AdvAlg3 6FittingALineToData.notebook. February 20, Correlation Coefficient. Window. Regression line. Linear Regression Model.
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1 Correlation Coefficient Window Regression line Linear Regression Model Sep 28 7:07 AM 1
2 1. If everyone in class tried to fit a line to a set of data points by hand would we all come up with the same equation? NO! Fitting a line to data points is a technique used to get a very good estimate of a line that can be used to describe the relationship in the data. 2. What is a scatterplot A graph of all the ordered pairs in a set of data. Typically these points are not connected. It is a discrete graph. Feb 9 2:56 PM 2
3 AA36.pdf Sep 29 6:49 PM 3
4 p 2500 = 10(t 2000) p 2500 = 10t 20, p = 10t 17,500 p(t) = 10t 17,500 Feb 9 3:01 PM 4
5 A linear model that can be used to estimate the relation in the data. p(t) = 10t 17,500 so p(2025) = 10(2025) 17,500 = 2,750 thousand people = 2,750,000 people p(t) = 10.4t 18,274 p(t) = 10.4t 18,274 so p(2025) = 10.4(2025) 18,274 = 2,786 thousand people = 2,786,000 people Feb 9 3:01 PM 5
6 The correlation coefficient, r, is a number generated by the line of best fit technique. It is used to give an indication of how good the line fits the particular data. Its values are all real numbers 1 to 1 inclusively. Negative numbers indicate a negative slope and a negative correlation. Positive numbers indicate a positive slope and a positive correlation. Numbers closer to 1 and 1 indicate a line is a very good fit. Numbers closer to zero indicate a line is a very poor fit. Interpretation of the correlation coefficient An r value of.8 would indicate a strong positive correlation between the independent variable and the dependent variable. The blue text must be replaced with a specific problems actual information with what the variables represent used in place of the independent and dependent variable. Feb 9 3:02 PM 6
7 There will be a perfect linear relation between the variables and the r value will be equal to 1 or 1 Feb 9 3:15 PM 7
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13 An r value of.998 would indicate a very strong positive correlation between the age and the weight. Feb 5 7:16 AM 13
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23 Lesson 3 Linear Regression By Hand.docx Sep 30 8:56 AM 23
24 Yes It seems that as the shoe length increases the height increases D: 11.4, 11.6, 11.8, 12.2, 12.6, 12.8 R: 65, 67, 68, 69, 70, 71, 72, 74 (L, H) no there are shoe lengths that repeat. (H, L) no there are heights that repeat. Sep 30 9:17 AM 24
25 on the line (12.2, 70) and (11.4, 65) 6.25 For each increase of one inch in shoe length there is a increase in height of 6.25 inches (H 70)=6.25(L 12.2) H=6.25L 6.25 A shoe length of zero inches indicates a height of 6.25 inches but this clearly does not make any sense. H(11.8)=6.25(11.8) 6.25=67.5 inches tall This is an estimate for the height of a man with shoe length of 11.8 inches Sep 30 9:20 AM 25
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27 Modeling two variable with a line Homework.docx Sep 30 8:57 AM 27
28 PINK Sheets Oct 1 9:44 AM 28
29 PINK Sheets Oct 5 6:29 PM 29
30 Regression on the TI.pdf TI NSpire Oct 29 8:52 PM 30
31 TI 84 Oct 29 8:55 PM 31
32 Lesson 4 Introducing Linear Regression with the Graphing Calculator.docx Sep 30 8:58 AM 32
33 TAN Sheet Later direction 1 1 strength Sep 30 9:27 AM 33
34 Fat Calories Fat Calories Sep 30 9:27 AM 34
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37 Linear Regression with Calculator Lets Try Key.pdf TAN Sheets Oct 2 9:30 AM 37
38 TAN Sheets Sep 30 9:28 AM 38
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42 WS 3 6 to 3 8 B.pdf Feb 8 7:05 PM 42
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45 WS 3 6 to 3 8 B KEY.pdf Sep 29 5:11 PM 45
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47 Attachments Regression on the TI.pdf WS 3 6 to 3 8 B.pdf WS 3 6 to 3 8 B KEY.pdf AA36.pdf Lesson 3 Linear Regression By Hand.docx Modeling two variable with a line Homework.docx Lesson 4 Introducing Linear Regression with the Graphing Calculator.docx Linear Regression with Calculator Lets Try Key.pdf
AdvAlg5.3SolvingSystemsBySubstitution.notebook February 26, 2018
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