HammondLA10 - 14.9 1.49 0.01 0.2 14.5 2.9 0.04 0.4 13.9...

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Simple linear regression results: Dependent Variable: Hours slept Independent Variable: Age Hours slept = 14.641539 - 1.1367521 Age Sample size: 10 R (correlation coefficient) = -0.8212 R-sq = 0.6743445 Estimate of error standard deviation: 0.2136176 Parameter estimates: Parameter Estimate Std. Err. DF T-Stat P-Value Intercept 14.641539 0.14268534 8 102.614174 <0.0001 Slope -1.1367521 0.27929237 8 -4.070115 0.0036 Analysis of variance table for regression model: Source DF SS MS F-stat P-value Model 1 0.7559402 0.7559402 16.565836 0.0036 Error 8 0.36505982 0.045632478 Total 9 1.121 Predicted values: X value Pred. Y s.e. (Pred. y) 95% C.I. 95% P.I. 0.3 14.300 513 0.07948 801 (14.117213, 14.483812) (13.774912, 14.826114)
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x y xy x^2 0.1
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Unformatted text preview: 14.9 1.49 0.01 0.2 14.5 2.9 0.04 0.4 13.9 5.56 0.16 0.7 14.1 9.87 0.49 0.6 13.9 8.34 0.36 0.9 13.7 12.33 0.81 0.2 14.3 2.86 0.04 0.6 13.9 8.34 0.36 0.5 14 7 0.25 0.3 14.1 4.23 0.09 4.5 141.3 62.92 2.61 = -1.137x + 14.6415 # 2 Two variables between which I think I can find a strong correlation are age of a persona and the hours spent watching T.V. I would place their age, in years, on the x-axis as the independent variable and the hours on the y-axis as the dependent variable. Based on this placement I predict a strong negative linear correlation reflecting that as they age, the average hours spent watching T.V. will increase....
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This note was uploaded on 02/03/2011 for the course ACCT 1100 taught by Professor Matter during the Spring '08 term at Metropolitan Community College- Omaha.

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HammondLA10 - 14.9 1.49 0.01 0.2 14.5 2.9 0.04 0.4 13.9...

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