression equation. b. Construct a residual plot for the data. Use the table below to guide you. y-9 Point on Plot Y c. Is a linear model appropriate...
Styles The data in the accompanying table represent the population of a certain country every 10 years for the years 1900-2000. An ecologist is interested in finding an equation that describes the population of the country over time. Complete parts (a) through (3) below Year, x 1900 1910 1920 1930 1940 1950 Population, y Year, x Population, y 179,323 203,302 79,212 1960 95,228 1970 104,021 1980 123,202 1990 132,164 2000 151,325 226,542 248,709 281,421 (a)Determine the least-squares regression equation, treating...
Perform the 6 step process to test the claim: There is a positive linear correlation between the duration of an eruption and the time to the next eruption. Make sure in step four to show the scatter plot and the residual plot to see if linear regression is appropriate. Summary duration Graph of residuals tatistics Multiple correlation coefficient 6 Coefficient of determination R2 0,921365528 0,848914437 0,844938501 5,410216471 15 10 7 RA2 adjusted 8 Typical error 10 11 VARIANCE ANALYSIS 12...
Score: 0 or 3 pts 9.2.33 Use the data in the table below to complete parts (a) through (d) 11 of 15 (10 complete) 34 20 40 23 Click the icon to view details on how to construct and interpret residual plots. 43 26 59 28 53 26 21 29 28 23 뮬 (a) Find the equation of the regression line. Steps for Constructing a Residual Plot (Round to three decimal places as needed.) To construct a residual plot, make...
Help with coding in R: cyl<-factor(scan(text= "6 6 4 6 8 6 8 4 4 6 6 8 8 8 8 8 8 4 4 4 4 8 8 8 8 4 4 4 8 6 8 4")) am<-factor(scan(text= "1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 1 1 1 1 1 1 1")) ## 1)## Using the data `cyl` and `am` (transmission...
6). a.b.b. The data in the table to the right are based on the results of a survey comparing the commute time of adults to their score on a well-being test. Complete parts (a) through (d) below. Click the icon to view the critical values for the correlation coefficient. Commute Time (in minutes) Well-Being Score 5 69.5 16 68.9 27 67.5 34 67.4 47 66.9 68 65.9 97 63.9 (a) Which variable is likely the explanatory variable and which is...
The following are 30 time lapses in minutes between eruptions of Old Faithful geyser in Yellowstone National Park, recorded between the hours of 8 a.m. and 10 p.m. on a certain day, and measured from the beginning of one eruption to the beginning of the next: 68, 63, 66, 63, 61, 44, 60, 62, 71, 62, 62, 55, 62, 67, 73, 72, 55, 67, 68, 65, 60, 61, 71, 60, 68, 67, 72, 69, 65, 66 A researcher wants to...
1. Consider data from a study of the association between vapor pressure (in mm and temperature (in degrees K). The vapor pressure y is the response and the temperature x is the predictor. We import the data with R and display a few rows. Hg) of water > vapor<-read.csv("VaporPressure.csv") > head(vapor) Temp.. in.K. Vapor.Pressure 4.6 1 273 283 9.2 2 3 293 17.5 4 303 31.8 313 55.3 323 92.5 (a) Here is a scatter plot of vapor pressure against...