Enter the appropriate frequencies for each contingency table cell in Column 3.Ĭhoose Analyze from the top row of the JMP spreadsheet, select Fit Y by X.įrom the Select Columns box click on the variable in Column 1 and then and click Y, Response. With a JMP Data Table open, enter the categories appearing in the rows of the contingency table into Column 1 and the categories appearing in the columns of the contingency table into Column 2, repeating categories to include all intersections of rows and columns from the contingency table. Select Run.Ĭhi-Square Distribution Test for Association Highlight the names of your data in Column 2 and Column 3 at the same time in the Select Columns box.
Label the columns as fitting.Ĭlick on the name of your data in Column 1 in the Select Columns box and click Y. Repeat this step to add a third column.Įnter the row names into Column 2 and the column numbers into Column 3. In the New Column window that opens, click the dropdown beside Modeling Type. Note: You can adjust the α level by clicking the red triangle next to Oneway Analysis of… and then select Set α Level. Click OK.įrom the Oneway Analysis output, click on the red triangle next to Oneway Analysis of…, then click on Compare Means and select All Pairs, Tukey HSD. Click on your response and then click on Y, Response. Select Analyze in the top row of the JMP spreadsheet and then select Fit Y by X.įrom the Select Columns box, click on your treatments, then click on X, Factor. In the window that opens, click on the Red Triangle and select Means/Anova.Įnter the data for the treatments in one column of an open JMP data table and a column for the response variable in another column and label the columns accordingly. Click on the name of your data in Column 2 in the Select Columns box and click X, Factor. Label the columns as fitting.Ĭlick on the name of your data in Column 1 in the Select Columns box and click Y, Response. Select OK.Įnter the groups into Column 2 for each data value. Specifically I'm dealing with the Kaggle Titanic dataset.Enter all of the data into Column 1, one column at a time. I've plotted a stacked histogram which shows ages that survived and died upon the titanic. I would like to alter the chart to show a single chart per bin of the percentage in that age group that survived. if a bin contained the ages between 10-20 years of age and 60% of people aboard the titanic in that age group survived, then the height would line up 60% along the y-axis.Įdit: I may have given a poor explanation to what I'm looking for. Rather than alter the y-axis values, I'm looking to change the actual shape of the bars based on the percentage that survived. For non-adjacent bars, hold down the Ctrl key, click the non-adjacent bars, and then double-click one bar. The first bin on the graph shows roughly 65% survived in that age group. To select or deselect a range of adjacent rows or columns, hold down the Shift key and click the first and last row or column in the range.įor adjacent bars, hold down the Shift key and double-click the right-most bar. I would like this bin to line up against the y-axis at 65%. # Creating the plot that will show survival % per age group and genderĪx = survival_per_age_ot(kind='bar', color='green')Īx.set_title("Survivors by Age Group", fontsize=14, fontweight='bold') Then you can plot your graph as follows: survival_per_age_group = oupby('AgeGroup').mean() The graph would end up actually looking something like this:įirst of all it would be better if you create a function that splits your data in age groups # This function splits our data frame in predifined age groups The following bins look to be 90%, 50%, 10% respectively, and so on. Right click on the columns that are in the graph. # Importing the relevant fuction to format the y axisįrom matplotlib.ticker import FuncFormatterĪx.t_major_formatter(FuncFormatter(lambda y, _: ''. Change the width from 10 to whatever specified length you want. So for example if you change the column width to 3. Column WHA2F14 will read 3.1 in the bar chart. Note: This method works only for continuous (interval) variables.
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