Column three now contains the set of differences, which we assume are random drawing from a Normal distribution.
Type the rubber data into c1 and c2 and let c3 = c2 - c1 in the session window. Repeat the whole procedure now for small sample sizes. Draw a histogram, boxplot and dotplot of c2. To see what kind of probability plots can be expected from skewed data generate 100 data points from a Normal Distribution into c1, then let c2 equal c1 squared: to do this, select Edit->Command Line Editor and type let c2 = c1 * c1. Repeat this whole process a few times to see the kind of variability that can be expected from Normal Probability plots based on samples of 100. Use Scatterplot from the Graphs menu to obtain a Normal probability plot. This calculates Normal scores for the data, which are then used to obtain Normal probability plots. Select Edit->Command Line Editor and type in the command nscores c1 c2. Examine the data you have generated using a Histogram, a Dotplot and a Boxplot and obtain summary statistics ( Stat->Basic Statistics ->Descriptive Statistics). Open Minitab and use Calc->Random Data->Normal to generate 100 observations from a Normal Distribution with mean (\(\mu \) = 0) and standard deviation (\(\sigma \) = 1). We will first explore Normal probability plots using randomly generated data and then use these plots as a means of assessing the assumption of Normality made when we carry out t-tests and construct confidence intervals for process averages, based on small sample sizes. In this laboratory session, we are going to use Minitab to analyze some experimental data.
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