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Wednesday 10-10-12

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- Today you need:
- Whiteboard, Marker, Eraser
- Calculator
- 1 page handout

1. Graph the following equation:

1. Graph the following equation:

Linear Regression

Section 2-6

Pages 95-100

- I can use Linear Regression with a calculator to find linear prediction Equations
- I can find the correlation co-efficient “r” for the data

- The correlation co-efficient “r” tells how linear the data is.
- Values of 1 or –1 indicate perfect linear lines, either positive or negative
- Values closer to zero mean the data has no linear relationship
- Small whiteboard number line with r=1 and r=-1

1.0.85

Sample “r Values

-.57.17

- When the data you plot forms a near linear relationship, then we can use a linear equation to approximate the graph.
- We use what’s called a Best-Fit Line. This line is drawn to be as close to the data points as possible, but may not touch them all.

y-axis

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x-axis

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- The calculator is a great resource to give us a prediction equation.
- It is more accurate than doing the equation Manually
- We will enter the data into the STAT mode of the calculator

Turn Diagnostics On. 2nd catalog,

arrow to Diagnostic on, enter, enter

Linear Regressions on the calculator:

(you should clear the calculator before beginning)

2nd, +, 7, 1, 2

#1.

- Finding the equation of your “Best Fit Line”
- STAT, then EDIT
- Enter X-Values in L1, Y-Values in L2
- STAT, then CALC
- Choose (4) LIN REG

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The table below shows the years of experience for eight technicians at Lewis Techomatic and the hourly rate of pay each technician earns.

- y = 1.234x + 5.574
- Remember:
- x = Experience in Years
- y = Pay rate in dollars
- We can use this to predict other values

- Must modify years starting at “0”
- If you don’t you get a really negative y-intercept value that won’t match the graph
- Example on next slide

If the Independent variable is Years and these are your values

1901

1903

1905

1910

1913

1920

Then these are the values we will actually enter for L1

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- Linear Regression Ws