In an study similar to the one in Exercise 3, the relative humidity and ozone levels were measured for 120 days in another city. The MINITAB output follows. Assume that assumptions 1 through 4 on page 549 hold. The regression equation is Ozone = 29.7 - 0.135 Humidity Predictor Coef SE Coef тР Constant 29.703 2.066 14.38 0.000 Humidity -0.13468 0.03798 -3.55 0.001 S= 6.26R-Sq = 9.6 %R-Sq(adj) = 8.9 % What is the slope of the least-squares line? Find a 95% confidence interval for the slope. Perform a test of the null hypothesis that the slope is greater than or equal to -0.1. What is the P-value? a. Б. C.

In an study similar to the one in Exercise 3, the relative humidity and ozone levels were measured for 120 days in another city. The MINITAB output follows. Assume that assumptions 1 through 4 on page 549 hold. The regression equation is Ozone = 29.7 - 0.135 Humidity Predictor Coef SE Coef тР Constant 29.703 2.066 14.38 0.000 Humidity -0.13468 0.03798 -3.55 0.001 S= 6.26R-Sq = 9.6 %R-Sq(adj) = 8.9 % What is the slope of the least-squares line? Find a 95% confidence interval for the slope. Perform a test of the null hypothesis that the slope is greater than or equal to -0.1. What is the P-value? a. Б. C.

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

Section5.6: Higher-degree Polynomials And Rational Functions

Problem 1TU: The following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t=...

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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 fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t= Years since 2006 0 1 2 3 4 5 6 7 8 9 10 K= Price 56 51 50 55 58 52 45 43 44 48 51 Make a quartic model of these data. Round the regression parameters to two decimal places.
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A researcher notes that, in a certain region, a disproportionate number of software millionaires were born around the year 1955. Is this a coincidence, or does birth year matter when gauging whether a software founder will besuccessful? The researcher investigated this question by analyzing the data shown in the accompanying table. Complete parts a through c below. a. Find the coefficient of determination for the simple linear regression model relating number (y) of software millionaire birthdays in a decade to total number (x) of births in the region. Interpret the result. The coefficient of determination is 1.___? (Round to three decimal places as needed.) This value indicates that 2.____ of the sample variation in the number of software millionaire birthdays is explained by the linear relationship with the total number of births in the region. (Round to one decimal place as needed.) b. Find the coefficient of determination for the simple linear regression model…
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A company has a set of data with employee age (X) and the corresponding number of annual on-the-job-accidents (Y). Analysis on the set finds that the regression equation is Y=60-0.5*X. What can be said of the correspondence (relation) between age and accidents? Are younger workers safer or more prone to accident? What is the likely number of accidents for someone aged 25?
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The Mayor of texas whom is partners with a local agriculturalist wants to know how the amount of fertilizer and the amount of water given to plants affect their growth. The results were inputted into MINITAB so as to fit the model a) Write out the regression equation b) What is the sample size used in this investigation? c) Determine the values of *, ** and ***, **** d) Conduct a hypothesis test, at the 5% level of significance, to determine whether ? is significant. e) What would be the growth of the plant if 4g of fertilizer and 7g of ater was given to it daily? f) Carry out an F -test at the 1% significance level to determine whether the model is significant
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