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Coefficient of Determination Section 3.2C
22

Section 3.2C. The regression line can be found using the calculator Put the data in L1 and L2. Press Stat – Calc - #8 (or 4) - enter To get the correlation.

Jan 05, 2016

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Page 1: Section 3.2C. The regression line can be found using the calculator Put the data in L1 and L2. Press Stat – Calc - #8 (or 4) - enter To get the correlation.

Coefficient of Determination

Section 3.2C

Page 2: Section 3.2C. The regression line can be found using the calculator Put the data in L1 and L2. Press Stat – Calc - #8 (or 4) - enter To get the correlation.

The regression line can be found using the calculator Put the data in L1 and L2.Press Stat – Calc - #8 (or 4) - enter

To get the correlation coefficient and coefficient of determination to show…Press 2nd catalog (0)Press DGo to Diagnostic on – press enter until you see

“done”

Using the Calculator

Page 3: Section 3.2C. The regression line can be found using the calculator Put the data in L1 and L2. Press Stat – Calc - #8 (or 4) - enter To get the correlation.

The following table lists the total weight lifted by the winners in eight weight classes of the 1996 Women’s National Weightlifting ChampionshipWeight Class (kg)

Total Lifted (kg)

46 14050 127.554 167.564 167.570 192.576 18583 200

1. Find LSRL

2. Find the correlation coefficient.

3. Find the residual for a 64 kg weight class.

4. Check out the residual plot.

Page 4: Section 3.2C. The regression line can be found using the calculator Put the data in L1 and L2. Press Stat – Calc - #8 (or 4) - enter To get the correlation.

If a line is appropriate, then we need to assess the accuracy of predictions based on the least squares line.

Page 5: Section 3.2C. The regression line can be found using the calculator Put the data in L1 and L2. Press Stat – Calc - #8 (or 4) - enter To get the correlation.

Coefficient of Determination

It’s the measure of the proportion of variability in the variable that can be “explained” by a linear relationship between the variables x and y.

Page 6: Section 3.2C. The regression line can be found using the calculator Put the data in L1 and L2. Press Stat – Calc - #8 (or 4) - enter To get the correlation.

Example# miles Cost

25 32.5

61 43.3

200 85

340 127

125 62.5

89 51.7

93 52.9

Rental Cost 25 0.3(Miles)

This relationship explains 100% of the variation in Cost.

Page 7: Section 3.2C. The regression line can be found using the calculator Put the data in L1 and L2. Press Stat – Calc - #8 (or 4) - enter To get the correlation.

But the line doesn’t always account for all of the variability.

Height Shoe Size

65 9

62 8.5

67 10

72 12

74 13

67 9.5

69 12

70 10

65 9

Shoe 16.03 .39 height

This doesn’t!

Page 8: Section 3.2C. The regression line can be found using the calculator Put the data in L1 and L2. Press Stat – Calc - #8 (or 4) - enter To get the correlation.

Total Sum of SquaresMeasures the total variation in the y-values.It’s the sum of squares of vertical distances

𝑺𝑺𝑻=∑ (𝒚 − 𝒚 )𝟐

Page 9: Section 3.2C. The regression line can be found using the calculator Put the data in L1 and L2. Press Stat – Calc - #8 (or 4) - enter To get the correlation.

Find the SST:

Height Shoe Size

65 9

62 8.5

67 10

72 12

74 13

67 9.5

69 12

70 10

65 9

Page 10: Section 3.2C. The regression line can be found using the calculator Put the data in L1 and L2. Press Stat – Calc - #8 (or 4) - enter To get the correlation.

Find the SST:

Height Shoe Size

65 9 1.7778

62 8.5 3.3611

67 10 .11111

72 12 2.7778

74 13 7.1111

67 9.5 .69444

69 12 2.7778

70 10 .11111

65 9 1.7778

𝑆𝑆𝑇=20.5

Page 11: Section 3.2C. The regression line can be found using the calculator Put the data in L1 and L2. Press Stat – Calc - #8 (or 4) - enter To get the correlation.

Sum of Squared Errors

This is the sum of the squared residuals

Total of the unexplained error

Formula:

Page 12: Section 3.2C. The regression line can be found using the calculator Put the data in L1 and L2. Press Stat – Calc - #8 (or 4) - enter To get the correlation.

Find the SSE:

Height Shoe Size

65 9 1.7778

62 8.5 3.3611

67 10 .11111

72 12 2.7778

74 13 7.1111

67 9.5 .69444

69 12 2.7778

70 10 .11111

65 9 1.7778

Page 13: Section 3.2C. The regression line can be found using the calculator Put the data in L1 and L2. Press Stat – Calc - #8 (or 4) - enter To get the correlation.

Find the SSE:

Height Shoe Size

65 9 1.7778 0.04478

62 8.5 3.3611 0.20543

67 10 .11111 1.4E-4

72 12 2.7778 0.00495

74 13 7.1111 0.08632

67 9.5 .69444 0.23833

69 12 2.7778 1.5258

70 10 .11111 1.3295

65 9 1.7778 0.04478

𝑆𝑆𝐸=3.48

Page 14: Section 3.2C. The regression line can be found using the calculator Put the data in L1 and L2. Press Stat – Calc - #8 (or 4) - enter To get the correlation.

Percent of unexplained error:

Page 15: Section 3.2C. The regression line can be found using the calculator Put the data in L1 and L2. Press Stat – Calc - #8 (or 4) - enter To get the correlation.

Coefficient of DeterminationIt’s the percent of variation in the y-

variable (response) that can be explained by the least-squares regression line of y on x.

Formula:

Page 16: Section 3.2C. The regression line can be found using the calculator Put the data in L1 and L2. Press Stat – Calc - #8 (or 4) - enter To get the correlation.

For height and shoe size – find and interpret the coefficient of determination.

𝑟2=1−𝑆𝑆𝐸𝑆𝑆𝑇

Page 17: Section 3.2C. The regression line can be found using the calculator Put the data in L1 and L2. Press Stat – Calc - #8 (or 4) - enter To get the correlation.

For height and shoe size – find and interpret the coefficient of determination.

Approximately 83% of the variation in shoe size can be explained by height.

Page 18: Section 3.2C. The regression line can be found using the calculator Put the data in L1 and L2. Press Stat – Calc - #8 (or 4) - enter To get the correlation.

Find the Coefficient of Determination:

Team Batting Avg.

Mean # runs per

game

0.289 5.9

0.279 5.5

0.277 4.9

0.274 5.2

0.271 4.9

0.271 5.4

0.268 4.5

0.268 4.6

0.266 5.1

Page 19: Section 3.2C. The regression line can be found using the calculator Put the data in L1 and L2. Press Stat – Calc - #8 (or 4) - enter To get the correlation.

Interpret this in context…

59.5% of the observed variability in mean number of runs per game can be explained by an approximate linear relationship between Team Batting average and mean runs per game.

Page 20: Section 3.2C. The regression line can be found using the calculator Put the data in L1 and L2. Press Stat – Calc - #8 (or 4) - enter To get the correlation.

Another example:

If r = 0.8, then what % can be explained by the least squares regression line?

Page 21: Section 3.2C. The regression line can be found using the calculator Put the data in L1 and L2. Press Stat – Calc - #8 (or 4) - enter To get the correlation.

Another example:A recent study discovered that the correlation between the age at which an infant first speaks and the child’s score on an IQ test given upon entering school is -0.68. A scatterplot of the data shows a linear form. Which of the following statements about this is true?A. Infants who speak at very early ages will have higher IQ scores by the beginning of elementary school than those who begin to speak later.B. 68% of the variation in IQ test scores is explained by the least- squares regression of age at first spoken word and IQ score.C. Encouraging infants to speak before they are ready can have a detrimental effect later in life, as evidenced by their lower IQ scores.D. There is a moderately strong, negative linear relationship between age at first spoken word and later IQ test score for the individuals this study.

Page 22: Section 3.2C. The regression line can be found using the calculator Put the data in L1 and L2. Press Stat – Calc - #8 (or 4) - enter To get the correlation.

HomeworkPage 192 (49, 51, 54, 56, 58, 71-78)