6. * Obviously, the pattern of the points in the sample does not match the pattern of the population.

7. * r, the correlation coefficient of the sample doesn’t equal  the correlation coefficient of the population

8. * Question: If our sample is clustered in an ellipse and looks fairly linear, does it come from a population with a similar ellipse or not? * Question: Is our r a good estimate of  ?

13. * Conditions for Inference on Regression * The true relationship between x and y is linear * Check the scatter plot and residual plot * For any value of x the values of y are independent * Random sample * For any value of x the y-values are normally distributed * Check a histogram or boxplot of the residuals * The standard deviation of the y values is constant * Check the scatter plot and residual plot

19. * Confidence Interval * statistic ± (crit. val.)(std. dev. of stat.) * b 1 ± t*(Std. dev. of stat.) or (std. error)

20. * 1. In a study of the performance of a computer printer, the size (in kilobytes) and the printing time (in seconds) for each of 22 small text files were recorded. A regression line was a satisfactory description of the relationship between size and printing time. The results of the regression analysis are shown below. Dependent variable: Printing Time SourceSum of SquaresdfMean SquareF-ratio Regression 53.33151 53.3315140 Residual 7.6238120 0.38115 VariableCoefficients.e. of coefft-ratioprob Constant 11.6559 0.3153 37 <0.0001 Size 3.47812 0.29411.8 <0.001 Rsquared = 87.5%Rsquared(adjusted) = 86.9% s=0.6174 with 22-2 = 20 degrees of freedom 95% Confidence Interval

21. Dependent variable: Printing Time SourceSum of Squares dfMean SquareF-ratio Regression 53.3315 1 53.3315140 Residual 7.62381 20 0.38115 VariableCoefficients.e. of coefft-ratio prob Constant 11.6559 0.3153 37 <0.0001 Size 3.47812 0.294 11.8 <0.001 s=0.6174 with 22-2 = 20 degrees of freedom Rsquared = 87.5% Rsquared(adjusted) = 86.9%

24. * 1. In a study of the performance of a computer printer, the size (in kilobytes) and the printing time (in seconds) for each of 22 small text files were recorded. A regression line was a satisfactory description of the relationship between size and printing time. The results of the regression analysis are shown below. Dependent variable: Printing Time SourceSum of SquaresdfMean SquareF-ratio Regression 53.33151 53.3315140 Residual 7.6238120 0.38115 VariableCoefficients.e. of coefft-ratioprob Constant 11.6559 0.3153 37 <0.0001 Size 3.47812 0.29411.8 <0.001 Rsquared = 87.5%Rsquared(adjusted) = 86.9% s=0.6174 with 22-2 = 20 degrees of freedom Sufficient Evidence of a linear relationship?