SPSS INSTRUCTION CHAPTER 9

Size: px
Start display at page:

Download "SPSS INSTRUCTION CHAPTER 9"

Transcription

1 SPSS INSTRUCTION CHAPTER 9 Chapter 9 does no more than introduce the repeated-measures ANOVA, the MANOVA, and the ANCOVA, and discriminant analysis. But, you can likely envision how complicated it can be to obtain calculated values for these tests. Calculations for any of these tests may cause anxiety for those uncomfortable with math. So, the possibility of needing to combine these operations for tests such as a repeated-measures ANCOVA or a multiple discriminant analysis may seem utterly overwhelming! Luckily, SPSS provides an option for those who wish to avoid the time-consuming and labor-intensive calculations. Each of the following sections provides instructions for using SPSS to perform its respective test as well as for interpreting the test s output. Repeated-Measures ANOVA with SPSS Your first order of business when conducting a repeated-measures ANOVA in SPSS is to organize your data correctly on the Data View screen. Unlike the setup for a betweensubjects ANOVA, you cannot use dummy variables to distinguish between groups for a repeated measures ANOVA. Dummy variables do not indicate the associations between subjects. To specify these associations for a repeated-measures ANOVA, you must assign a row to each group. The procedure to enter data for the paired-subjects t-test can serve as a guide. The arrangement of data for a repeated-measures ANOVA differs only in that it uses more than two rows because it compares more than two categories. Example SPSS Data View Screen for Repeated Measures ANOVA A partial display of the imaginary data used to create the tables in Example 9.10 shows three separate rows, each pertaining to one of the conditions in which subjects complete crossword puzzles. TABLE 9.9 SPSS REPEATED MEASURES ANOVA DATA ARRANGEMENT Placing data points from the samples side by side in the SPSS data view screen indicates the links between scores. The user inserts column headings, which describe the conditions for each sample, in the variable view screen.

2 For this study, the same people complete a puzzle from a newspaper, a puzzle from a magazine, and a puzzle from a crossword puzzle book. Each row contains the times that it took a particular subject to complete the puzzles in the three conditions. Reading across each row, SPSS knows that the values pertain to the same subjects or to subjects who have some connection with each other. The program looks for this association when you instruct it to perform a repeated measures ANOVA. SPSS regards this association as an additional factor in the analysis. Even a oneway repeated-measures ANOVA requires attention to this additional factor. As a result, you must use SPSS s General Linear Model function to perform the test. This function, also used for the multi-way ANOVA described in Chapter 7, suits situations involving a comparison of at least three groups that have relationships among themselves or with other variables. Its wide applicability makes it appropriate for some of the other tests described in Chapter 9 as well. SPSS, however, requires more input for the repeated-measures ANOVA than Chapter 7 s multi-way ANOVA. The necessary steps for a one-way repeated measures ANOVA are as follows. 1. Choose the General Linear Model option in SPSS Analyze pull-down menu. 2. Choose Repeated Measures from the prompts given. A window entitled Repeated Measures Define Factor(s) should appear. FIGURE 9.6 SPSS REPEATED MEASURES DEFINE FACTORS WINDOW

3 The user inputs preliminary information about the repeated measures test in this box. For a one-way repeated-measures ANOVA, attention should focus upon the top half of the window. The Within-Subject Factor Name refers to a term that describes the condition that distinguishs between groups. The Number of Levels refers to the number of groups involved in the comparison. 3. You have two main tasks in the Repeated Measures Define Factor(s) window. a. SPSS asks you to create a name that describes the overall comparison factor. This name, commonly terms such as condition, or time, distinguishes between the sets of data that you wish to compare. You should type this term into the box marked Within-Subject Factor Name at the top of the window. b. The number that you type into the Number of Levels box tells SPSS how many data sets you wish to compare. Your analysis can include all of your data sets or only some of them. In the next window, you can specify which data sets to include in the analysis. 4. Click Define. A window entitled Repeated Measures appears. FIGURE 9.7 SPSS REPEATED MEASURES WINDOW The Within-Subjects Variables box contains spaces for the number of variables specified as the Number of Levels when defining factors. This particular window would suit a comparison of four means, hence the four spaces in the Within-Subjects Variables box. 5. A box on the left side of the window contains the names of all variables for which you have entered data. For each variable that you would like to include in the analysis, click on its name and, then, on the arrow pointing to the Within-Subjects Variables box. Doing so should move the variable name.

4 6. To include descriptive statistics for the groups in the output, click on the window s Options button. a. Move the name of the analysis within subjects factor to the box labeled Display Means for box. b. Mark Descriptive Statistics in Display box. c. Click Continue to return to the Repeated Measures box. 7. Click OK. These steps create many output tables. Not all of these tables provide new information or values that help you to determine whether category means differ significantly. The one of primary interest to you should be the Tests of Within-Subjects Effects Table, which contains significance values for the repeated-measures ANOVA, itself. In the upper portion of this table, labeled with the independent variable s name, four F and four p values appear. These values are usually the same or almost the same. However, for a standard repeatedmeasures ANOVA the row labeled Sphericity Assumed provides the F and p values that you need. If you requested that SPSS provide you with descriptive statistics (Step #6 in the process) output also includes a table entitled Descriptive Statistics. Values in this table become especially useful when you reject the null hypothesis. In this situation, you must refer to the descriptive statitics to determine what category or categories means differ from the other(s) and the direction of the difference. This information can also help you determine how to begin post-hoc tests. Example 9.16 Selected SPSS Output for One-Way Repeated Measures ANOVA A oneway analysis performed using an expansion of the mock data set shown in Table 9.9 produces the following descriptive statistics and within-subject effects values. Descriptive Statistics Mean Std. Deviation N Newspaper Magazine Book Measure:MEASURE_1 Tests of Within-Subjects Effects

5 Type III Sum of Source Squares Df Mean Square F Sig. Publication Sphericity Assumed Greenhouse-Geisser Huynh-Feldt Lower-bound Error(publication) Sphericity Assumed Greenhouse-Geisser Huynh-Feldt Lower-bound AND TABLE 9.11 SELECTED SPSS OUTPUT FOR ONEWAY REPEATED-MEASURES ANOVA According to the means listed in the Descriptive Statistics Table (Table 9.10), subjects spent almost the same amount of time completing crossword puzzles from newspapers and from magazines. Puzzles from crossword puzzle books, however, took more time than did those from either or the other two publications. The Sphericity Assumed Row in the section of Table 9.11, entitled Tests of Within-Subjects Effects, contains the F and p values that indicate whether these means differ significantly. The researcher must strongly contemplate the decision about accepting or rejecting the null hypothesis for this analysis. The p value of.088 exceeds that standard α of.05, suggesting that no significant differences exist between the means listed in Table Raising the α level to.10, however, would allow the researcher to reject the null hypothesis of equality between means. He or she should weigh the importance of finding significant differences against the increased chance of making a Type I error when deciding whether to change the α value. If the repeated-measures ANOVA indicates significant differences between category means, you must conduct post-hoc tests. These tests search for sources of the significant omnibus results by comparing two groups or two combinations of groups using t-tests. The strategy for determining which groups or combinations of groups to compare follows that explained for the ANOVAs in Chapter 7. However, rather than using independent-samples t tests for the post-hoc tests, as explained in Chapter 7, you must use paired-samples t tests for a repeated-measures ANOVA s post hoc tests. By using the paired-samples t-tests, you continue to acknowledge the one-to-one relationships between subjects in the independent-variable categories that made the repeated-measures ANOVA necessary in the first place. MANOVA with SPSS

6 If you instruct SPSS to perform a MANOVA, it automatically arranges your dependent variables into a canonical variate. The program, then, compares the mean canonical variate values for each independent variable group. You can include as many independent variables as you wish in the analysis by entering their names as fixed factors. For a oneway MANOVA, though, you should identify only one fixed factor, as explained in the following steps. 1. Choose the General Linear Model option in SPSS Analyze pull-down menu. 2. Choose Multivariate from the prompts given. A window entitled Multivariate should appear. FIGURE 9.8 SPSS MULTIVARIATE WINDOW The user performs a MANOVA in SPSS by moving the names of relevant variables from the box on the left side of the window to the Dependent Variables and Fixed Factor(s) boxes in the center of the window. Because the MANOVA involves multiple dependent variables, the Dependent Variables box should contain at least two variable names. The number of variable names moved to the Fixed Factor(s) box depends upon the number of independent variables involved in the analysis. 3. Identify the variables involved in the analysis. a. Move the names of dependent variables from the box on the left side of the window to the box labeled Dependent Variables. b. Move the name of the independent variable from the box on the left side of the window to the box labeled Fixed Factor(s). 4. To include descriptive statistics for the groups in the output, click on the window s Options button. a. Move the name of the independent variable to the box labeled Display Means for box. b. Mark Descriptive Statistics in the Display box. c. Click Continue to return to the Multivariate window.

7 5. Click OK. As with almost all SPSS output, the first table shown simply identifies the categories and the number of subjects in each one. Of more interest that this information, however, is likely the Descriptive Statistics output table, which appears only if you included Step #4 in the process of requesting the MANOVA. This table contains group means and standard deviations for each individual dependent variable. To assess the significance of differences between the mean values, you must evaluate values in the Multivariate Tests table and, in some cases, the Tests of Between-Subjects Effects table. The first of these tables contains F and p values for the MANOVA analysis comparing groups canonical variate means. The Tests of Between Subject Effects table provides data for ANOVAs performed using each individual dependent variable. Example 9.17 Selected SPSS Output for Oneway MANOVA Tables 9.12, 9.13, and 9.14 show sample MANOVA output based upon imaginary data for the scenario described in Example 9.4. SPSS output for the MANOVA contains other tables as well. However, these three tables provide the information needed to address the omnibus hypothesis and the role of the dependent variables in determining whether canonical variate means differ significantly. Descriptive Statistics Genre Mean Std. Deviation N Setting Written Film Musical Total Characters Written Film Musical Total Plot Written Film Musical Total

8 Multivariate Tests c Effect Value F Hypothesis df Error df Sig. Intercept Pillai's Trace a Wilks' Lambda a Hotelling's Trace a Roy's Largest Root a Genre Pillai's Trace Wilks' Lambda a Hotelling's Trace Roy's Largest Root b Tests of Between-Subjects Effects Source Dependent Variable Type III Sum of Squares df Mean Square F Sig. Corrected Model Setting a Characters b Plot c Intercept Setting Characters Plot Genre Setting Characters Plot Error Setting Characters Plot Total Setting Characters Plot Corrected Total Setting Characters

9 Plot TABLE 9.12, TABLE 9.13, AND TABLE 9.14 SELECTED SPSS OUTPUT FOR ONEWAY MANOVA Category means and standard deviations for the canonical variate appear in Table 9.12, entitled Descriptive Statistics. Values in the Multivariate Tests table (Table 9.13) indicate whether these means differ significantly. In this table, the row labeled Wilks Lambda contains the values pertaining to the MANOVA procedure described in Chapter 9. To further understand the p value included in this table, the researcher might find values in Table 9.14, Test of Between-Subjects Effects, useful. This table provides p values for oneway ANOVAs comparing category means for each of the dependent variables that compose the canonical variate. Values in lower portion of the Multivariate Tests table, labeled genre, indicate whether canonical variate means differ significantly for those who experienced the story by reading it, watching it as a film, and watching it as a Broadway musical. In this table, SPSS presents the results from four possible techniques of obtaining F for the MANOVA. For an analysis using Section s method involving Λ, values in the Wilks Lambda row of the table should be examined. The F of and the p of.000) indicate a significant difference between the mean canonical variate values for each genre. The presence of a significant difference in canonical variate means, however, does not imply significant differences in the means for each dependent variable. The results of ANOVAs that compare the mean setting, characters, and plot scores for each category appear in Table According to the values in the genre row of this table and based upon the standard α of.05, subjects in the three independent-variable categories do not have significantly different recall of characters (F=1.715, p=.182). They do, however, have significantly different recall of the story s setting (F=14.932, p=.000) and plot (F=7.355, p=.001). The differences in these dependent variable scores, provide a mathematical explanation for the differences in canonical variate scores. Although not an issue for this analysis, values from the Table 9.14 can also provide a behind the scenes look when you have insignificant results. One cannot assume that accepting the MANOVA s null hypothesis implies that the independent variable groups have equal scores on each dependent variable. Scores for one or more dependent variables may differ significantly among groups. But, a majority of dependent variables with similar scores may mute these differences in the canonical variate. The ANOVA results presented in the Tests of Between Subjects Effects table identify any individual dependent variables with significantly different group means. Had results from Example 9.17 s analysis led to an accepted null hypothesis, you could end your analysis by stating that no significant differences between mean canonical variate

10 values exist. However, with a rejected null hypothesis, you must continue the analysis with post-hoc comparisons to find at least one reason for the significant difference The same technique for performing post-hoc analyses for the ANOVA applies to the MANOVA. However, rather than comparing category means for individual dependent variables, the MANOVA s post-hoc analyses compare category means for canonical variates. So, you should begin by identifying a category or categories with combinations of dependent-variable scores that you believe differ from the others. The total values in the Descriptive Statistics table can help you to determine which category or categories you should contrast from the others. Performing the post-hoc comparisons of canonical variates requires more MANOVAs. These MANOVAs, however, compare only two independent variable categories. SPSS s Select Cases function allows you to specify the categories that you wish to include in the analysis. When necessary, you can also combine categories by recoding them. (See Chapter 2 for instructions about selecting cases and recoding categories.) As with any post-hoc exercise, you must continue making comparisons until you find at least one disparity that produces a p<α. The distinction between the categories that produces these results helps to explain the significant omnibus results. You can obtain very specific information about the source of significant omnibus MANOVA results by determining whether you can associate these differences with particular dependent variables. To do so, you need to compare the means for a particular dependent variable across categories or combinations of categories that your original post-hoc tests identified as different. This investigation uses values in the Tests of Between-Subjects Effects table. The rows labeled with independent variable names contain results from ANOVAs that compare dependent variable means. (Note that, in Example 9.17, these values and those that appear in the Corrected Model row are the same. The two rows contain identical values only for a oneway test.) The values that appear to the right of each dependent variable name indicate whether category means for that dependent variable, alone, differ significantly. If a dependent variable s scores don t differ significantly among groups (p>α) then that dependent variable doesn t contribute to the difference in canonical variate values. But, you may wish to give some attention to dependent variables with scores that do differ significantly (p<α). Post-hoc comparisons of these dependent variable s means amount to nothing more than the t-tests used for post-hoc analyses of ANOVA results, described in Chapter 7. When results of these tests indicate significant differences between means, you know that that scores for this component of the canonical variate helps to account for its significantly different canonical variate means.

11 ANCOVA and MANCOVA with SPSS If you know how to use SPSS s Univariate window to perform a multi-way ANOVA, then you simply need to add a step to the process for an ANCOVA. Similarly, performing a MANCOVA requires just one more step than performing a MANOVA using SPSS s Multivariate window. In both cases, this step involves the identification of covariates. Both the Univariate and the Multivariate windows contain a box labeled Covariate(s). The entire process for performing an ANCOVA in SPSS, then, requires six steps. 1. Choose Compare Means from the Analyze pull-down menu. 2. Choose General Linear Model from the options provided. A new menu should appear to the right of the pull-down menu. Select Univariate from the new menu. A Univariate window should appear on the screen. FIGURE 9.9 SPSS UNIVARIATE WINDOW The user performs an ANCOVA by selecting the appropriate variable names from those listed in the box on the left side of the window. The names of the independent variables should be moved to the fixed factor(s) box. The name of the Dependent Variable and covariate(s) should also be moved to the appropriate areas in the center of the window. 3. Highlight the name of the dependent variable from the list appearing in the upper left corner of the window. Click on the arrow to the left of the Dependent Variable box. The name of the variable should move to this box. 4. Highlight the name of one independent variable from the list appearing in the upper left corner of the window. Click on the arrow to the left of the Fixed Factor(s) box. The

12 name of the variable should move to this box. Continue this process with each independent variable name until they all appear as fixed factors. 5. Highlight the name of one covariate from the list appearing in the upper left corner of the window. Click on the arrow to the left of the Covariate(s) box. The name of the variable should move to this box. Continue this process with each independent variable name until they all appear as covariates. 6. If you would like your output to include descriptive statistics, select the Options button, located on the right side of the window. A new window, entitled Univariate: Options should appear. Select Descriptive Statistics from the Display portion of this window. Then, click Continue to return to the One-Way ANOVA window. Failing to complete this step will still produce valid ANCOVA results. 7. Click OK. Assuming you performed Step #6, above, the SPSS output for an ANCOVA begins with descriptive statistics for each independent-variable category. The results of the significance test appear in the table entitled Tests of Between-Subjects Effects. The Corrected Model values in this table provide the ANCOVA s adjusted sum of squares and the resulting F and significance (p) values. Example 9.18 Selected SPSS Output for Oneway ANCOVA. SPSS output for an analysis of sample data from the situation presented in Example 9.6, which addresses the effectiveness of relaxation techniques, appears as follows. Descriptive Statistics Dependent Variable:change technique Mean Std. Deviation N yoga meditation biofeedback Total Dependent Variable:change Tests of Between-Subjects Effects Source Type III Sum of Squares Df Mean Square F Sig.

13 Corrected Model a Intercept health technique Error Total Corrected Total a. R Squared =.033 (Adjusted R Squared =.013) TABLE 9.15 AND TABLE 9.16 SELECTED SPSS OUTPUT FOR ONEWAY ANCOVA Descriptive statistics for each category of the independent variable appear in Table 9.15, labeled Descriptive Statistics. The Tests of Between-Subjects Effects table (Table 9.16) lists both the independent variable, in this case, technique, and the covariate, in this case health, as predictors of the dependent variable. The values used to determine whether changes in heart rate differ significantly with respect to the independent variable and considering the possible effects of the covariate appear in the top row of this table. According to results of this analysis, those exposed each of the three relaxation techniques did not experience significantly different changes in heart rate. The p value of.183 lies above the standard α of.05 as well as above an elevated α of.10, indicating that one would accept the null hypothesis of equality at these levels of significance. The analysis considered differences in the overall health of patients in the three independent-variable conditions when calculating these results, hence the designation of a Type III Sum of Squares value in the Tests of Between-Subjects Effects table. The process used to request and analyze SPSS results of an ANCOVA translate easily into a MANCOVA. Performing a MANCOVA in SPSS requires the same steps, only you would need to use SPSS s Multivariate, rather than Univariate window. In the Multivariate window, you can identify as many dependent variables as needed for the analysis. SPSS assembles the values for the dependent variables into canonical variate scores. By inputting names of covariates into the Covariate(s) box, you tell SPSS to consider the roles of these covariates upon the relationship between the independent variables and the canonical variate. The MANCOVA output that results contains a Multivariate Tests table. This table resembles the Multivariate Tests table produced for a MANCOVA, however, it also includes the names of covariates. Assuming you wish to consider results based upon the Wilks Lambda procedure for obtaining F, you should focus upon values in this row of the table. A p-value that exceeds α indicates significant differences between mean canonical variate values for the covariate-biased independent-variable categories.

14 Discriminant Analysis with SPSS Rather than working with pre-existing classifications of subjects, as the other tests in Chapter 9 do, a discriminant analysis attempts to create classifications. To conduct a discriminant analysis in SPSS, therefore, you cannot use the General Linear Model function. The following process allows you to use continuous values to predict subjects group placements. 1. Choose the Classify option in SPSS Analyze pull-down menu. 2. Identify your desired type of classification as Discriminant. Choose Discriminant from the prompts given. A window entitled a window entitled Discriminant Analysis should appear. FIGURE 9.9 SPSS DISCRIMINANT ANALYSIS WINDOW The user identifies the variables involved in a one-way discriminant analysis by selecting their names from those listed on the left side of the Discriminant Analysis window. SPSS performs the test using variables with names placed into the Independents and variables with names placed into the Grouping Variables box. 3. In this window, you can define the variables involved in the analysis as follows a. Move the name of the categorical dependent variable from the box on the left to the Grouping Variable box. You must also click on the Define Range button below this box and type the values for the lowest and highest dummy-variable values used to identify groups. b. Identify the continuous measure(s) used to predict subjects categories by moving the names of the predictor(s) to the Independents box. 4. Click OK. The Discriminant Analysis Independents Variable box allows you to identify more than one predictor of subjects categories. Inputting more than one independent variable leads to a multiple discriminant analysis. The analysis presented in Chapter 9 s examples, though, use a single independent variable.

15 Example SPSS Output for Discriminant Analysis Tables 9.18 through 9.21 show the some of the output from applying these steps to imaginary data for the acreage and fencing style example first presented in Example 9.9. As with the output for most tests of significance, SPSS first presents descriptive statistics and then follows with values that indicate predictability. Among these values is a measure of significance based upon the conversion of Wilks Lambda into F, as described in Section 9.5. Group Statistics Valid N (listwise) Fence Unweighted Weighted chain link acreage wrought iron acreage wood acreage vinyl acreage Total acreage Eigenvalues Functio n Eigenvalue % of Variance Cumulative % Canonical Correlation a a. First 1 canonical discriminant functions were used in the analysis. Wilks' Lambda Test of Functio n(s) Wilks' Lambda Chi-square Df Sig Standardized Canonical Discriminant Function Coefficients Function 1

16 Acreage 1 TABLE 9.18, TABLE 9.19, TABLE 9.20, AND TABLE 9.21 SPSS OUTPUT FOR DISCRIMINANT ANALYSIS The number of subjects in each grouping variable category appear as Group Statistics in Table 9.18 The remainder of the tables provide information regarding the predictability of these groups from continuous predictor variable values. The Eigenvalues table (Table 9.19) contains a correlation coefficient (See Chapter 8) representing the linear relationship between the predictor variable and the grouping variable. With the significance value in the Wilks Lambda table (Table 9.20) and the coefficient in the Standardized Canonical Discriminant Function Coefficients table (Table 9.21), the user can determine the strength of the relationship between variables. Of course, given the fact that this analysis involves only one independent variable, the output is relatively simplistic compared to the output for a multiple discriminant analysis. The canonical correlation shown in Table 9.19 amounts to the pairwise correlation between the two variables. For a multiple discriminant analysis, it would describe the linear relationship between the canonical variate (a combination of independent variables) and the grouping variable. Also, the coefficient of 1, shown in Table 9.21, implies a discriminating function of G=x. This equation suggests that all of the responsibility for predicting fencing style lies with acreage. Still, you can easily see, based upon the significance value in Table 9.20, that acreage sufficiently predicts the type of fencing used to enclose property. The p value of.004 indicates a significant relationship between acreage and fencing type at both α=.05 and α=.01. For evaluations that involve more predictors than that in Example 9.20 does, you can use output values in a variety of ways. In particular, researchers often use values in the Standardized Canonical Discriminant Function Coefficient table for more than just identifying the discriminating function. These values can signify the importance of each predictor variable in the relationship with the grouping variable. Because predictor variables with very small coefficients have weak linear relationships with the grouping variable, they likely add little to the predictability of the model. You may wish to perform another discriminant analysis, omitting the predictor variables with low coefficients, to determine whether you really need them to help classify subjects. If results of this analysis also indicate significance, then you know that their presence makes little difference in the ability to classify subjects. So, you do not have to regard them as contributors to the overall canonical predictor. This process allows you to limit your grouping variables to only those that truly help to predict subjects categories.

Set up of the data is similar to the Randomized Block Design situation. A. Chang 1. 1) Setting up the data sheet

Set up of the data is similar to the Randomized Block Design situation. A. Chang 1. 1) Setting up the data sheet Repeated Measure Analysis (Univariate Mixed Effect Model Approach) (Treatment as the Fixed Effect and the Subject as the Random Effect) (This univariate approach can be used for randomized block design

More information

Example Using Missing Data 1

Example Using Missing Data 1 Ronald H. Heck and Lynn N. Tabata 1 Example Using Missing Data 1 Creating the Missing Data Variable (Miss) Here is a data set (achieve subset MANOVAmiss.sav) with the actual missing data on the outcomes.

More information

An introduction to SPSS

An introduction to SPSS An introduction to SPSS To open the SPSS software using U of Iowa Virtual Desktop... Go to https://virtualdesktop.uiowa.edu and choose SPSS 24. Contents NOTE: Save data files in a drive that is accessible

More information

Multiple Regression White paper

Multiple Regression White paper +44 (0) 333 666 7366 Multiple Regression White paper A tool to determine the impact in analysing the effectiveness of advertising spend. Multiple Regression In order to establish if the advertising mechanisms

More information

ANSWERS -- Prep for Psyc350 Laboratory Final Statistics Part Prep a

ANSWERS -- Prep for Psyc350 Laboratory Final Statistics Part Prep a ANSWERS -- Prep for Psyc350 Laboratory Final Statistics Part Prep a Put the following data into an spss data set: Be sure to include variable and value labels and missing value specifications for all variables

More information

DATA DEFINITION PHASE

DATA DEFINITION PHASE Twoway Analysis of Variance Unlike previous problems in the manual, the present problem involves two independent variables (gender of juror and type of crime committed by defendant). There are two levels

More information

Chapter 13 Multivariate Techniques. Chapter Table of Contents

Chapter 13 Multivariate Techniques. Chapter Table of Contents Chapter 13 Multivariate Techniques Chapter Table of Contents Introduction...279 Principal Components Analysis...280 Canonical Correlation...289 References...298 278 Chapter 13. Multivariate Techniques

More information

- 1 - Fig. A5.1 Missing value analysis dialog box

- 1 - Fig. A5.1 Missing value analysis dialog box WEB APPENDIX Sarstedt, M. & Mooi, E. (2019). A concise guide to market research. The process, data, and methods using SPSS (3 rd ed.). Heidelberg: Springer. Missing Value Analysis and Multiple Imputation

More information

Further Maths Notes. Common Mistakes. Read the bold words in the exam! Always check data entry. Write equations in terms of variables

Further Maths Notes. Common Mistakes. Read the bold words in the exam! Always check data entry. Write equations in terms of variables Further Maths Notes Common Mistakes Read the bold words in the exam! Always check data entry Remember to interpret data with the multipliers specified (e.g. in thousands) Write equations in terms of variables

More information

Repeated Measures Part 4: Blood Flow data

Repeated Measures Part 4: Blood Flow data Repeated Measures Part 4: Blood Flow data /* bloodflow.sas */ options linesize=79 pagesize=100 noovp formdlim='_'; title 'Two within-subjecs factors: Blood flow data (NWK p. 1181)'; proc format; value

More information

Introduction to Mixed Models: Multivariate Regression

Introduction to Mixed Models: Multivariate Regression Introduction to Mixed Models: Multivariate Regression EPSY 905: Multivariate Analysis Spring 2016 Lecture #9 March 30, 2016 EPSY 905: Multivariate Regression via Path Analysis Today s Lecture Multivariate

More information

Chapter 3. Finding Sums. This chapter covers procedures for obtaining many of the summed values that are

Chapter 3. Finding Sums. This chapter covers procedures for obtaining many of the summed values that are 1 Chapter 3 Finding Sums This chapter covers procedures for obtaining many of the summed values that are commonly used in statistical calculations. These procedures will produce values that are identical

More information

Introduction. About this Document. What is SPSS. ohow to get SPSS. oopening Data

Introduction. About this Document. What is SPSS. ohow to get SPSS. oopening Data Introduction About this Document This manual was written by members of the Statistical Consulting Program as an introduction to SPSS 12.0. It is designed to assist new users in familiarizing themselves

More information

Statistics Lab #7 ANOVA Part 2 & ANCOVA

Statistics Lab #7 ANOVA Part 2 & ANCOVA Statistics Lab #7 ANOVA Part 2 & ANCOVA PSYCH 710 7 Initialize R Initialize R by entering the following commands at the prompt. You must type the commands exactly as shown. options(contrasts=c("contr.sum","contr.poly")

More information

CDAA No. 4 - Part Two - Multiple Regression - Initial Data Screening

CDAA No. 4 - Part Two - Multiple Regression - Initial Data Screening CDAA No. 4 - Part Two - Multiple Regression - Initial Data Screening Variables Entered/Removed b Variables Entered GPA in other high school, test, Math test, GPA, High school math GPA a Variables Removed

More information

STATISTICS (STAT) Statistics (STAT) 1

STATISTICS (STAT) Statistics (STAT) 1 Statistics (STAT) 1 STATISTICS (STAT) STAT 2013 Elementary Statistics (A) Prerequisites: MATH 1483 or MATH 1513, each with a grade of "C" or better; or an acceptable placement score (see placement.okstate.edu).

More information

E-Campus Inferential Statistics - Part 2

E-Campus Inferential Statistics - Part 2 E-Campus Inferential Statistics - Part 2 Group Members: James Jones Question 4-Isthere a significant difference in the mean prices of the stores? New Textbook Prices New Price Descriptives 95% Confidence

More information

Step-by-Step Guide to Advanced Genetic Analysis

Step-by-Step Guide to Advanced Genetic Analysis Step-by-Step Guide to Advanced Genetic Analysis Page 1 Introduction In the previous document, 1 we covered the standard genetic analyses available in JMP Genomics. Here, we cover the more advanced options

More information

SPSS QM II. SPSS Manual Quantitative methods II (7.5hp) SHORT INSTRUCTIONS BE CAREFUL

SPSS QM II. SPSS Manual Quantitative methods II (7.5hp) SHORT INSTRUCTIONS BE CAREFUL SPSS QM II SHORT INSTRUCTIONS This presentation contains only relatively short instructions on how to perform some statistical analyses in SPSS. Details around a certain function/analysis method not covered

More information

Laboratory for Two-Way ANOVA: Interactions

Laboratory for Two-Way ANOVA: Interactions Laboratory for Two-Way ANOVA: Interactions For the last lab, we focused on the basics of the Two-Way ANOVA. That is, you learned how to compute a Brown-Forsythe analysis for a Two-Way ANOVA, as well as

More information

Mean Tests & X 2 Parametric vs Nonparametric Errors Selection of a Statistical Test SW242

Mean Tests & X 2 Parametric vs Nonparametric Errors Selection of a Statistical Test SW242 Mean Tests & X 2 Parametric vs Nonparametric Errors Selection of a Statistical Test SW242 Creation & Description of a Data Set * 4 Levels of Measurement * Nominal, ordinal, interval, ratio * Variable Types

More information

Statistical Analysis Using SPSS for Windows Getting Started (Ver. 2018/10/30) The numbers of figures in the SPSS_screenshot.pptx are shown in red.

Statistical Analysis Using SPSS for Windows Getting Started (Ver. 2018/10/30) The numbers of figures in the SPSS_screenshot.pptx are shown in red. Statistical Analysis Using SPSS for Windows Getting Started (Ver. 2018/10/30) The numbers of figures in the SPSS_screenshot.pptx are shown in red. 1. How to display English messages from IBM SPSS Statistics

More information

Data Mining. SPSS Clementine k-means Algorithm. Spring 2010 Instructor: Dr. Masoud Yaghini. Clementine

Data Mining. SPSS Clementine k-means Algorithm. Spring 2010 Instructor: Dr. Masoud Yaghini. Clementine Data Mining SPSS 12.0 6. k-means Algorithm Spring 2010 Instructor: Dr. Masoud Yaghini Outline K-Means Algorithm in K-Means Node References K-Means Algorithm in Overview The k-means method is a clustering

More information

The following procedures and commands, are covered in this part: Command Purpose Page

The following procedures and commands, are covered in this part: Command Purpose Page Some Procedures in SPSS Part (2) This handout describes some further procedures in SPSS, following on from Part (1). Because some of the procedures covered are complex, with many sub-commands, the descriptions

More information

Data Analysis and Solver Plugins for KSpread USER S MANUAL. Tomasz Maliszewski

Data Analysis and Solver Plugins for KSpread USER S MANUAL. Tomasz Maliszewski Data Analysis and Solver Plugins for KSpread USER S MANUAL Tomasz Maliszewski tmaliszewski@wp.pl Table of Content CHAPTER 1: INTRODUCTION... 3 1.1. ABOUT DATA ANALYSIS PLUGIN... 3 1.3. ABOUT SOLVER PLUGIN...

More information

Correctly Compute Complex Samples Statistics

Correctly Compute Complex Samples Statistics SPSS Complex Samples 15.0 Specifications Correctly Compute Complex Samples Statistics When you conduct sample surveys, use a statistics package dedicated to producing correct estimates for complex sample

More information

6:1 LAB RESULTS -WITHIN-S ANOVA

6:1 LAB RESULTS -WITHIN-S ANOVA 6:1 LAB RESULTS -WITHIN-S ANOVA T1/T2/T3/T4. SStotal =(1-12) 2 + + (18-12) 2 = 306.00 = SSpill + SSsubj + SSpxs df = 9-1 = 8 P1 P2 P3 Ms Ms-Mg 1 8 15 8.0-4.0 SSsubj= 3x(-4 2 + ) 4 17 15 12.0 0 = 96.0 13

More information

Research Methods for Business and Management. Session 8a- Analyzing Quantitative Data- using SPSS 16 Andre Samuel

Research Methods for Business and Management. Session 8a- Analyzing Quantitative Data- using SPSS 16 Andre Samuel Research Methods for Business and Management Session 8a- Analyzing Quantitative Data- using SPSS 16 Andre Samuel A Simple Example- Gym Purpose of Questionnaire- to determine the participants involvement

More information

An Example of Using inter5.exe to Obtain the Graph of an Interaction

An Example of Using inter5.exe to Obtain the Graph of an Interaction An Example of Using inter5.exe to Obtain the Graph of an Interaction This example covers the general use of inter5.exe to produce data from values inserted into a regression equation which can then be

More information

PRI Workshop Introduction to AMOS

PRI Workshop Introduction to AMOS PRI Workshop Introduction to AMOS Krissy Zeiser Pennsylvania State University klz24@pop.psu.edu 2-pm /3/2008 Setting up the Dataset Missing values should be recoded in another program (preferably with

More information

Chapter 4: Analyzing Bivariate Data with Fathom

Chapter 4: Analyzing Bivariate Data with Fathom Chapter 4: Analyzing Bivariate Data with Fathom Summary: Building from ideas introduced in Chapter 3, teachers continue to analyze automobile data using Fathom to look for relationships between two quantitative

More information

BIOL 458 BIOMETRY Lab 10 - Multiple Regression

BIOL 458 BIOMETRY Lab 10 - Multiple Regression BIOL 458 BIOMETRY Lab 0 - Multiple Regression Many problems in biology science involve the analysis of multivariate data sets. For data sets in which there is a single continuous dependent variable, but

More information

Enter your UID and password. Make sure you have popups allowed for this site.

Enter your UID and password. Make sure you have popups allowed for this site. Log onto: https://apps.csbs.utah.edu/ Enter your UID and password. Make sure you have popups allowed for this site. You may need to go to preferences (right most tab) and change your client to Java. I

More information

Tutorial #1: Using Latent GOLD choice to Estimate Discrete Choice Models

Tutorial #1: Using Latent GOLD choice to Estimate Discrete Choice Models Tutorial #1: Using Latent GOLD choice to Estimate Discrete Choice Models In this tutorial, we analyze data from a simple choice-based conjoint (CBC) experiment designed to estimate market shares (choice

More information

SPSS TRAINING SPSS VIEWS

SPSS TRAINING SPSS VIEWS SPSS TRAINING SPSS VIEWS Dataset Data file Data View o Full data set, structured same as excel (variable = column name, row = record) Variable View o Provides details for each variable (column in Data

More information

Study Guide. Module 1. Key Terms

Study Guide. Module 1. Key Terms Study Guide Module 1 Key Terms general linear model dummy variable multiple regression model ANOVA model ANCOVA model confounding variable squared multiple correlation adjusted squared multiple correlation

More information

Correctly Compute Complex Samples Statistics

Correctly Compute Complex Samples Statistics PASW Complex Samples 17.0 Specifications Correctly Compute Complex Samples Statistics When you conduct sample surveys, use a statistics package dedicated to producing correct estimates for complex sample

More information

Statistics with a Hemacytometer

Statistics with a Hemacytometer Statistics with a Hemacytometer Overview This exercise incorporates several different statistical analyses. Data gathered from cell counts with a hemacytometer is used to explore frequency distributions

More information

CHAPTER 7 EXAMPLES: MIXTURE MODELING WITH CROSS- SECTIONAL DATA

CHAPTER 7 EXAMPLES: MIXTURE MODELING WITH CROSS- SECTIONAL DATA Examples: Mixture Modeling With Cross-Sectional Data CHAPTER 7 EXAMPLES: MIXTURE MODELING WITH CROSS- SECTIONAL DATA Mixture modeling refers to modeling with categorical latent variables that represent

More information

The Power and Sample Size Application

The Power and Sample Size Application Chapter 72 The Power and Sample Size Application Contents Overview: PSS Application.................................. 6148 SAS Power and Sample Size............................... 6148 Getting Started:

More information

8. MINITAB COMMANDS WEEK-BY-WEEK

8. MINITAB COMMANDS WEEK-BY-WEEK 8. MINITAB COMMANDS WEEK-BY-WEEK In this section of the Study Guide, we give brief information about the Minitab commands that are needed to apply the statistical methods in each week s study. They are

More information

AMELIA II: A Program for Missing Data

AMELIA II: A Program for Missing Data AMELIA II: A Program for Missing Data Amelia II is an R package that performs multiple imputation to deal with missing data, instead of other methods, such as pairwise and listwise deletion. In multiple

More information

Chapter 1. Using the Cluster Analysis. Background Information

Chapter 1. Using the Cluster Analysis. Background Information Chapter 1 Using the Cluster Analysis Background Information Cluster analysis is the name of a multivariate technique used to identify similar characteristics in a group of observations. In cluster analysis,

More information

Data analysis using Microsoft Excel

Data analysis using Microsoft Excel Introduction to Statistics Statistics may be defined as the science of collection, organization presentation analysis and interpretation of numerical data from the logical analysis. 1.Collection of Data

More information

Multivariate Capability Analysis

Multivariate Capability Analysis Multivariate Capability Analysis Summary... 1 Data Input... 3 Analysis Summary... 4 Capability Plot... 5 Capability Indices... 6 Capability Ellipse... 7 Correlation Matrix... 8 Tests for Normality... 8

More information

Clustering and Visualisation of Data

Clustering and Visualisation of Data Clustering and Visualisation of Data Hiroshi Shimodaira January-March 28 Cluster analysis aims to partition a data set into meaningful or useful groups, based on distances between data points. In some

More information

Resources for statistical assistance. Quantitative covariates and regression analysis. Methods for predicting continuous outcomes.

Resources for statistical assistance. Quantitative covariates and regression analysis. Methods for predicting continuous outcomes. Resources for statistical assistance Quantitative covariates and regression analysis Carolyn Taylor Applied Statistics and Data Science Group (ASDa) Department of Statistics, UBC January 24, 2017 Department

More information

DI TRANSFORM. The regressive analyses. identify relationships

DI TRANSFORM. The regressive analyses. identify relationships July 2, 2015 DI TRANSFORM MVstats TM Algorithm Overview Summary The DI Transform Multivariate Statistics (MVstats TM ) package includes five algorithm options that operate on most types of geologic, geophysical,

More information

Ronald H. Heck 1 EDEP 606 (F2015): Multivariate Methods rev. November 16, 2015 The University of Hawai i at Mānoa

Ronald H. Heck 1 EDEP 606 (F2015): Multivariate Methods rev. November 16, 2015 The University of Hawai i at Mānoa Ronald H. Heck 1 In this handout, we will address a number of issues regarding missing data. It is often the case that the weakest point of a study is the quality of the data that can be brought to bear

More information

WELCOME! Lecture 3 Thommy Perlinger

WELCOME! Lecture 3 Thommy Perlinger Quantitative Methods II WELCOME! Lecture 3 Thommy Perlinger Program Lecture 3 Cleaning and transforming data Graphical examination of the data Missing Values Graphical examination of the data It is important

More information

If the active datasheet is empty when the StatWizard appears, a dialog box is displayed to assist in entering data.

If the active datasheet is empty when the StatWizard appears, a dialog box is displayed to assist in entering data. StatWizard Summary The StatWizard is designed to serve several functions: 1. It assists new users in entering data to be analyzed. 2. It provides a search facility to help locate desired statistical procedures.

More information

4. Descriptive Statistics: Measures of Variability and Central Tendency

4. Descriptive Statistics: Measures of Variability and Central Tendency 4. Descriptive Statistics: Measures of Variability and Central Tendency Objectives Calculate descriptive for continuous and categorical data Edit output tables Although measures of central tendency and

More information

The Solution to the Factorial Analysis of Variance

The Solution to the Factorial Analysis of Variance The Solution to the Factorial Analysis of Variance As shown in the Excel file, Howell -2, the ANOVA analysis (in the ToolPac) yielded the following table: Anova: Two-Factor With Replication SUMMARYCounting

More information

Principal Components Analysis with Spatial Data

Principal Components Analysis with Spatial Data Principal Components Analysis with Spatial Data A SpaceStat Software Tutorial Copyright 2013, BioMedware, Inc. (www.biomedware.com). All rights reserved. SpaceStat and BioMedware are trademarks of BioMedware,

More information

Independent Variables

Independent Variables 1 Stepwise Multiple Regression Olivia Cohen Com 631, Spring 2017 Data: Film & TV Usage 2015 I. MODEL Independent Variables Demographics Item: Age Item: Income Dummied Item: Gender (Female) Digital Media

More information

Your Name: Section: INTRODUCTION TO STATISTICAL REASONING Computer Lab #4 Scatterplots and Regression

Your Name: Section: INTRODUCTION TO STATISTICAL REASONING Computer Lab #4 Scatterplots and Regression Your Name: Section: 36-201 INTRODUCTION TO STATISTICAL REASONING Computer Lab #4 Scatterplots and Regression Objectives: 1. To learn how to interpret scatterplots. Specifically you will investigate, using

More information

TABEL DISTRIBUSI DAN HUBUNGAN LENGKUNG RAHANG DAN INDEKS FASIAL N MIN MAX MEAN SD

TABEL DISTRIBUSI DAN HUBUNGAN LENGKUNG RAHANG DAN INDEKS FASIAL N MIN MAX MEAN SD TABEL DISTRIBUSI DAN HUBUNGAN LENGKUNG RAHANG DAN INDEKS FASIAL Lengkung Indeks fasial rahang Euryprosopic mesoprosopic leptoprosopic Total Sig. n % n % n % n % 0,000 Narrow 0 0 0 0 15 32,6 15 32,6 Normal

More information

STATS PAD USER MANUAL

STATS PAD USER MANUAL STATS PAD USER MANUAL For Version 2.0 Manual Version 2.0 1 Table of Contents Basic Navigation! 3 Settings! 7 Entering Data! 7 Sharing Data! 8 Managing Files! 10 Running Tests! 11 Interpreting Output! 11

More information

Minitab Study Card J ENNIFER L EWIS P RIESTLEY, PH.D.

Minitab Study Card J ENNIFER L EWIS P RIESTLEY, PH.D. Minitab Study Card J ENNIFER L EWIS P RIESTLEY, PH.D. Introduction to Minitab The interface for Minitab is very user-friendly, with a spreadsheet orientation. When you first launch Minitab, you will see

More information

Linear Methods for Regression and Shrinkage Methods

Linear Methods for Regression and Shrinkage Methods Linear Methods for Regression and Shrinkage Methods Reference: The Elements of Statistical Learning, by T. Hastie, R. Tibshirani, J. Friedman, Springer 1 Linear Regression Models Least Squares Input vectors

More information

For our example, we will look at the following factors and factor levels.

For our example, we will look at the following factors and factor levels. In order to review the calculations that are used to generate the Analysis of Variance, we will use the statapult example. By adjusting various settings on the statapult, you are able to throw the ball

More information

Psychology 282 Lecture #21 Outline Categorical IVs in MLR: Effects Coding and Contrast Coding

Psychology 282 Lecture #21 Outline Categorical IVs in MLR: Effects Coding and Contrast Coding Psychology 282 Lecture #21 Outline Categorical IVs in MLR: Effects Coding and Contrast Coding In the previous lecture we learned how to incorporate a categorical research factor into a MLR model by using

More information

Product Catalog. AcaStat. Software

Product Catalog. AcaStat. Software Product Catalog AcaStat Software AcaStat AcaStat is an inexpensive and easy-to-use data analysis tool. Easily create data files or import data from spreadsheets or delimited text files. Run crosstabulations,

More information

STATISTICS FOR PSYCHOLOGISTS

STATISTICS FOR PSYCHOLOGISTS STATISTICS FOR PSYCHOLOGISTS SECTION: JAMOVI CHAPTER: USING THE SOFTWARE Section Abstract: This section provides step-by-step instructions on how to obtain basic statistical output using JAMOVI, both visually

More information

Spatial Patterns Point Pattern Analysis Geographic Patterns in Areal Data

Spatial Patterns Point Pattern Analysis Geographic Patterns in Areal Data Spatial Patterns We will examine methods that are used to analyze patterns in two sorts of spatial data: Point Pattern Analysis - These methods concern themselves with the location information associated

More information

Within-Cases: Multivariate approach part one

Within-Cases: Multivariate approach part one Within-Cases: Multivariate approach part one /* sleep2.sas */ options linesize=79 noovp formdlim=' '; title "Student's Sleep data: Matched t-tests with proc reg"; data bedtime; infile 'studentsleep.data'

More information

Applied Regression Modeling: A Business Approach

Applied Regression Modeling: A Business Approach i Applied Regression Modeling: A Business Approach Computer software help: SPSS SPSS (originally Statistical Package for the Social Sciences ) is a commercial statistical software package with an easy-to-use

More information

Minitab Notes for Activity 1

Minitab Notes for Activity 1 Minitab Notes for Activity 1 Creating the Worksheet 1. Label the columns as team, heat, and time. 2. Have Minitab automatically enter the team data for you. a. Choose Calc / Make Patterned Data / Simple

More information

( ) = Y ˆ. Calibration Definition A model is calibrated if its predictions are right on average: ave(response Predicted value) = Predicted value.

( ) = Y ˆ. Calibration Definition A model is calibrated if its predictions are right on average: ave(response Predicted value) = Predicted value. Calibration OVERVIEW... 2 INTRODUCTION... 2 CALIBRATION... 3 ANOTHER REASON FOR CALIBRATION... 4 CHECKING THE CALIBRATION OF A REGRESSION... 5 CALIBRATION IN SIMPLE REGRESSION (DISPLAY.JMP)... 5 TESTING

More information

Monitoring and Improving Quality of Data Handling

Monitoring and Improving Quality of Data Handling Monitoring and Improving Quality of Data Handling The purpose of this document is to: (a) (b) (c) Maximise the quality of the research process once the question has been formulated and the study designed.

More information

Data Analysis and Hypothesis Testing Using the Python ecosystem

Data Analysis and Hypothesis Testing Using the Python ecosystem ARISTOTLE UNIVERSITY OF THESSALONIKI Data Analysis and Hypothesis Testing Using the Python ecosystem t-test & ANOVAs Stavros Demetriadis Assc. Prof., School of Informatics, Aristotle University of Thessaloniki

More information

Table Of Contents. Table Of Contents

Table Of Contents. Table Of Contents Statistics Table Of Contents Table Of Contents Basic Statistics... 7 Basic Statistics Overview... 7 Descriptive Statistics Available for Display or Storage... 8 Display Descriptive Statistics... 9 Store

More information

STAT 311 (3 CREDITS) VARIANCE AND REGRESSION ANALYSIS ELECTIVE: ALL STUDENTS. CONTENT Introduction to Computer application of variance and regression

STAT 311 (3 CREDITS) VARIANCE AND REGRESSION ANALYSIS ELECTIVE: ALL STUDENTS. CONTENT Introduction to Computer application of variance and regression STAT 311 (3 CREDITS) VARIANCE AND REGRESSION ANALYSIS ELECTIVE: ALL STUDENTS. CONTENT Introduction to Computer application of variance and regression analysis. Analysis of Variance: one way classification,

More information

Fathom Dynamic Data TM Version 2 Specifications

Fathom Dynamic Data TM Version 2 Specifications Data Sources Fathom Dynamic Data TM Version 2 Specifications Use data from one of the many sample documents that come with Fathom. Enter your own data by typing into a case table. Paste data from other

More information

DEPARTMENT OF HEALTH AND HUMAN SCIENCES HS900 RESEARCH METHODS

DEPARTMENT OF HEALTH AND HUMAN SCIENCES HS900 RESEARCH METHODS DEPARTMENT OF HEALTH AND HUMAN SCIENCES HS900 RESEARCH METHODS Using SPSS Topics addressed today: 1. Accessing data from CMR 2. Starting SPSS 3. Getting familiar with SPSS 4. Entering data 5. Saving data

More information

Unit 1 Review of BIOSTATS 540 Practice Problems SOLUTIONS - Stata Users

Unit 1 Review of BIOSTATS 540 Practice Problems SOLUTIONS - Stata Users BIOSTATS 640 Spring 2018 Review of Introductory Biostatistics STATA solutions Page 1 of 13 Key Comments begin with an * Commands are in bold black I edited the output so that it appears here in blue Unit

More information

I. MODEL. Q3i: Check my . Q29s: I like to see films and TV programs from other countries. Q28e: I like to watch TV shows on a laptop/tablet/phone

I. MODEL. Q3i: Check my  . Q29s: I like to see films and TV programs from other countries. Q28e: I like to watch TV shows on a laptop/tablet/phone 1 Multiple Regression-FORCED-ENTRY HIERARCHICAL MODEL DORIS ACHEME COM 631/731, Spring 2017 Data: Film & TV Usage 2015 I. MODEL IV Block 1: Demographics Sex (female dummy):q30 Age: Q31 Income: Q34 Block

More information

THE UNIVERSITY OF BRITISH COLUMBIA FORESTRY 430 and 533. Time: 50 minutes 40 Marks FRST Marks FRST 533 (extra questions)

THE UNIVERSITY OF BRITISH COLUMBIA FORESTRY 430 and 533. Time: 50 minutes 40 Marks FRST Marks FRST 533 (extra questions) THE UNIVERSITY OF BRITISH COLUMBIA FORESTRY 430 and 533 MIDTERM EXAMINATION: October 14, 2005 Instructor: Val LeMay Time: 50 minutes 40 Marks FRST 430 50 Marks FRST 533 (extra questions) This examination

More information

DDoS Attack Detection Using Moment in Statistics with Discriminant Analysis

DDoS Attack Detection Using Moment in Statistics with Discriminant Analysis DDoS Attack Detection Using Moment in Statistics with Discriminant Analysis Pradit Pitaksathienkul 1 and Pongpisit Wuttidittachotti 2 King Mongkut s University of Technology North Bangkok, Thailand 1 praditp9@gmail.com

More information

Basic concepts and terms

Basic concepts and terms CHAPTER ONE Basic concepts and terms I. Key concepts Test usefulness Reliability Construct validity Authenticity Interactiveness Impact Practicality Assessment Measurement Test Evaluation Grading/marking

More information

Building Better Parametric Cost Models

Building Better Parametric Cost Models Building Better Parametric Cost Models Based on the PMI PMBOK Guide Fourth Edition 37 IPDI has been reviewed and approved as a provider of project management training by the Project Management Institute

More information

Statistical Pattern Recognition

Statistical Pattern Recognition Statistical Pattern Recognition Features and Feature Selection Hamid R. Rabiee Jafar Muhammadi Spring 2012 http://ce.sharif.edu/courses/90-91/2/ce725-1/ Agenda Features and Patterns The Curse of Size and

More information

Land Cover Stratified Accuracy Assessment For Digital Elevation Model derived from Airborne LIDAR Dade County, Florida

Land Cover Stratified Accuracy Assessment For Digital Elevation Model derived from Airborne LIDAR Dade County, Florida Land Cover Stratified Accuracy Assessment For Digital Elevation Model derived from Airborne LIDAR Dade County, Florida FINAL REPORT Submitted October 2004 Prepared by: Daniel Gann Geographic Information

More information

SPSS: AN OVERVIEW. V.K. Bhatia Indian Agricultural Statistics Research Institute, New Delhi

SPSS: AN OVERVIEW. V.K. Bhatia Indian Agricultural Statistics Research Institute, New Delhi SPSS: AN OVERVIEW V.K. Bhatia Indian Agricultural Statistics Research Institute, New Delhi-110012 The abbreviation SPSS stands for Statistical Package for the Social Sciences and is a comprehensive system

More information

[/TTEST [PERCENT={5}] [{T }] [{DF } [{PROB }] [{COUNTS }] [{MEANS }]] {n} {NOT} {NODF} {NOPROB}] {NOCOUNTS} {NOMEANS}

[/TTEST [PERCENT={5}] [{T }] [{DF } [{PROB }] [{COUNTS }] [{MEANS }]] {n} {NOT} {NODF} {NOPROB}] {NOCOUNTS} {NOMEANS} MVA MVA [VARIABLES=] {varlist} {ALL } [/CATEGORICAL=varlist] [/MAXCAT={25 ** }] {n } [/ID=varname] Description: [/NOUNIVARIATE] [/TTEST [PERCENT={5}] [{T }] [{DF } [{PROB }] [{COUNTS }] [{MEANS }]] {n}

More information

Centering and Interactions: The Training Data

Centering and Interactions: The Training Data Centering and Interactions: The Training Data A random sample of 150 technical support workers were first given a test of their technical skill and knowledge, and then randomly assigned to one of three

More information

THIS IS NOT REPRESNTATIVE OF CURRENT CLASS MATERIAL. STOR 455 Midterm 1 September 28, 2010

THIS IS NOT REPRESNTATIVE OF CURRENT CLASS MATERIAL. STOR 455 Midterm 1 September 28, 2010 THIS IS NOT REPRESNTATIVE OF CURRENT CLASS MATERIAL STOR 455 Midterm September 8, INSTRUCTIONS: BOTH THE EXAM AND THE BUBBLE SHEET WILL BE COLLECTED. YOU MUST PRINT YOUR NAME AND SIGN THE HONOR PLEDGE

More information

One Factor Experiments

One Factor Experiments One Factor Experiments 20-1 Overview Computation of Effects Estimating Experimental Errors Allocation of Variation ANOVA Table and F-Test Visual Diagnostic Tests Confidence Intervals For Effects Unequal

More information

Lecture 3: Linear Classification

Lecture 3: Linear Classification Lecture 3: Linear Classification Roger Grosse 1 Introduction Last week, we saw an example of a learning task called regression. There, the goal was to predict a scalar-valued target from a set of features.

More information

Statistical Models for Management. Instituto Superior de Ciências do Trabalho e da Empresa (ISCTE) Lisbon. February 24 26, 2010

Statistical Models for Management. Instituto Superior de Ciências do Trabalho e da Empresa (ISCTE) Lisbon. February 24 26, 2010 Statistical Models for Management Instituto Superior de Ciências do Trabalho e da Empresa (ISCTE) Lisbon February 24 26, 2010 Graeme Hutcheson, University of Manchester Principal Component and Factor Analysis

More information

Minitab 17 commands Prepared by Jeffrey S. Simonoff

Minitab 17 commands Prepared by Jeffrey S. Simonoff Minitab 17 commands Prepared by Jeffrey S. Simonoff Data entry and manipulation To enter data by hand, click on the Worksheet window, and enter the values in as you would in any spreadsheet. To then save

More information

Robust Linear Regression (Passing- Bablok Median-Slope)

Robust Linear Regression (Passing- Bablok Median-Slope) Chapter 314 Robust Linear Regression (Passing- Bablok Median-Slope) Introduction This procedure performs robust linear regression estimation using the Passing-Bablok (1988) median-slope algorithm. Their

More information

24 - TEAMWORK... 1 HOW DOES MAXQDA SUPPORT TEAMWORK?... 1 TRANSFER A MAXQDA PROJECT TO OTHER TEAM MEMBERS... 2

24 - TEAMWORK... 1 HOW DOES MAXQDA SUPPORT TEAMWORK?... 1 TRANSFER A MAXQDA PROJECT TO OTHER TEAM MEMBERS... 2 24 - Teamwork Contents 24 - TEAMWORK... 1 HOW DOES MAXQDA SUPPORT TEAMWORK?... 1 TRANSFER A MAXQDA PROJECT TO OTHER TEAM MEMBERS... 2 Sharing projects that include external files... 3 TRANSFER CODED SEGMENTS,

More information

CHAPTER 5. BASIC STEPS FOR MODEL DEVELOPMENT

CHAPTER 5. BASIC STEPS FOR MODEL DEVELOPMENT CHAPTER 5. BASIC STEPS FOR MODEL DEVELOPMENT This chapter provides step by step instructions on how to define and estimate each of the three types of LC models (Cluster, DFactor or Regression) and also

More information

Meet MINITAB. Student Release 14. for Windows

Meet MINITAB. Student Release 14. for Windows Meet MINITAB Student Release 14 for Windows 2003, 2004 by Minitab Inc. All rights reserved. MINITAB and the MINITAB logo are registered trademarks of Minitab Inc. All other marks referenced remain the

More information

Generalized Procrustes Analysis Example with Annotation

Generalized Procrustes Analysis Example with Annotation Generalized Procrustes Analysis Example with Annotation James W. Grice, Ph.D. Oklahoma State University th February 4, 2007 Generalized Procrustes Analysis (GPA) is particularly useful for analyzing repertory

More information

1. What specialist uses information obtained from bones to help police solve crimes?

1. What specialist uses information obtained from bones to help police solve crimes? Mathematics: Modeling Our World Unit 4: PREDICTION HANDOUT VIDEO VIEWING GUIDE H4.1 1. What specialist uses information obtained from bones to help police solve crimes? 2.What are some things that can

More information

QUEEN MARY, UNIVERSITY OF LONDON. Introduction to Statistics

QUEEN MARY, UNIVERSITY OF LONDON. Introduction to Statistics QUEEN MARY, UNIVERSITY OF LONDON MTH 4106 Introduction to Statistics Practical 1 10 January 2012 In this practical you will be introduced to the statistical computing package called Minitab. You will use

More information

THE L.L. THURSTONE PSYCHOMETRIC LABORATORY UNIVERSITY OF NORTH CAROLINA. Forrest W. Young & Carla M. Bann

THE L.L. THURSTONE PSYCHOMETRIC LABORATORY UNIVERSITY OF NORTH CAROLINA. Forrest W. Young & Carla M. Bann Forrest W. Young & Carla M. Bann THE L.L. THURSTONE PSYCHOMETRIC LABORATORY UNIVERSITY OF NORTH CAROLINA CB 3270 DAVIE HALL, CHAPEL HILL N.C., USA 27599-3270 VISUAL STATISTICS PROJECT WWW.VISUALSTATS.ORG

More information

CREATING SIMULATED DATASETS Edition by G. David Garson and Statistical Associates Publishing Page 1

CREATING SIMULATED DATASETS Edition by G. David Garson and Statistical Associates Publishing Page 1 Copyright @c 2012 by G. David Garson and Statistical Associates Publishing Page 1 @c 2012 by G. David Garson and Statistical Associates Publishing. All rights reserved worldwide in all media. No permission

More information