【文章內(nèi)容簡(jiǎn)介】 et’s look at data using a Scatterplot…. ?Go to File… Open Project…. Load the file …. ?Now let’s generate the Scatterplot of the GPA results against our Math and Verbal scores... Scatter plot.... ?Go to: ?Graph… ?Plot... Scatter Plot.... Fill out the screen as follows: ?Select GPA for our Y Variable…. ?Select Math and Verbal for our X Variables….. ?Select OK when done... Scatter plot.... We now have two Scatter plots of the data stacked on top of each other… We can display this better by tiling the graphs…. Scatter plot.... To do this: ?Go to Window… ?Tile... Scatter plot.... Now we can see both Scatter plots of the data… Scatter plot.... ?There is another way we can generate these scatter plots…. ?Go to: ?Graph… ?Matrix Plot... Scatter Plot.... Fill out the screen as follows: ?Click in the “Graph variables” block ?Highlight all three available data sets… ?Click on the “Select” button... ?Select OK when done... Scatter plot.... We now have a series of Scatter plots, each one corresponding to a bination of the data sets available… Note that there appears to be a strong correlation between Verbal and both Math and GPA data…. Minitab Statistical Tools 71 PROCESS CAPABILITY ANALYSIS 72 Let’s do a process capability study…. Open Minitab and load the file …. SETTING UP THE TEST…. Go to Stat… Quality Tools…. Capability Analysis (Weibull)…. Select “Torque” for our single data column... Enter a lower spec of 10 and an upper spec of 30. Then select “OK”…. SETTING UP THE TEST…. Note that the data does not fit the normal curve very well... Note that the Long Term capability (Ppk) is . This equates to a Z value of 3*= standard deviations or sigma values. This equates to an expected defect rate PPM of 147,055. INTERPRETING THE DATA…. HYPOTHESIS TESTING 77 ?Load the file ….. Setting up the test in Minitab Checking the Data for Normality…. ?It’s important that we check for normality of data samples. ?Let’s see how this works…. ?Go to STAT…. Basic Statistics... Normality Test…. Set up the Test ?We will test the “Before” column of data…. ?Check AndersonDarling ?Click OK Analyzing the Results ?Since the P value is greater than .05 we can assume the “Before” data is normal ?Now repeat the test for the “After” Data (this is left to the student as a learning exercise..) Checking for equal variance.. ?We now want to see if we have equal variances in our samples. ?To perform this test, our data must be “stacked”. ?To acplish this go to Manip… Stack… Stack Columns…. ?Select both of the available columns (Before and After) to stack.... ?Type in the location where you want the stacked data…. In this example we will use C4…. ?Type in the location where you want the subscripts stored… In this example we will use C3…. ?Select OK…. Checking for equal variance.. ?Now that we have our data stacked, we are ready to test for equal variances.… ?Go to Stat… ANOVA…. Test for equal Variances... Checking for equal variance.. Setting up the test…. ?Our response will be the actual receipt performance for the two weeks we are paring. In this case we had put the stacked data in column C4…. ?Our factors is the label column we created when we stacked the data (C3).. ?We set our Confidence Level for the test (95%). ?Then select “OK”. ?Here, we see the 95% confidence intervals for the two populations. Since they overlap, we know that we will fail to reject the null hypothesis. ?The F test results are shown here. We can see from the PValue of .263 that again we would fail to reject the null hypothesis. Note that the F test assumes normality ?Note that we get a graphical summary of both sets of data as well as the relevant statistics…. Analyzing the data…. ?Levene’s test also pares the variance of the two samples and is robust to nonnormal data. Again, the PValue of .229 indicates that we would fail to reject the null hypothesis. ?Here we have box plot representations of both populations. Lets test the data with a 2 Sample t Test ?Under Stat… Basic Statistics…. We see several