The following table shows the number of fiber optic cable connections to homes in a country from 2000 to 2004 (t = 0 represents 2000). Year t o 1 2 3 4 Connections c s15 3o 70 155 (Thousands) (a) Use technology to obtain the linear regression line and the correlation coefficient r, with all coefficients rounded to two decimal places. c(t) = Plot the regression line and the given points. 100 100 (b) Does the graph suggest that a nonlinear relationship between t and c would be more appropriate than a linear one? Why? Yes, the first and last points lie above the regression line, while the central points lie below it, suggesting a nonlinear relationship. No, the points appear to be randomly scattered about the regression line which does not imply a nonlinear relationship. (C) Use technology to obtain the residuals. (Round your answers to two decimal places.) Year t Residual 1 2 3 4 What can you say about the residuals in support of the claim in part (b)? The residuals vary from positive to negative with no observable pattern, confirming the impression from the graph. The residuals are positive at first, become negative, and then become positive, confirming the impression from the graph.

The following table shows the number of fiber optic cable connections to homes in a country from 2000 to 2004 (t = 0 represents 2000). Year t o 1 2 3 4 Connections c s15 3o 70 155 (Thousands) (a) Use technology to obtain the linear regression line and the correlation coefficient r, with all coefficients rounded to two decimal places. c(t) = Plot the regression line and the given points. 100 100 (b) Does the graph suggest that a nonlinear relationship between t and c would be more appropriate than a linear one? Why? Yes, the first and last points lie above the regression line, while the central points lie below it, suggesting a nonlinear relationship. No, the points appear to be randomly scattered about the regression line which does not imply a nonlinear relationship. (C) Use technology to obtain the residuals. (Round your answers to two decimal places.) Year t Residual 1 2 3 4 What can you say about the residuals in support of the claim in part (b)? The residuals vary from positive to negative with no observable pattern, confirming the impression from the graph. The residuals are positive at first, become negative, and then become positive, confirming the impression from the graph.

Name: Help me to solve this problem please. Thank You The following table sh
Uploaded: 2024-03-20T07:06:27.000Z
Channel: Bartleby
Description: Help me to solve this problem please. Thank You The following table shows the number of fiber optic cable connections to homes in a country from 2000 to 2004 (t = 0 represents 2000).Year t o 1 2 3 4Connections c s15 3o 70 155(Thousands)(a) Use techno

College Algebra

7th Edition

ISBN:9781305115545

Author:James Stewart, Lothar Redlin, Saleem Watson

Publisher:James Stewart, Lothar Redlin, Saleem Watson

Chapter1: Equations And Graphs

Section: Chapter Questions

Problem 10T: Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s...

See similar textbooks

Similar questions

Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
The following table shows the number of fiber optic cable connections to homes in a country from 2000 to 2004 (t = 0 represents 2000). Year t 0 1 2 3 4 Connections c (Thousands) 5 15 30 70 155 (a) Use technology to obtain the linear regression line and the correlation coefficient r, with all coefficients rounded to two decimal places. c(t) = r =
The following table gives the data for the average temperature and the snow accumulation in several small towns for a single month. Determine the equation of the regression line, yˆ=b0+b1x�^=�0+�1�. Round the slope and y-intercept to the nearest thousandth. Then determine if the regression equation is appropriate for making predictions at the 0.050.05 level of significance. Critical Values of the Pearson Correlation Coefficient Average Temperatures and Snow Accumulations Average Temperature (℉℉) 3939 2525 1515 4242 4242 2424 3232 2020 3030 3737 Snow Accumulation (in.in.) 66 1515 2929 66 1414 2626 2323 1212 1616 77 Copy Data Regression equation: yˆ=�^= Is the equation appropriate? Yes
The following table contains data for the number of bacteria y (in millions) growing in a particular medium after x hours. Using a calculator or statistical software, find the linear regression line for the data in the table. Enter your answer in the form y=mx+b, with m and b both rounded to two decimal places. x y 0 3.67 1 5.24 2 7.04 3 10.31 4 9.83 5 11.04 6 14.68 7 15.02
The following table gives the data for the average temperature and the snow accumulation in several small towns for a single month. Determine the equation of the regression line, yˆ=b0+b1xy^=b0+b1x. Round the slope and y-intercept to the nearest thousandth. Then determine if the regression equation is appropriate for making predictions at the 0.01 level of significance. Critical Values of the Pearson Correlation Coefficient Average Temperatures and Snow Accumulations Average Temperature (℉℉) 45 31 16 35 35 22 26 23 25 39 Snow Accumulation (in.in.) 9 20 23 6 10 29 22 14 12 9 Question 1: Regression equation y=__________ Question 2: Is the equation appropriate? Yes or No?
A random sample of nonindustrialized countries was selected, and the life expectancy in years is listed for both men and women. Men 61.7 42.8 72.6 57.9 55.4 62.1 Women 50.4 74.2 75.3 75.4 49.1 65.2 The correlation coefficient for the data is =r−0.003 and =α0.05. Should regression analysis be done? Now Find the equation of the regression line. Round the coefficients to at least three decimal places. =y′+abx =a =b Now Find women's life expectancy in a country where men's life expectancy =60 years. Round your answer to at least three decimal places. Women's life expectancy is ____ years.
The following table gives the data for the average temperature and the snow accumulation in several small towns for a single month. Determine the equation of the regression line, yˆ=b0+b1x. Round the slope and y-intercept to the nearest thousandth. Then determine if the regression equation is appropriate for making predictions at the 0.01 level of significance. Critical Values of the Pearson Correlation Coefficient Average Temperatures and Snow Accumulations Average Temperature (℉) 42 27 22 39 44 21 27 18 32 36 Snow Accumulation (in.) 6 11 22 8 12 27 27 20 12 6
Suppose that you run a correlation and find the correlation coefficient is 0.421 and the regression equation is ˆy=2.2x+6.96y^=2.2x+6.96. The center of your x-data was 5.2 and the center of your y-data was 18.6.If the critical value is .396, use the appropriate method to predict the yy value when xx is 6.8
The birth lengths in cm (x) and birth weights in kg (y) of a sample of 50 newborn female babies are compared, yielding a correlation coefficient of r=0.578 and a linear regression equation of ŷ =−8.89+0.243x The babies all had lengths between 46.5 and 53.0 cm, and weights between 2.50 and 4.05 kg. Based on this, predict the birth weight of a newborn female baby with a birth length of 48.5 cm.
uppose IQ scores were obtained from randomly selected couplescouples. For 2020 such pairs of people, the linear correlation coefficient is 0.8900.890 and the equation of the regression line is ModifyingAbove y with caret equals negative 0.74 plus 1.02 xy=−0.74+1.02x, where x represents the IQ score of the husbandhusband. Also, the 2020 x values have a mean of 101.17101.17 and the 2020 y values have a mean of 102.7102.7. What is the best predicted IQ of the wifewife, given that the husbandhusband has an IQ of 9999? Use a significance level of 0.05. The best predicted IQ of the wifewife is nothing. (Round to two decimal places as needed.
A set of n = 15 pairs of X and Y values has a correlation of r = +0.80 with SSY = 75, and the regression equation for predicting Y is computed. Find the standard error of estimate for the regression equation. How big would the standard error be if the sample size were n = 30.
If x and y in a regression model are totally unrelated, _______. the coefficient of determination would be 0 the MSE would be 0s the SSE would be 0 the correlation coefficient would be -1 the coefficient of determination would be 1