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**Describing the Relation Between Two Variables**

Chapter 4 Describing the Relation Between Two Variables

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**Overview Data for a single variable is univariate data**

Many or most real world models have more than one variable … multivariate data In this chapter we will study the relations between two variables … bivariate data

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**Chapter 4 Chapter 4 – Describing the Relation Between Two Variables**

Only section 1 and 2 Scatter Diagrams and Correlation Least-Squares Regression

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**Scatter Diagrams and Correlation**

Chapter 4 Section 1 Scatter Diagrams and Correlation

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Chapter 4 – Section 1 In many studies, we measure more than one variable for each individual Some examples are Rainfall amounts and plant growth Exercise and cholesterol levels for a group of people Height and weight for a group of people In these cases, we are interested in whether the two variables have some kind of a relationship In many studies, we measure more than one variable for each individual In many studies, we measure more than one variable for each individual Some examples are Rainfall amounts and plant growth Exercise and cholesterol levels for a group of people Height and weight for a group of people

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Chapter 4 – Section 1 When we have two variables, they could be related in one of several different ways They could be unrelated One variable (the explanatory or predictor variable) could be used to explain the other (the response or dependent variable) One variable could be thought of as causing the other variable to change In this chapter, we examine the second case … explanatory and response variables When we have two variables, they could be related in one of several different ways They could be unrelated One variable (the explanatory or predictor variable) could be used to explain the other (the response or dependent variable) One variable could be thought of as causing the other variable to change When we have two variables, they could be related in one of several different ways They could be unrelated When we have two variables, they could be related in one of several different ways When we have two variables, they could be related in one of several different ways They could be unrelated One variable (the explanatory or predictor variable) could be used to explain the other (the response or dependent variable)

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Chapter 4 – Section 1 Sometimes it is not clear which variable is the explanatory variable and which is the response variable Sometimes the two variables are related without either one being an explanatory variable Sometimes the two variables are both affected by a third variable, a lurking variable, that had not been included in the study

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**Chapter 4 – Section 1 An example of a lurking variable**

A researcher studies a group of elementary school children Y = the student’s height X = the student’s shoe size It is not reasonable to claim that shoe size causes height to change The lurking variable of age affects both of these two variables An example of a lurking variable A researcher studies a group of elementary school children Y = the student’s height X = the student’s shoe size An example of a lurking variable A researcher studies a group of elementary school children Y = the student’s height X = the student’s shoe size It is not reasonable to claim that shoe size causes height to change

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**Chapter 4 – Section 1 Some other examples**

Rainfall amounts and plant growth Explanatory variable – rainfall Response variable – plant growth Possible lurking variable – amount of sunlight Exercise and cholesterol levels Explanatory variable – amount of exercise Response variable – cholesterol level Possible lurking variable – diet Some other examples Rainfall amounts and plant growth Explanatory variable – rainfall Response variable – plant growth Possible lurking variable – amount of sunlight

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Chapter 4 – Section 1 The most useful graph to show the relationship between two quantitative variables is the scatter diagram Each individual is represented by a point in the diagram The explanatory (X) variable is plotted on the horizontal scale The response (Y) variable is plotted on the vertical scale

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**Chapter 4 – Section 1 An example of a scatter diagram**

Note the truncated vertical scale!

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Chapter 4 – Section 1 There are several different types of relations between two variables A relationship is linear when, plotted on a scatter diagram, the points follow the general pattern of a line A relationship is nonlinear when, plotted on a scatter diagram, the points follow a general pattern, but it is not a line A relationship has no correlation when, plotted on a scatter diagram, the points do not show any pattern There are several different types of relations between two variables A relationship is linear when, plotted on a scatter diagram, the points follow the general pattern of a line A relationship is nonlinear when, plotted on a scatter diagram, the points follow a general pattern, but it is not a line There are several different types of relations between two variables A relationship is linear when, plotted on a scatter diagram, the points follow the general pattern of a line There are several different types of relations between two variables

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Chapter 4 – Section 1 Linear relations have points that cluster around a line Linear relations can be either positive (the points slants upwards to the right) or negative (the points slant downwards to the right)

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**Chapter 4 – Section 1 For positive (linear) associations Examples**

Above average values of one variable are associated with above average values of the other (above/above, the points trend right and upwards) Below average values of one variable are associated with below average values of the other (below/below, the points trend left and downwards) Examples “Age” and “Height” for children “Temperature” and “Sales of ice cream” For positive (linear) associations Above average values of one variable are associated with above average values of the other (above/above, the points trend right and upwards) Below average values of one variable are associated with below average values of the other (below/below, the points trend left and downwards)

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**Chapter 4 – Section 1 For negative (linear) associations Examples**

Above average values of one variable are associated with below average values of the other (above/below, the points trend right and downwards) Below average values of one variable are associated with above average values of the other (below/above, the points trend left and upwards) Examples “Age” and “Time required to run 50 meters” for children “Temperature” and “Sales of hot chocolate” For negative (linear) associations Above average values of one variable are associated with below average values of the other (above/below, the points trend right and downwards) Below average values of one variable are associated with above average values of the other (below/above, the points trend left and upwards)

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Chapter 4 – Section 1 Nonlinear relations have points that have a trend, but not around a line The trend has some bend in it

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**Chapter 4 – Section 1 When two variables are not related**

There is no linear trend There is no nonlinear trend Changes in values for one variable do not seem to have any relation with changes in the other

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Chapter 4 – Section 1 Nonlinear relations and no relations are very different Nonlinear relations are definitely patterns … just not patterns that look like lines No relations are when no patterns appear at all

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**Chapter 4 – Section 1 Examples of nonlinear relations**

“Age” and “Height” for people (including both children and adults) “Temperature” and “Comfort level” for people Examples of no relations “Temperature” and “Closing price of the Dow Jones Industrials Index” (probably) “Age” and “Last digit of telephone number” for adults Examples of nonlinear relations “Age” and “Height” for people (including both children and adults) “Temperature” and “Comfort level” for people

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Chapter 4 – Section 1 The linear correlation coefficient is a measure of the strength of linear relation between two quantitative variables The sample correlation coefficient “r” is This should be computed with software (and not by hand) whenever possible

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Chapter 4 – Section 1 Some properties of the linear correlation coefficient r is a unitless measure (so that r would be the same for a data set whether x and y are measured in feet, inches, meters, or fathoms) r is always between –1 and +1 Positive values of r correspond to positive relations Negative values of r correspond to negative relations Some properties of the linear correlation coefficient r is a unitless measure (so that r would be the same for a data set whether x and y are measured in feet, inches, meters, or fathoms) r is always between –1 and +1 Positive values of r correspond to positive relations Some properties of the linear correlation coefficient r is a unitless measure (so that r would be the same for a data set whether x and y are measured in feet, inches, meters, or fathoms) r is always between –1 and +1 Some properties of the linear correlation coefficient r is a unitless measure (so that r would be the same for a data set whether x and y are measured in feet, inches, meters, or fathoms)

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Chapter 4 – Section 1 Some more properties of the linear correlation coefficient The closer r is to +1, the stronger the positive relation … when r = +1, there is a perfect positive relation The closer r is to –1, the stronger the negative relation … when r = –1, there is a perfect negative relation The closer r is to 0, the less of a linear relation (either positive or negative) Some more properties of the linear correlation coefficient The closer r is to +1, the stronger the positive relation … when r = +1, there is a perfect positive relation Some more properties of the linear correlation coefficient The closer r is to +1, the stronger the positive relation … when r = +1, there is a perfect positive relation The closer r is to –1, the stronger the negative relation … when r = –1, there is a perfect negative relation

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**Chapter 4 – Section 1 Examples of positive correlation**

In general, if the correlation is visible to the eye, then it is likely to be strong Strong Positive r = .8 Moderate Positive r = .5 Very Weak r = .1

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**Chapter 4 – Section 1 Examples of negative correlation**

In general, if the correlation is visible to the eye, then it is likely to be strong Strong Negative r = –.8 Moderate Negative r = –.5 Very Weak r = –.1

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**Chapter 4 – Section 1 Nonlinear correlation and no correlation**

Both sets of variables have r = 0.1, but the difference is that the nonlinear relation shows a clear pattern Nonlinear Relation No Relation

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**Chapter 4 – Section 1 Correlation is not causation!**

Just because two variables are correlated does not mean that one causes the other to change There is a strong correlation between shoe sizes and vocabulary sizes for grade school children Clearly larger shoe sizes do not cause larger vocabularies Clearly larger vocabularies do not cause larger shoe sizes Often lurking variables result in confounding Correlation is not causation! Just because two variables are correlated does not mean that one causes the other to change There is a strong correlation between shoe sizes and vocabulary sizes for grade school children Clearly larger shoe sizes do not cause larger vocabularies Clearly larger vocabularies do not cause larger shoe sizes Correlation is not causation! Just because two variables are correlated does not mean that one causes the other to change Correlation is not causation!

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**Summary: Chapter 4 – Section 1**

Correlation between two variables can be described with both visual (graphic) and numeric methods Visual methods Scatter diagrams Numeric methods Linear correlation coefficient

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**Least-Squares Regression**

Chapter 4 Section 2 Least-Squares Regression

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Chapter 4 – Section 2 If we have two variables X and Y, we often would like to model the relation as a line Draw a line through the scatter diagram We want to find the line that “best” describes the linear relationship … the regression line

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**Chapter 4 – Section 2 We want to use a linear model**

Linear models can be written in several different (equivalent) ways y = m x + b y – y1 = m (x – x1) y = b1 x + b0 Because the slope and the intercept are important to analyze, we will use We want to use a linear model We want to use a linear model Linear models can be written in several different (equivalent) ways y = m x + b y – y1 = m (x – x1) y = b1 x + b0

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**Residual = Observed – Predicted**

Chapter 4 – Section 2 The difference between the observed value and the predicted value is called an error or residual The formula for the residual is always Residual = Observed – Predicted

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Chapter 4 – Section 2 For example, say that we want to predict a value of y for a specific value of x Assume that we are using y = 10 x + 25 as our model To predict the value of y when x = 3, the model gives us y = 10 = 55, or a predicted value of 55 Assume the actual value of y for x = 3 is equal to 50 The actual value is 50, the predicted value is 55, so the residual (or error) is 50 – 55 = –5 For example, say that we want to predict a value of y for a specific value of x Assume that we are using y = 10 x + 25 as our model To predict the value of y when x = 3, the model gives us y = 10 = 55, or a predicted value of 55 Assume the actual value of y for x = 3 is equal to 50 For example, say that we want to predict a value of y for a specific value of x Assume that we are using y = 10 x + 25 as our model For example, say that we want to predict a value of y for a specific value of x For example, say that we want to predict a value of y for a specific value of x Assume that we are using y = 10 x + 25 as our model To predict the value of y when x = 3, the model gives us y = 10 = 55, or a predicted value of 55

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**Chapter 4 – Section 2 What the residual is on the scatter diagram**

The model line The observed value y The predicted value y The x value of interest

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Chapter 4 – Section 2 We want to minimize the residuals, but we need to define what this means We use the method of least-squares We consider a possible linear mode We calculate the residual for each point We add up the squares of the residuals The line that has the smallest is called the least-squares regression line We want to minimize the residuals, but we need to define what this means We want to minimize the residuals, but we need to define what this means We use the method of least-squares We consider a possible linear mode We calculate the residual for each point We add up the squares of the residuals

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Chapter 4 – Section 2 The equation for the least-squares regression line is given by y = b1x + b0 b1 is the slope of the least-squares regression line b0 is the y-intercept of the least-squares regression line

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Chapter 4 – Section 2 Finding the values of b1 and b0, by hand, is a very tedious process You should use software for this Finding the coefficients b1 and b0 is only the first step of a regression analysis We need to interpret the slope b1 We need to interpret the y-intercept b0

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**Chapter 4 – Section 2 Interpreting the slope b1**

The slope is sometimes referred to as The slope is also sometimes referred to as The slope relates changes in y to changes in x

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**Chapter 4 – Section 2 For example, if b1 = 4 For example, if b1 = –7**

If x increases by 1, then y will increase by 4 If x decreases by 1, then y will decrease by 4 A positive linear relationship For example, if b1 = –7 If x increases by 1, then y will decrease by 7 If x decreases by 1, then y will increase by 7 A negative linear relationship For example, if b1 = 4 If x increases by 1, then y will increase by 4 If x decreases by 1, then y will decrease by 4 A positive linear relationship

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Chapter 4 – Section 2 For example, say that a researcher studies the population in a town (the y or response variable) in each year (the x or predictor variable) To simplify the calculations, years are measured from 1900 (i.e. x = 55 is the year 1955) The model used is y = 300 x + 12,000 A slope of 300 means that the model predicts that, on the average, the population increases by 300 per year For example, say that a researcher studies the population in a town (the y or response variable) in each year (the x or predictor variable) To simplify the calculations, years are measured from 1900 (i.e. x = 55 is the year 1955) For example, say that a researcher studies the population in a town (the y or response variable) in each year (the x or predictor variable) To simplify the calculations, years are measured from 1900 (i.e. x = 55 is the year 1955) The model used is y = 300 x + 12,000

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**Chapter 4 – Section 2 Interpreting the y-intercept b0**

Sometimes b0 has an interpretation, and sometimes not If 0 is a reasonable value for x, then b0 can be interpreted as the value of y when x is 0 If 0 is not a reasonable value for x, then b0 does not have an interpretation In general, we should not use the model for values of x that are much larger or much smaller than the observed values Interpreting the y-intercept b0 Sometimes b0 has an interpretation, and sometimes not If 0 is a reasonable value for x, then b0 can be interpreted as the value of y when x is 0 If 0 is not a reasonable value for x, then b0 does not have an interpretation

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Chapter 4 – Section 2 For example, say that a researcher studies the population in a town (the y or response variable) in each year (the x or predictor variable) To simplify the calculations, years are measured from 1900 (i.e. x = 55 is the year 1955) The model used is y = 300 x + 12,000 An intercept of 12,000 means that the model predicts that the town had a population of 12,000 in the year 1900 (i.e. when x = 0) For example, say that a researcher studies the population in a town (the y or response variable) in each year (the x or predictor variable) To simplify the calculations, years are measured from 1900 (i.e. x = 55 is the year 1955) For example, say that a researcher studies the population in a town (the y or response variable) in each year (the x or predictor variable) To simplify the calculations, years are measured from 1900 (i.e. x = 55 is the year 1955) The model used is y = 300 x + 12,000

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Chapter 4 – Section 2 After finding the slope b1 and the intercept b0, it is very useful to compute the residuals, particularly Again, this is a tedious computation All the least-squares regression software would compute this quantity

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**Summary: Chapter 4 – Section 2**

We can find the least-squares regression line that is the “best” linear model for a set of data The slope can be interpreted as the change in y for every change of 1 in x The intercept can be interpreted as the value of y when x is 0, as long as a value of 0 for x is reasonable

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