These instructions accompany *Applied Regression Modeling* by Iain Pardoe, 2nd edition published by Wiley in 2012. The numbered items cross-reference with the "computer help" references in the book. These instructions are based on Data Desk 6 for Windows, but they (or something similar) should also work for other versions. Find instructions for other statistical software packages here.

#### Getting started and summarizing univariate data

- If desired, change Data Desk's default
**options**by selecting`Edit > Preferences`. - To open a Data Desk
**data file**, select`File > Open Datafile`. You can also use`File > Import`to open text data files or use Data Desk/XL, an Excel add-in, to export Excel spreadsheets to Data Desk. - Data Desk does not appear to offer a way to
**edit last dialog**box. **Output**appears in a folder named`Results`.- You can access
**help**by selecting`Help > Data Desk Help`. - To
**transform data**or compute a**new variable**, select the variable you want to transform (denoted*Y*) and select`Manip > Transform`and the required transformation. Examples are`Exponentials > ln(y)`for the natural logarithm of*Y*and`Exponentials > y^2`for*Y*^{2}. Alternatively, select`Manip > Transform > New Derived Variable`, name the new derived variable, click OK, and in the resulting text window type the equation for the new variable (this is particularly useful if the variable is a function of more than one of the existing variables). The new variable should now appear in the same icon window as the original variable, and have an appropriate name, e.g., LY for the natural logarithm of*Y*(check it looks correct by showing the numbers); it can now be used just like any other variable. - To create
**indicator (dummy) variables**from a qualitative variable, select`Manip > Transform > New Derived Variable`. Name the new derived variable, click OK, and in the resulting text window write:`If TextOf(`var') = "cat1" then 1 else 0`, where`var`is the name of the qualitative variable and`cat1`is the category for which you want the indicator variable to have the value 1. Check that the correct indicator variable has been created by showing the numbers. Repeat for other indicator variables (if necessary). - Data Desk does not appear to offer a way to find
**percentiles (critical values)**for t, F, or chi-squared distributions. - Data Desk does not appear to offer a way to find
**tail areas (p-values)**for t, F, or chi-squared distributions. - Calculate
**descriptive statistics**for quantitative variables by selecting the quantitative variable (denoted*Y*) and selecting`Calc > Summaries > Reports`. Use the`HyperView`menu (top-left triangle) to select the summaries, such as the`Mean`, that you would like. - Create
**contingency tables**or**cross-tabulations**for qualitative variables by selecting the first qualitative variable (denoted*Y*, representing the row categories), shift-selecting the second qualitative variable (denoted*X*, representing the column categories), and selecting`Calc > Contingency Tables`. Use the`HyperView`menu to calculate cell percentages (within rows, columns, or the whole table). - If you have quantitative variables and qualitative variables, you can calculate
**descriptive statistics**for cases grouped in different categories by selecting the quantitative variable (denoted*Y*), shift-selecting the qualitative variable (denoted*X*), and selecting`Calc > Summaries > Reports By Groups`. Use the`HyperView`menu to select the summaries, such as the`Mean`, that you would like. If you want to group using two qualitative variables, first create a new variable consisting of all category combinations by selecting the two qualitative variables (one can be*Y*, the other*X*, it does not matter which) and selecting`Manip > Transform > Misc > Concatenate(y,x)`. Then use the new variable as the qualitative variable (*X*) in the previous instructions. - Data Desk does not appear to offer an automatic way to make a
**stem-and-leaf plot**for a quantitative variable. - To make a
**histogram**for a quantitative variable, select the quantitative variable (denoted*Y*) and select`Plot > Histograms`. - To make a
**scatterplot**with two quantitative variables, select the vertical axis quantitative variable (denoted*Y*), shift-select the horizontal-axis quantitative variable (denoted*X*), and select`Plot > Scatterplots`. - All possible scatterplots for more than two variables can be drawn simultaneously (called a
**scatterplot matrix**) by selecting the variables you want plotted (it does not matter which are denoted*Y*or*X*) and selecting`Plot > Plot Matrix`. - You can
**mark or label cases**in a scatterplot with different colors/symbols according to categories in a qualitative variable by selecting the qualitative variable and selecting`Modify > Colors > Add > by Group`or`Modify > Symbols > Add > by Group`. - You can
**identify individual cases**in a scatterplot using labels by opening the variable window containing the labels and selecting the`Query tool`from the tools palette (fourth one down in the right column). You can then click on a point in the scatterplot and the label for that point will be displayed. - To
**remove one of more observations**from a dataset, double-click the response variable in the`Data`folder, highlight the value(s) that you want to remove and select`Edit > Clear`. - To make a
**bar chart**for cases in different categories, select the qualitative variable that represents the different categories and select`Plot > Bar Charts`.- This will produce a frequency bar chart of the qualitative variable. For frequency bar charts of two qualitative variables use a newly created qualitative variable consisting of all category combinations (as in computer help #12).
- Data Desk does not appear to offer an automatic way to have the bars represent summary functions for a quantitative variable, such as the mean.

- To make
**boxplots**for cases in different categories, select the quantitative variable (denoted*Y*), shift-select the qualitative variable (denoted*X*), and select`Plot > Boxplot y by x`. For two qualitative variables, use a newly created qualitative variable consisting of all category combinations (as in computer help #12). - To make a
**QQ-plot**(also known as a**normal probability plot**) for a quantitative variable, select the quantitative variable (denoted*Y*) and select`Plot > Normal Prob Plot`. - To compute a
**confidence interval**for a univariate population mean, select the quantitative variable (denoted*Y*) and select`Calc > Estimate`. In the resulting window, select`t-Interval for Individual μ's`, select`Individual`(rather than`Total`), specify the confidence level for the interval, and click`Show Results`. - To do a
**hypothesis test**for a univariate population mean, select the quantitative variable (denoted*Y*) and select`Calc > Test`. In the resulting window, select`t-Test of Individual μ's`, select`Individual`(rather than`Total`), specify the significance level (`Alpha level`) for the test, type the (null) hypothesized value into the`"Ho:μ="`box, select the alternative hypothesis (`Ha`) to be lower-tail (`"μ<"`), two-tail (`"μ≠"`), or upper-tail (`"μ>"`), and click`Show Results`.

#### Simple linear regression

- To fit a
**simple linear regression model**(i.e., find a least squares line), select the response variable (denoted*Y*), shift-select the predictor variable (denoted*X*), and select`Calc > Regression`. Some of the items in the`HyperView`menu are addressed below. In the rare circumstance that you wish to fit a model without an intercept term (regression through the origin), select`Calc > Calculation Options > Regression Options...`and uncheck`Include constant term`before fitting the model. - To add a
**regression line**or**least squares line**to a scatterplot, select`Add Regression Line`from the scatterplot's`HyperView`menu. - Data Desk does not appear to offer an automatic way to find 95%
**confidence intervals for the regression parameters**in a simple or multiple linear regression model. It is possible to calculate these intervals by hand using Data Desk regression output and appropriate percentiles from a t-distribution. -
- To find a
**fitted value**or**predicted value**of Y (the response variable) at a particular value of X (the predictor variable) in a linear regression model, select`Compute > Predicted`from the regression's`HyperView`menu. The fitted or predicted values of Y at each of the X-values in the dataset are displayed in a new variable named`predicted(*)`, where the star abbreviates the response variable name. - You can also obtain a fitted or predicted value of Y at an X-value that is not in the dataset by doing the following. Before fitting the regression model, add the X-value to the dataset by typing the X-value at the bottom of the column of values for the predictor. Then fit the regression model and follow the steps above. Data Desk will ignore the X-value you typed when fitting the model (since there is no corresponding Y-value), so all the regression output (such as the estimated regression parameters) will be the same. But Data Desk will calculate a fitted or predicted value of Y at this new X-value based on the results of the regression. Again, look for it in the new variable named
`predicted(*)`. *This applies more generally to multiple linear regression also.*

- To find a
- Data Desk does not appear to offer an automatic way to find a
**confidence interval for the mean of Y**at a particular value of X in a simple linear regression model. It is possible to calculate such an interval by hand using Data Desk regression output and an appropriate percentile from a t-distribution.*This applies more generally to multiple linear regression also.* - Data Desk does not appear to offer an automatic way to find a
**prediction interval for an individual Y-value**at a particular X-value in a simple linear regression model. It is possible to calculate such an interval by hand using Data Desk regression output and an appropriate percentile from a t-distribution.*This applies more generally to multiple linear regression also.*

#### Multiple linear regression

- To fit a
**multiple linear regression model**, select the response variable (denoted*Y*), shift-select the predictor variables (denoted*X*), and select`Calc > Regression`. Some of the items in the`HyperView`menu are addressed below. In the rare circumstance that you wish to fit a model without an intercept term (regression through the origin), select`Calc > Calculation Options > Regression Options...`and uncheck`Include constant term`before fitting the model. - Data Desk does not appear to offer an automatic way to to add a
**quadratic regression line**to a scatterplot. - Categories of a qualitative variable can be thought of as defining
**subsets**of the sample. If there are also a quantitative response and a quantitative predictor variable in the dataset, a regression model can be fit to the data to represent separate regression lines for each subset. First use computer help #15 and #17 to make a scatterplot with the response variable on the vertical axis, the quantitative predictor variable on the horizontal axis, and the cases marked with different colors according to the categories in the qualitative predictor variable. To add a**regression line for each subset**to this scatterplot, select`Add Color Regression Lines`from the`HyperView`menu. - Data Desk does not appear to offer an automatic way to to find the F-statistic and associated p-value for a
**nested model F-test**in multiple linear regression. It is possible to calculate these quantities by hand using Data Desk regression output and appropriate percentiles from a F-distribution. - To save
**residuals**in a multiple linear regression model, select`Compute > Residuals`from the regression's`HyperView`menu. The residuals are saved as a variable called`residuals(*)`, where the star abbreviates the response variable name; they can now be used just like any other variable, for example, to construct residual plots. To save what Pardoe (2012) calls**standardized residuals**, select`Compute > IStudRes`—they will be saved as a variable called`IStudRes(*)`. To save what Pardoe (2012) calls**studentized residuals**, select`Compute > EStudRes`—they will be saved as a variable called`EStudRes(*)`. - To add a
**loess fitted line**to a scatterplot (useful for checking the zero mean regression assumption in a residual plot), select`Smoothing > Add Lowess Smooth`from the scatterplot's`HyperView`menu. Select`Smoothing > Smoothing Options`to change the value of the`Lowess Span %`; you can experiment to find a value that captures the major trends in the scatterplot without being overly "wiggly." - To save
**leverages**in a multiple linear regression model, select`Compute > Leverages`from the regression's`HyperView`menu. The leverages are saved as a variable called`leverages(*)`, where the star abbreviates the response variable name; they can now be used just like any other variable, for example, to construct scatterplots. - To save
**Cook's distances**in a multiple linear regression model, select`Compute > Cook`from the regression's`HyperView`menu. The Cook's distances are saved as a variable called`Cook(*)`, where the star abbreviates the response variable name; they can now be used just like any other variable, for example, to construct scatterplots. - To create a
**residual plot**automatically in a multiple linear regression model, select`Scatterplot studentized residual vs predicted`from the regression's`HyperView`menu. This will create a scatterplot of the studentized residuals on the vertical axis versus the predicted values on the horizontal axis. To create residual plots manually, first create studentized residuals (see computer help #35), and then construct scatterplots with these studentized residuals on the vertical axis. - To create a
**correlation matrix**of quantitative variables (useful for checking potential**multicollinearity**problems), select the variables (it does not matter which are denoted*Y*or*X*) and select`Calc > Correlations > Pearson Product-Moment`. - Data Desk does not appear to offer an automatic way to to find
**variance inflation factors**in multiple linear regression. - To draw a
**predictor effect plot**for graphically displaying the effects of transformed quantitative predictors and/or interactions between quantitative and qualitative predictors in multiple linear regression, first create a variable representing the effect, say, "X1effect" (see computer help #6). Then select the "X1effect" variable (denoted*Y*), shift-select the X1 variable (denoted*X*), and select`Plot > Scatterplots`.- If the "X1effect" variable just involves X1 (e.g., 1 + 3X1 + 4X1
^{2}), the resulting plot should be fine, albeit the effect will be represented by points rather than a line (as in Section 5.5 in Pardoe 2012). If you would prefer a line, select`Add Regression Line`from the scatterplot's`HyperView`menu (as in computer help #26). - If the "X1effect" variable also involves a qualitative variable (e.g., 1 − 2X1 + 3D2X1, where D2 is an indicator variable), you should then select the qualitative variable and select
`Modify > Colors > Add > by Group`(as in computer help #17) and finally select`Add Color Regression Lines`from the scatterplot's`HyperView`menu (as in computer help #33).

See Section 5.5 in Pardoe (2012) for an example.

- If the "X1effect" variable just involves X1 (e.g., 1 + 3X1 + 4X1