Concept explainers
Researchers in Food Science studied how big people’s mouths tend to be. They measured mouth volume by pouring water into the mouths of subjects who lay on their backs. Unless this is your idea of a good time, it would be helpful to have a model to estimate mouth volume more simply. Fortunately, mouth volume is related to height. (Mouth volume is measured in cubic centimeters and height in meters.)
The data were checked and deemed suitable for regression. Take a look at the computer output. (Data in Mouth_volume)
1. What does the t-ratio of 3.27 for the slope tell about this relationship? How does the P-value help your understanding?
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Chapter 23 Solutions
Intro STATS, Books a la Carte Plus New Mystatlab with Pearson Etext -- Access Card Package
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- Life Expectancy The following table shows the average life expectancy, in years, of a child born in the given year42 Life expectancy 2005 77.6 2007 78.1 2009 78.5 2011 78.7 2013 78.8 a. Find the equation of the regression line, and explain the meaning of its slope. b. Plot the data points and the regression line. c. Explain in practical terms the meaning of the slope of the regression line. d. Based on the trend of the regression line, what do you predict as the life expectancy of a child born in 2019? e. Based on the trend of the regression line, what do you predict as the life expectancy of a child born in 1580?2300arrow_forwardUsing your graphing calculator, make a scatter plot of the data from the table. Then graph your model from Question 2 along with the data. How well does your model fit the data? What could you do to try to improve your model?arrow_forwardResearchers are interested in predicting the height of a child based on the heights of their mother and father. Data were collected, which included height of the child (height ), height of the mother ( mothersheight ), and height of the father (fathersheight ). The initial analysis used the heights of the parents to predict the height of the child (all units are inches). The results of the analysis, a multiple regression, are presented below. . regress height mothersheight fathersheight Source Model Residual Total height mothersheight fathersheight _cons SS 208.008457 314.295372 522.303829 df 2 37 Interpret the intercept of this model. 104.004228 8.49446952 Coef. Std. Err. MS 39 13.3924059 .6579529 .1474763 .2003584 .1382237 9.804327 12.39987 t P>|t| 4.46 0.000 C 0.156 0.79 0.434 Number of obs = F( 2, 37) = Prob > F R-squared Adj R-squared = Root MSE 40 12.24 0.0001 0.3983 0.3657 2.9145 [95% Conf. Interval] .3591375 -.0797093 -15.32021 .9567683 .4804261 34.92886arrow_forward
- Researchers are interested in predicting the height of a child based on the heights of their mother and father. Data were collected, which included height of the child (height ), height of the mother ( mothersheight), and height of the father (fathersheight ). The initial analysis used the heights of the parents to predict the height of the child (all units are inches). The results of the analysis, a multiple regression, are presented below. . regress height mothersheight fathersheight Source Model Residual Total height mothersheight fathersheight _cons SS 208.008457 314.295372 522.303829 df 104.004228 2 37 8.49446952 MS 39 13.3924059 Coef. Std. Err. .6579529 .1474763 .2003584 .1382237 9.804327 12.39987 Interpret the slope associated with mother's height. t P>|t| 4.46 0.000 с 0.156 0.79 0.434 Number of obs = F( 2, 37) = Prob > F R-squared Adj R-squared = Root MSE = = .3591375 -.0797093 -15.32021 = 40 12.24 0.0001 0.3983 0.3657 2.9145 [95% Conf. Intervall 9567683 .4804261 34.92886arrow_forwardThe slope of a regression line tells you how much or little a change in your dependent variable impacts your independent variable. O TrueO Falsearrow_forwardWhy the regression line is a straight line rather than a curved line?arrow_forward
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