Simple linear regression results: Dependent Variable: Height (in.) Independent Variable: Shoe Size (in.) Height (in.) = 52.293743 + 1.3679745 Shoe Size (in.) Sample size: 16 R (correlation coefficient) = 0.72170907 R-sq = 0.52086398 Estimate of error standard deviation: 2.6920127 Parameter estimates: Intercept 52.293743 3.4200195 Slope Parameter Estimate Std. Err. AlternativeDF T-Stat P-value +0 1415.290481<0.0001 +0 14 3.901187 0.0016 1.36797450.35065598 Analysis of variance table for regression model: Source DF SS MS F-stat P-value Model 1110.29295110.2929515.21926 0.0016 Error 14101.457057.2469323 Positiu Total 15 211.75 with Fitted line 0.72 Plot Sh in sho Sontee TIses. Close 64in. Shoe. Height (in.) 72 70 68 66 64o 62 60 12 10 Shoe Size (in.) ito

Definition Definition Measure of central tendency that is the average of a given data set. The mean value is evaluated as the quotient of the sum of all observations by the sample size. The mean, in contrast to a median, is affected by extreme values. Very large or very small values can distract the mean from the center of the data. Arithmetic mean: The most common type of mean is the arithmetic mean. It is evaluated using the formula: μ = 1 N ∑ i = 1 N x i Other types of means are the geometric mean, logarithmic mean, and harmonic mean. Geometric mean: The nth root of the product of n observations from a data set is defined as the geometric mean of the set: G = x 1 x 2 ... x n n Logarithmic mean: The difference of the natural logarithms of the two numbers, divided by the difference between the numbers is the logarithmic mean of the two numbers. The logarithmic mean is used particularly in heat transfer and mass transfer. ln x 2 − ln x 1 x 2 − x 1 Harmonic mean: The inverse of the arithmetic mean of the inverses of all the numbers in a data set is the harmonic mean of the data. 1 1 x 1 + 1 x 2 + ...