Given the following linear regression line (assume σ = 0.025): y = 0.095 x -0.12 Follow the steps below to compute the probability that the observed value of flow rate will exceed 0.845 m³/minute for a pressure drop of 10 inches. Flow rate, y Regression line (rough) line. 0 S 10 + + 15 20 Pressure drop, x Compute the expected flow rate for a pressure drop of 10 inches based on the regression Compute the relevant test statistic for the problem. Compute the probability that the observed value of flow rate will exceed 0.845 m³/minute for a pressure drop of 10 inches. Edit View Insert Format Tools Table 12pt v Paragraph BIUA 12v

Measurements of the electrical resistivity of tungsten at specified temperatures are presented in the table below. The error in the determination of the temperature was negligible. The temperatures are stated as absolute temperatures in °K. The units of resistivity are u Ncm . Carry out a regression analysis for resistivity as a function of temperature. Temperature Resistivity 300 5.54 Include a scatterplot showing the residuals vs temperature. 400 7.95 Do the regression assumptions appear to be valid? 500 11.00 600 12.28 Include a scatterplot showing the raw data points as black circles as well as a continuous blue line representing the regression model. 700 15.95 800 18.46 Find the lower and upper limits for a 99% confidence interval for the slope of the regression model. Add a pair of red lines to the scatterplot: both passing through the centroid of the data, one having the slope corresponding to the lower bound of the confidence interval, and the other having the slope…

When examining a plot of the residuals produced by the regression model, which of the following statements are true The residuals should be both positive and negative values. They should expand outward, producing a conical shape as your predicted y value increases in size. The residuals should show a clear positive relationship. Low values of your independent variable should produce negative residuals, while high values of your independent variable should produce positive residuals. The residuals should produce a clear u-shaped patter. All values should be positive. The residuals should appear to be random, with a horizontal band around the x- axis. They should be both positive and negative values.

Correlations Pearson Correlation 444 Sig. (2-tailed) .199 10 10 y Pearson Correlation 444 1 Sig. (2-tailed) .199 N 10 10 Descriptive Statistics Mean Std. Deviation 10 83.40 9.009 10 46.70 18.185 Valid N (listwise) 10