Two-Way Analysis of Variance [electronic resource] : Statistical Tests and Graphics Using R /
MacFarland, Thomas W.author.
In statistics, analysis of variance (ANOVA) is a collection of statistical models used to distinguish between an observed variance in a particular variable and its component parts.  In its simplest form, ANOVA provides a statistical test of whether or not the means of several groups are all equal, and therefore generalizes a test between these groups. One test often used by statisticians and researchers in their work is the Two-Way ANOVA, which determines the differences--and possible interactions--when variables are presented from the perspective of two or more categories.  When a Two-Way ANOVA is implemented, it enables one to compare and contrast variables resulting from independent or joint actions. This brief provides guidance on how R can be used to facilitate Two-Way ANOVA for data analysis and graphical presentation.  Along with instruction on the use of R and R syntax associated with Two-Way ANOVA, this brief will also reinforce the use of descriptive statistics and graphical figures to complement outcomes from parametric Two-Way ANOVA. .
Description based upon print version of record.
Two-Way Analysis of  Variance; Contents; Chapter 1: Learn R with Sample Lessons in Education and the Social Sciences, Health, and the Biological Sciences; 1.1 Purpose of These Lessons; 1.2 Background on R; 1.3 Background on Two-Way ANOVA; 1.4 Organization of These Lessons; 1.4.1 Data Import or Data Entry; 1.4.2 Data Organization; 1.4.3 Display the Code Book; 1.4.4 Conduct a Visual Data Check; 1.4.5 Descriptive Analysis of the Data; 1.5 Details of the Three Sample Datasets; 1.5.1 Tab-Separated ASCII File; 1.5.2 Fixed Width-Fixed Column ASCII File; 1.5.3 Comma-Separated Values ASCII File
Chapter 2: Two-Way Analysis of Variance (ANOVA) Sample 1: Comparison of Scores on a Final Examination by Teaching Method and by Status as a Community College Graduate2.1 Data Import of a .txt Tab-Delimited Data File into R; 2.1.1 Data Import or Data Entry; 2.2 Organize the Data and Display the Code Book; 2.3 Conduct a Visual Data Check; 2.3.1 Simple Plots; 2.3.2 Histogram of the Summary Object Variable; 2.3.3 Horizontal and Vertical Boxplots of the Summary Object Variable; 2.3.4 Sorted Dot Chart of the Summary Object Variable by Breakout Object Variables
2.3.5 Histogram of the Summary Object Variable by Breakout Object Variables2.4 Descriptive Analysis of the Data; 2.4.1 Summary Descriptive Statistics; 2.4.2 Breakout Descriptive Statistics; 2.5 Use R for Two-Way Analysis of Variance (ANOVA); 2.5.1 Two-Way ANOVA: aov() Function; 2.5.2 Outcome to Sample 1; Chapter 3: Two-Way Analysis of Variance (ANOVA) Sample 2: Comparison of Systolic Blood Pressure Readings by Self-Declared Smoking Habits and by Self-Declared Drinking Habits; 3.1 Data Import of a .prn Fixed Width-Fixed Column (e.g., Space-Separated) Format Data File into R
3.1.1 Data Import or Data Entry3.2 Organize the Data and Display the Code Book; 3.3 Conduct a Visual Data Check; 3.3.1 Simple Plots; 3.3.2 Histogram of the Summary Object Variable; 3.3.3 Horizontal and Vertical Boxplots of the Summary Object Variable; 3.3.4 Histogram of the Summary Object Variable by Breakout Object Variables; 3.3.4.1 Breakout Histograms by Count; 3.3.5 Density Plot of the Summary Object Variable by Breakout Object Variables; 3.3.6 Boxplot of the Summary Object Variable by Breakout Object Variables; 3.4 Descriptive Analysis of the Data; 3.4.1 Summary Descriptive Statistics
3.4.2 Breakout Descriptive Statistics3.5 Use R for Two-Way Analysis of Variance (ANOVA); 3.5.1 Two-Way ANOVA: aov() Function; 3.5.2 s20x Package; 3.5.3 Outcome to Sample 2; 3.5.3.1 Smoking Habits; 3.5.3.2 Drinking Habits; 3.5.3.3 Interaction Between Smoking Habits and Drinking Habits; Chapter 4: Two-Way Analysis of Variance (ANOVA) Sample 3: Comparison of Larvae Counts by AgChem Formulation and by AgChem Application Time-of-Day; 4.1 Data Import of a .csv Spreadsheet-Type Data File into R; 4.1.1 Data Import or Data Entry; 4.2 Organize the Data and Display the Code Book
4.3 Conduct a Visual Data Check
In statistics, analysis of variance (ANOVA) is a collection of statistical models used to distinguish between an observed variance in a particular variable and its component parts.  In its simplest form, ANOVA provides a statistical test of whether or not the means of several groups are all equal, and therefore generalizes a test between these groups. One test often used by statisticians and researchers in their work is the Two-Way ANOVA, which determines the differences--and possible interactions--when variables are presented from the perspective of two or more categories.  When a Two-Way ANOVA is implemented, it enables one to compare and contrast variables resulting from independent or joint actions. This brief provides guidance on how R can be used to facilitate Two-Way ANOVA for data analysis and graphical presentation.  Along with instruction on the use of R and R syntax associated with Two-Way ANOVA, this brief will also reinforce the use of descriptive statistics and graphical figures to complement outcomes from parametric Two-Way ANOVA. .
Statistics .
R (Computer program language).
Statistics for Social Sciences, Humanities, Law.
Statistics, general.
Statistical Theory and Methods.
SpringerBriefs in Statistics