Final Exam period: 3-4:50 pm on Tuesday, December 16th.
For Excel and R simulation: When I Procrastinate, I Write Code - see November 20th, 2014 blog entry.
Week #11 resources...
Assignment due on Oct 30: ANOVA and ggplot and Regression...
...QUIZ on Tuesday, October 21st...
Quiz One -
Readings/pages from DSUR,
the fall of 2014 textbook:
You have this as a HANDOUT from Thursday, October 21st.
Assignment due Tuesday October 14th: Oct7th2014Assignment.pdf...
Up and Running with R by Barton Poulson.
SPSS Statistics Essential Training 2011 from lynda.uni.edu.
What to turn in for the assignment: 1. Using the AlbumSales.csv file and the R stat software through RStudio, you will produce 4 scatterplots. There are 4 independent variables and 1 dependent variable. Investigate visually the relationship of each IV with the DV. You will do the regression in R using the 4 IVs and the 1 DV. If you need to review the R, go to lynda.uni.edu and use the Up and Running with R video tutorial set. 5. Charts for Associations 5.2. Creating scatterplots 4m 15s 6. Statistics for Associations 6.2. Computing a regression 6m 33s 2. Using the AlbumSales.sav SPSS data set and SPSS software, produce the 4 different scatterplots (Each IV with the DV). At the FIT line for the LINEAR best fitting regression line to each of your scatterplot charts. Run the SPSS Regression with the 4 IVs and the 1 DV. You do NOT need to do anything except choose the 4 IVs and the 1 DV. You will just use the DEFAULTS in the Regression dialogue. It is set to ENTER. That is the default. I mentioned STEPWISE and BACKWARD and FORWARD methods in class just to see who has heard of those and to preview what we will get to 8 or so weeks from now, probably sometime in NOVEMBER!
# Introduction to ggplot2 and the mpg dataset (from the qqplot2 library) install.packages("ggplot2") library(ggplot2) # Look at the data from ggplot2 libary that we're going to use - miles per gallon ?mpg head(mpg) str(mpg) names(mpg) # Basic scatterplot qplot(displ, hwy, data = mpg) # Add an additional variable with aesthetics: colour, shape, size qplot(displ, hwy, data = mpg, colour = class) qplot(displ, hwy, data = mpg, colour = cyl) qplot(displ, hwy, data = mpg, shape = factor(cyl)) qplot(displ, hwy, data = mpg, shape = factor(cyl), colour = factor(cyl)) # Add an additional variable with faceting qplot(displ, hwy, data = mpg) qplot(displ, hwy, data = mpg) + facet_grid(. ~ cyl) qplot(displ, hwy, data = mpg) + facet_grid(drv ~ .) qplot(displ, hwy, data = mpg) + facet_grid(drv ~ cyl) qplot(displ, hwy, data = mpg) + facet_wrap(~ class) # Deal with overplotting by using JITTER qplot(cty, hwy, data = mpg) qplot(cty, hwy, data = mpg, geom = "jitter") qplot(cty, hwy, data = mpg, geom = "jitter", colour = year) qplot(cty, hwy, data = mpg, geom = "jitter", colour = class) # Note: On 09/11/Thursday # We did NOT do the following two R qplots # with the added very smooth GEOM method lm (linear model) qplot(cty, hwy, data = mpg) + geom_smooth(method = "lm") qplot(cty, hwy, data = mpg, geom = "jitter", colour = class) + geom_smooth(method = "lm") # Reordering + boxplots qplot(class, hwy, data = mpg) qplot(reorder(class, hwy), hwy, data = mpg) qplot(reorder(class, hwy), hwy, data = mpg, geom = "jitter") qplot(reorder(class, hwy), hwy, data = mpg, geom = "boxplot") qplot(reorder(class, hwy), hwy, data = mpg, geom = c("jitter", "boxplot"))
Crosstabs and CHI SQUARE, recoding variables from interval to ordinal (birthrate and female life expectancy for 15 and for 122 countries), linear regression, scatter plots. The best fitting linear regression line goes through the point that is the mean for the DV and the mean for the IV. DV = dependendent variable = y. IV = independent variable = x.
SPSS Statistics Essential Training with Barton Poulson. This is another lynda.uni.edu resource. (5 hours and 5 minutes).
In this course, author Barton Poulson takes a practical, visual, and non-mathematical approach to the basics of statistical concepts and data analysis in SPSS, the statistical package for business, government, research, and academic organization. From importing spreadsheets to creating regression models to exporting presentation graphics, this course covers all the basics, with an emphasis on clarity, interpretation, communicability, and application.
Up and Running with R with Barton Poulson. (2 hours 25 minutes).
Join author Barton Poulson as he introduces the R statistical processing language, including how to install R on your computer, read data from SPSS and spreadsheets, and use packages for advanced R functions.
The course continues with examples on how to create charts and plots, check statistical assumptions and the reliability of your data, look for data outliers, and use other data analysis tools. Finally, learn how to get charts and tables out of R and share your results with presentations and web pages.