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Wednesday 10-10-12. Today you need: Whiteboard, Marker, Eraser Calculator 1 page handout. Warm-up Need a White Board. 1. Graph the following equation:. Warm-up Need a White Board. 1. Graph the following equation:. Linear Regression. Section 2-6 Pages 95-100. Objectives.

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## Wednesday 10-10-12

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**Wednesday 10-10-12**• Today you need: • Whiteboard, Marker, Eraser • Calculator • 1 page handout**Warm-upNeed a White Board**1. Graph the following equation:**Warm-upNeed a White Board**1. Graph the following equation:**Linear Regression**Section 2-6 Pages 95-100**Objectives**• I can use Linear Regression with a calculator to find linear prediction Equations • I can find the correlation co-efficient “r” for the data**Correlation Co-efficient**• 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**Plotting Data**• 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**45 40 35 30 25 20 15 10 5 x-axis 0 1 2 3 4 5 6 7 8 9 10**Using the Calculator (Linear Regression)**• 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.**Linear Regression**• 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**y-axis**45 40 35 30 25 20 15 10 5 x-axis 0 1 2 3 4 5 6 7 8 9 10**The table below shows the years of experience for eight**technicians at Lewis Techomatic and the hourly rate of pay each technician earns.**Prediction Equations**• y = 1.234x + 5.574 • Remember: • x = Experience in Years • y = Pay rate in dollars • We can use this to predict other values**When Dealing with Years**• 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 0 2 4 9 12 19 Inputting Years**Homework**• Linear Regression Ws

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