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Ch. 9 - Correlation and Regression
Larson - Elementary Statistics: Picturing the World 8th Edition
Larson8th EditionElementary Statistics: Picturing the WorldISBN: 9780137493470Not the one you use?Change textbook
Chapter 9, Problem 9.1.14

Graphical Analysis In Exercises 11–14, determine whether there is a perfect positive linear correlation, a strong positive linear correlation, a perfect negative linear correlation, a strong negative linear correlation, or no linear correlation between the variables.
Scatterplot showing a perfect positive linear correlation with points closely aligned along an upward sloping line.

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Step 1: Observe the scatterplot provided in the image. The points are closely aligned along a straight line that slopes upward from left to right.
Step 2: Recall the definition of correlation. A perfect positive linear correlation occurs when all data points lie exactly on a straight line with a positive slope, indicating a direct proportional relationship between the variables.
Step 3: Compare the alignment of the points in the scatterplot to the characteristics of a perfect positive linear correlation. Since the points are perfectly aligned along the upward-sloping line, this matches the definition of a perfect positive linear correlation.
Step 4: Note that there is no deviation of the points from the line, which confirms the relationship is perfect and positive.
Step 5: Conclude that the scatterplot demonstrates a perfect positive linear correlation between the variables.

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Key Concepts

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

Correlation

Correlation measures the strength and direction of a linear relationship between two variables. It is quantified by the correlation coefficient, which ranges from -1 to 1. A value of 1 indicates a perfect positive correlation, meaning as one variable increases, the other also increases proportionally. Conversely, a value of -1 indicates a perfect negative correlation, where one variable increases as the other decreases.
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Linear Relationship

A linear relationship between two variables means that the relationship can be represented by a straight line on a graph. This implies that changes in one variable result in proportional changes in the other. In the context of correlation, a strong linear relationship suggests that the data points closely follow a straight line, indicating predictability in the relationship between the variables.
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Scatterplot

A scatterplot is a graphical representation of two variables, where each point represents an observation. It helps visualize the relationship between the variables, making it easier to identify patterns, trends, or correlations. In the provided image, the points are closely aligned along an upward sloping line, indicating a perfect positive linear correlation, where increases in one variable correspond to increases in the other.
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Related Practice
Textbook Question

"Predicting y-Values In Exercises 3-6, use the multiple regression equation to predict the y-values for the values of the independent variables.

3. Cauliflower Yield The equation used to predict the annual cauliflower yield (in pounds

per acre) is y=24,791+4.508x_1-4.723x_2

where x_1 is the number of acres planted and x_2 is the number of acres harvested.(Adapted from United States Department of Agriculture)

a. x_1 = 36,500, x_2 = 36,100

b. x_1 = 38,100, x_2 = 37,800

c. x_1 = 39,000, x_2 = 38,800

d. x_1 = 42,200, x_2 = 42,100"

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Textbook Question

"[APPLET] Registered Nurse Salaries In Exercises 27–30, use the table, which shows the years of experience of 14 registered nurses and their annual salaries (in thousands of dollars). (Adapted from Payscale, Inc.)

27. Correlation Using the scatter plot of the registered nurse salary data shown below, what type of correlation, if any, do you think the data have? Explain.


"

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Textbook Question

In Exercise 25, remove the data for the international soccer player with a maximum weight of 170 kilograms and a jump height of 64 centimeters. Describe how this affects the correlation coefficient r.

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Textbook Question

"In Exercises 9 and 10, identify the explanatory variable and the response variable.

9. A nutritionist wants to determine whether the amounts of water consumed each day by persons of the same weight and on the same diet can be used to predict individual weight

loss."

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Textbook Question

4. For a set of data and a corresponding regression line, describe all values of x that provide meaningful predictions for y.

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Textbook Question

2. Two variables have a positive linear correlation. Is the slope of the regression line for the variables positive or negative?

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