Linear Regression and the Coefficient of Determination

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Section 4.2

• Linear Regression and the Coefficient of
  Determination

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The Least Squares Line

• When there appears to be a linear relationship
  between x and y we attempt to fit a line to
  the scatter diagram.

Least Squares Criterion
The sum of the squares of the vertical distances
from the points to the line is made as small as
possible.
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Least Squares Criterion

• d represents the difference between the y
  coordinate of the data point and the
  corresponding y coordinate on the line.
• Thus if the data point lies above the line, d is
  positive, but if the data point lies below the
  line, d is negative.
• As a result, the sum of the d values can be small
  even if the points are widely spread in the
  scatter diagram.
• However, the squares cannot be negative.
• By minimizing the sum of the squares, we are, in
  effect, not allowing positive and negative d
  values to cancel out one another in the sum.
• It is this way that we can meet the least-squares
  critirion of minimizing the sum of the squares of
  the vertical distances between the points and the
  line over all points in the scatter diagram.

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Equation of the Least Squares Line

• y a bx

a the y-intercept
b the slope
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Finding the Equation of the Least Squares Line

• Obtain a random sample of n data pairs (x, y).
• 1. Using the data pairs, compute Sx, Sy, Sx2,
  Sy2, and Sxy.
• Compute the sample means

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Finding the Slope

• 2. Use the following formula
• Finding the y-intercept

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ExampleFind the Least Squares Line
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Example cont.Finding the Slope
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Example cont.Finding the y-intercept
The equation of the least squares line is
y a bx y 2.77 1.70x
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Graph the least-Squares Line

• We can use the slope-intercept method of algebra,
  but may not always be convenient if the intercept
  is not within the range of the sample data
  values.
• It is better to select two x values in the range
  of the x data values and then use the
  least-squares line to compute two corresponding y
  values.
• The point is always on the
  least-squares line.
• To find another point, give x a value and find
  the y.
• In our example (8.3 , 16.9)

Try x 5. Compute y y 2.8 1.7(5) 11.3
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Graphing the least squares line

• Using two values in the range of x, compute two
  corresponding y values.
• Plot these points.
• Join the points with a straight line.

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Sketching the Line
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Meaning of Slope
y a bx

• In the equation , the
  slope b tell us how many units y changes for each
  unit change in x.
• In our example regarding the miles traveled and
  the time in minutes
• y 2.77 1.70x
• The slope 1.70 tell us that a change in one mile
  takes in average 1.70 minutes.
• The slope of the least-squares line tells how
  many units the response variable is expected to
  change for each unit change in the explanatory
  variable. The number of units change in the
  response variable for each unit change in the
  explanatory variable is called marginal change of
  the response variable.

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Using the Equation of the Least Squares Line to
Make Predictions

• Choose a value for x (within the range of x
  values).
• Substitute the selected x in the least squares
  equation.
• Determine corresponding value of y.

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Predict the time to make a trip of 14 miles

• Equation of least squares line
• y 2.8 1.7x
• Substitute x 14
• y 2.8 1.7 (14)
• y 26.6
• According to the least squares equation, a trip
  of 14 miles would take 26.6 minutes.

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Interpolation

• using the least squares line to predict y values
  for x values that are between observed x values
  in the data set.

Extrapolation
using the least squares line to predict y values
for x values that are beyond observed x values in
the data set.
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Extreme Data Points

• The least squares line can be greatly affected by
  extreme or influential data points.

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The least squares line

• Is developed from sample data pairs (x, y).
• May not reflect the relationship between x and y
  for values of x outside the data range.
• For example, there is a fairly high correlation
  between height and age for boys ages 1 year to 10
  years. In general the older the boy, the taller
  the boy. A least-squares line based on such date
  give good predictions of height for ages 1 to 10.
• However, it would be fairly meaningless to use
  the same linear regression line to predict the
  height of 20 to 50 years old.

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The least squares line

• Each different sample data will produce a
  slightly different equation for the least-squares
  line.
• The least-squares line developed with x as the
  explanatory variable and y as the response
  variable can be used only to predict y values
  from specified x values.

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A statistic related to r

• If the sample correlation coefficient is r
• The coefficient of determination r2

How good is the least squares line as an
instrument of regression? The answer is the
coefficient of determination
Coefficient of Determination
Is a measure of the proportion of the variation
in y that is explained by the regression line
using x as the predicting variable
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Interpretation of r2

• If r 0.9753643, then what percent of the
  variation in minutes (y) is explained by the
  linear relationship with x, miles traveled?
• What percent is unexplained?
• If r 0.9753643, then r2 .9513355
• Approximately 95 percent of the variation in
  minutes (y) is explained by the linear
  relationship with x, miles traveled.
• is unexplained (due to the
  random chance or the probability of lurking
  variables that influence y).

Assignments 7, 8 and 9
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Correlation Coefficient r Coefficient of
Determination, r 2 (calc)

• The correlation coefficient, r, and the
  coefficient of determination, r 2 ,will appear
  on the screen that shows the regression equation
  information (be sure the Diagnostics are turned
  on ---2nd Catalog (above 0), arrow down to
  DiagnosticOn, press ENTER twice.)
• In addition to appearing with the regression
  information, the values r and r 2 can be found
  under
• VARS, 5 Statistics ? EQ 7 r and 8 r 2 .

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Linear Regression (calc)

• A linear regression is also know as the "line of
  best fit". 
• Side note  Although commonly used when dealing
  with "sets" of data, the linear regression can
  also be used to simply find the equation of the
  line between two points.Find the equation of the
  line passing through (-1, 1) and (-4,7).Entering
  the information as described in the example
  below, we see the following screens
• The equation is y -2x -1.The correlation
  coefficient is -1 since both point are "on" the
  line and the line slopes negatively

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Linear Regression Model Example (calc)

• Let's examine an example of the linear regression
  as it pertains to a "set" of data. 
• Data  Is there a relationship between Math SAT
  scores and the number of hours spent studying for
  the test?  A study was conducted involving 20
  students as they prepared for and took the Math
  section of the SAT Examination.
• Let x be the Hours Spent Studying and y be Math
  SAT Score
• x y x y x y
• 4 390 22 790 10 690
• 9 580 1 350 11 690
• 10 650 3 400 16 770
• 14 730 8 590 13 700
• 4 410 11 640 13 730
• 7 530 5 450 10 640
• 12 600 6 520

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Linear Regression Model Example cont.

• Task
• a) Determine a linear regression model equation
  to represent this data.  
• b) Graph the new equation.  
• c) Decide whether the new equation is a "good
  fit" to represent this data.  
• d) Interpolate data  If a student studied for
  15 hours, based upon this study, what would be
  the expected Math SAT score?
• e) Interpolate data  If a student obtained a
  Math SAT score of 720, based upon this study, how
  many hours did the student most likely spend
  studying?  
• f) Extrapolate data  If a student spent 100
  hours studying, what would be the expected Math
  SAT score?  Discuss this answer. Any answers in
  relation to this problem are to be rounded to the
  nearest tenth.If rounding is not indicated in a
  problem, leave the full calculator entries as
  answers

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Linear Regression Model Example cont.

• Step 1.  Enter the data into the lists. 
• Step 2.  Create a scatter plot of the data. 
       Go to STATPLOT (2nd Y) and choose the
  first plot.  Turn the plot ON, set the icon to
  Scatter Plot (the first one), set Xlist to L1 and
  Ylist to L2 (assuming that is where you stored
  the data), and select a Mark of your choice. 
• Step 3.  Choose Linear Regression Model.    
  Press STAT, arrow right to CALC, and arrow down
  to 4 LinReg (axb).  Hit ENTER.  When LinReg
  appears on the home screen, type the parameters
  L1, L2, Y1.  The Y1 will put the equation into Y
  for you.        (Y1 comes from VARS ? YVARS,
  Function, Y1)

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Linear Regression Model Example cont.

• Step 4.  Graph the Linear Regression Equation
  from Y1.     ZOOM 9 ZoomStat to see the graph.
  (answer to part b)
• Step 5.  Is this model a "good fit"?     The
  correlation coefficient, r, is .9336055153 which
  places the correlation into the "strong"
  category.  (0.8 or greater is a "strong"
  correlation)     The coefficient of
  determination, r 2, is .8716192582 which means
  that 87 of the total variation in y can be
  explained by the relationship between x and y. 
  The other 13 remains unexplained.     Yes, it
  is a "good fit".          (answer to part c)

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Linear Regression Model Example cont.

• Step 6.  Interpolate  (within the data set)    
   If a student studied for 15 hours, based upon
  this study, what would be the expected Math SAT
  score? From the graph screen, hit TRACE, arrow
  up to obtain the linear equation at the top of
  the screen, type 15, hit ENTER, and the answer
  will appear at the bottom of the
  screen.                                        
  (answer to part d --                       
  Math SAT score of 733.1)

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Linear Regression Model Example cont.

• Step 7.  Interpolate  (within the data
  set)   If a student obtained a Math SAT score of
  720, based upon this study, how many hours did
  the student most likely spend studying?  Go to
  TBLSET (above WINDOW) and set the TblStart to 13
  (since 13 hours gives a score of 700).  Set the
  delta Tbl to a decimal setting of your choice. 
  Go to TABLE and arrow up or down to find your
  desired score of 720, in the Y1 column         
                     
• (answer to part e --  approx. 14.5 hours)

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Linear Regression Model Example cont.

• Step 8.  Extrapolate data  (beyond the data
  set)     If a student spent 100 hours studying,
  what would be the expected Math SAT score?     
  Discuss this answer.                 
• With your linear equation in Y1, go to the home
  screen and type Y1(100).  Press ENTER. (Y1 comes
  from VARS ? YVARS, Function, Y1(100))
• Our equation shows that if a student studies 100
  hours, he/she should score 2885.8 on the Math
  section of the SAT examination.  The only problem
  with this answer is that the highest score that
  can be obtained is 800.  So why is this score so
  outrageous?   ANSWER  When you extrapolate data,
  the further you move away from the data set, the
  less accurate your information becomes.  In this
  problem, the largest number of hours in the data
  set was 22 hours, but the extrapolation tried to
  jump to 100 hours. (answer to part f)

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ExampleLinear Regression with Biological
Data(or the realities of working with real-life
data)

• Pierce (1949) measured the frequency (thenumber
  of wing vibrations per second) of chirps made by
  a ground cricket, at various ground
  temperatures.  Since crickets are ectotherms
  (cold-blooded), the rate of their physiological
  processes and their overall metabolism are
  influenced by temperature.  Consequently, there
  is reason to believe that temperature would have
  a profound effect on aspects of their behavior,
  such as chirp frequency.

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Example cont.

• Chirps/Second Temperature (º F)
• 20.0 88.6
• 16.0 71.6
• 19.8 93.3
• 18.4 84.3
• 17.1 80.6
• 15.5 75.2
• 14.7 69.7
• 17.1 82.0
• 15.4 69.4
• 16.2 83.3
• 15.0 78.6
• 17.2 82.6
• 16.0 80.6
• 17.0 83.5
• 14.1 76.3

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Example cont.

• Task
• Determine a linear regression model equation to
  represent this data  
• Graph the new equation.  
• Decide whether the new equation is a "good fit"
  to represent this data.  
• Extrapolate data  If the ground temperature
  reached 95º, then at what approximate rate would
  you expect the crickets to be chirping?  
• Interpolate data  With a listening device, you
  discovered that on a particular morning the
  crickets were chirping at a rate of 18 chirps per
  second.  What was the approximate ground
  temperature that morning?   
• f) If the ground temperature should drop to
  freezing (32º F), what happens to the
  cricket's chirping?   Answers in this problem are
  to be rounded to the nearest thousandth.

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Example cont.

• Step 1.  Enter the data into the lists. 
• Step 2.  Create a scatter plot of the data. 
       Go to STATPLOT (2nd Y) and choose the
  first plot.  Turn the plot ON, set the icon to
  Scatter Plot (the first one), set Xlist to L1 and
  Ylist to L2 (assuming that is where you stored
  the data), and select a Mark of your
  choice.Obviously, there is some scatter to this
  data. This variability is the norm, rather than
  the exception, when working with biological data
  sets.  Real life data seldom creates a nice
  straight line.
• Step 3.  Choose the Linear Regression Model.    
  Press STAT, arrow right to CALC, and arrow down
  to 4 LinReg (axb).  Hit ENTER.  When LinReg
  appears on the home screen, type the parameters
  L1, L2, Y1.  The Y1 will put the equation in to
  Y for you.             (Y1 comes from VARS ?
  YVARS, Function, Y1)

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Example cont.

• Step 4.  Graph the Linear Regression Equation
  from Y1.     ZOOM 9 ZoomStat to see the graph.
  (answer to part b)
• Step 5.  Is this model a "good fit"?     The
  correlation coefficient, r, is .8364792791 which
  just barely places the correlation into the
  "strong" category.  (0.8 or greater is a "strong"
  correlation)     The coefficient of
  determination, r 2, is .6996975844 which means
  that 70 of the total variation in y can be
  explained by the relationship between x and y. 
  The other 30 remains unexplained.     Yes, it
  is somewhat of a "good fit". (answer to part
  c)

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Example cont.

• Step 6.  Extrapolate  (beyond the data set)    
   If the ground temperature reached 95º, then at
  what approximate rate would you expect the
  crickets to be chirping?Go to TBLSET (above
  WINDOW) and set the TblStart to 20 (since the
  highest temperature in the data set had 19.8
  chirps/second).  Set the delta Tbl to a decimal
  setting of your choice.  Go to TABLE (above
  GRAPH) and arrow up or down to find your desired
  temperature, 95º, in the Y1 column.           
  (answer to part d --  approx. 21.265 chirps per
  second)

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Example cont.

• Step 7.  Interpolate                           
  (within the data set)     With a listening
  device, you discovered that on a particular
  morning the crickets were chirping at a rate of
  18 chirps per second. 
• What was the approximate ground temperature that
  morning?  From the graph screen, hit TRACE,
  arrow up to obtain the power equation, type 47,
  hit ENTER, and the answer will appear at the
  bottom of the screen. (answer to part e --  the
  ground temperature will be approx. 84.407º F)

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Example cont.

• Step 8.  If the ground temperature should drop to
  freezing (32º F), what happens to the cricket's
  chirping?
• The TABLE tells us that at 32º F there are 1.85
  chirps per second.  So, what does this really
  mean?  Are the crickets cold?
• These findings are a bit deceiving.  At 32º F,
  the crickets are dead.  The lifespan of a cricket
  in a cold climate is very short.  The crickets
  spend the winter as eggs laid in the soil.  These
  eggs hatch in late spring or early summer, and
  tiny immature crickets called nymphs emerge. 
  Nymphs develop into adults within approximately
  90 days. The adults mate and lay eggs in late
  summer before succumbing to old age or freezing
  temperatures in the fall.
• Also, remember that the further you extrapolate
  away from the data set, the less reliable the
  information will be.