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Ch. 10 - Correlation and Regression
Triola - Elementary Statistics 14th Edition
Triola14th EditionElementary StatisticsISBN: 9780137366446Not the one you use?Change textbook
Chapter 10, Problem 10.3.1

se Notation Using Data Set 1 “Body Data” in Appendix B, if we let the predictor variable x represent heights of males and let the response variable y represent weights of males, the sample of 153 heights and weights results in se = 16.27555 cm. In your own words, describe what that value of se represents.

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1
The value 'se' represents the standard error of the estimate, which measures the average distance that the observed values (weights of males) fall from the regression line when predicting weights based on heights.
To interpret the given value of se = 16.27555 cm, it means that, on average, the predicted weights of males deviate from the actual observed weights by approximately 16.27555 cm in the units of the response variable (weights).
The standard error of the estimate is calculated using the formula: (y-y^)2n-2, where y represents the observed values, y^ represents the predicted values, and n is the sample size.
A smaller value of se indicates that the regression model provides a better fit to the data, as the observed values are closer to the predicted values.
In this case, the value of se = 16.27555 cm provides a measure of how well the heights of males (predictor variable x) can predict the weights of males (response variable y) using the regression model.

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Key Concepts

Here are the essential concepts you must grasp in order to answer the question correctly.

Standard Error (se)

The standard error (se) measures the accuracy of a sample mean in estimating the population mean. It quantifies the variability of the sample means around the true population mean, indicating how much the sample mean is expected to fluctuate due to sampling variability. A smaller se suggests that the sample mean is a more precise estimate of the population mean.
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Predictor and Response Variables

In statistical modeling, the predictor variable (independent variable) is the variable that is manipulated or used to predict changes in another variable, known as the response variable (dependent variable). In this context, heights of males (x) are used to predict weights of males (y), establishing a relationship that can be analyzed through regression techniques.
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Correlation and Regression

Correlation measures the strength and direction of a linear relationship between two variables, while regression analysis estimates the relationship between a predictor and a response variable. In this scenario, understanding how heights correlate with weights allows for predictions about weight based on height, and the se value reflects the precision of these predictions.
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Related Practice
Textbook Question

Finding the Best Model

In Exercises 5–16, construct a scatterplot and identify the mathematical model that best fits the given data. Assume that the model is to be used only for the scope of the given data, and consider only linear, quadratic, logarithmic, exponential, and power models.

Landing on the Moon When the Apollo spacecraft landed on the Moon, the rocket engine would typically cut off at about 1.3 meters above the surface so that hot gases and dust and other surface materials would not cause damage. The landing module was in freefall starting at about 1 meter above the surface. The table below lists the time t (seconds) after being dropped and the distance d (meters) travelled by an object dropped near the surface of the Moon.

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Textbook Question

Finding the Best Model

In Exercises 5–16, construct a scatterplot and identify the mathematical model that best fits the given data. Assume that the model is to be used only for the scope of the given data, and consider only linear, quadratic, logarithmic, exponential, and power models.

Earthquakes Listed below are earthquake depths (km) and magnitudes (Richter scale) of different earthquakes. Find the best model and then predict the magnitude for the last earthquake with a depth of 3.78 km. Is the predicted value close to the actual magnitude of 7.1?

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Textbook Question

Garbage: Finding the Best Multiple Regression Equation

In Exercises 9–12, refer to the accompanying table, which was obtained by using the data from 62 households listed in Data Set 42 “Garbage Weight” in Appendix B. The response (y) variable is PLAS (weight of discarded plastic in pounds). The predictor (x) variables are METAL (weight of discarded metals in pounds), PAPER (weight of discarded paper in pounds), and GLASS (weight of discarded glass in pounds).

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If exactly two predictor (x) variables are to be used to predict the weight of discarded plastic, which two variables should be chosen? Why?

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Textbook Question

Interpreting R^2 For the multiple regression equation given in Exercise 1, we get R^2 = 0.897. What does that value tell us?

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Textbook Question

Interpreting r

In Exercises 5–8, use a significance level of α = 0.05 and refer to the accompanying displays.

Bear Weight and Chest Size Fifty-four wild bears were anesthetized, and then their weights and chest sizes were measured and listed in Data Set 18 “Bear Measurements” in Appendix B; results are shown in the accompanying Statdisk display. Is there sufficient evidence to support the claim that there is a linear correlation between the weights of bears and their chest sizes? When measuring an anesthetized bear, is it easier to measure chest size than weight? If so, does it appear that a measured chest size can be used to predict the weight?

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Textbook Question

Testing for a Linear Correlation

In Exercises 13–28, construct a scatterplot, and find the value of the linear correlation coefficient r. Also find the P-value or the critical values of r from Table A-6. Use a significance level of α = 0.05. Determine whether there is sufficient evidence to support a claim of a linear correlation between the two variables. (Save your work because the same data sets will be used in Section 10-2 exercises.)

Taxis Using the data from Exercise 15, is there sufficient evidence to support the claim that there is a linear correlation between the distance of the ride and the fare (cost of the ride)?

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