Weight Gain According to Water Consumption 10 8. 7 6. 5. 4 1. 23 456 Glasses of Water Consumed per Day Looking at the graph above showing a line of best fit, what is the correlation between the x and y variables? O A. y increases as x increases O B. y is constant O C.y decreases as x decreases O D. y decreases as x increases Amount of Weight Gained (Ib)

Weight Gain According to Water Consumption 10 8. 7 6. 5. 4 1. 23 456 Glasses of Water Consumed per Day Looking at the graph above showing a line of best fit, what is the correlation between the x and y variables? O A. y increases as x increases O B. y is constant O C.y decreases as x decreases O D. y decreases as x increases Amount of Weight Gained (Ib)

Functions and Change: A Modeling Approach to College Algebra (MindTap Course List)

6th Edition

ISBN:9781337111348

Author:Bruce Crauder, Benny Evans, Alan Noell

Publisher:Bruce Crauder, Benny Evans, Alan Noell

Chapter5: A Survey Of Other Common Functions

Section5.3: Modeling Data With Power Functions

Problem 1E: Zipfs Law The following table shows U.S cities by rank in terms of population and population in...

See similar textbooks

Similar questions

Zipfs Law The following table shows U.S cities by rank in terms of population and population in thousands. City Rank r Population N New York 1 8491 Chicago 3 2722 Philadelphia 5 1560 Dallas 9 1280 Austin 11 913 San Francisco 13 852 Columbus 15 836 A rule known as Zipfs law tells us that it is reasonable to approximate these data with a power function. a Use power regression to express the population as a function of the rank. b Plot the data along with the power function from part a. c Phoenix is the sixth largest city in the United States. Use your answer from part a to estimate population of Phoenix. Round your answer in thousands to the nearest whole number. Note: The actual population is 1537 thousand.
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?
13/20 Compute the linear correlation coefficient between age and HDL cholesterol. r = _. Round to three decimal places as needed. Does a linear relation exist between age and HDL cholesterol?
A random variable X is normally distributed with a mean of 100 and a variance of 100, and a random variable Y is normally distributed with a mean of 200 and a variance of 400. The random variables have a correlation coefficient equal to 0.5. Find the mean and variance of the random variable:W = 5X + 4Y
1. Compute the correlation coefficient. (Round your answer to 4 decimal places.) 2. Compute the correlation coefficient between x2 and y. (Round your answer to 4 decimal places.)