Demonstration of Statistical Software

1. Demonstration of Statistical Software Eugene Tseytlin Department of BioMedical Informatics, University of Pittsburgh

2. Overview • Dataset Overview • Descriptive Statistics Using Calc • Descriptive Statistics Using PSPP • Descriptive Statistics Using R • EpiInfo Demonstration Video

3. Brain Size and Intelligence Are the size and weight of your brain indicators of your mental capacity? In this study by Willerman et al. (1991) the researchers use Magnetic Resonance Imaging (MRI) to determine the brain size of the subjects. The researchers take into account gender and body size to draw conclusions about the connection between brain size and intelligence. http://lib.stat.cmu.edu/DASL/Stories/BrainSizeandIntelligence.html Methods • Correlation • Regression • Scatterplot

4. Brain Size and Intelligence Description: Willerman et al. (1991) collected a sample of 40 right-handed Anglo introductory psychology students at a large southwestern university. Subjects took four subtests (Vocabulary, Similarities, Block Design, and Picture Completion) of the Wechsler (1981) Adult Intelligence Scale-Revised. The researchers used Magnetic Resonance Imaging (MRI) to determine the brain size of the subjects. Information about gender and body size (height and weight) are also included. The researchers withheld the weights of two subjects and the height of one subject for reasons of confidentiality.

5. DataGender: Male or FemaleFSIQ: Full Scale IQ scores based on the four Wechsler (1981) subtestsVIQ: Verbal IQ scores based on the four Wechsler (1981) subtestsPIQ: Performance IQ scores based on the four Wechsler (1981) subtestsWeight: body weight in poundsHeight: height in inchesMRI_Count: total pixel Count from the 18 MRI scansNumber of cases: 40 Gender FSIQ VIQ PIQ Weight Height MRI_Count Female 133 132 124 118 64.5 816932 Male 140 150 124 ¥ 72.5 1001121 Male 139 123 150 143 73.3 1038437 Male 133 129 128 172 68.8 965353 Female 137 132 134 147 65.0 951545 Female 99 90 110 146 69.0 928799 Female 138 136 131 138 64.5 991305 Female 92 90 98 175 66.0 854258 Male 89 93 84 134 66.3 904858 Male 133 114 147 172 68.8 955466 Female 132 129 124 118 64.5 833868 Male 141 150 128 151 70.0 1079549 Male 135 129 124 155 69.0 924059 Female 140 120 147 155 70.5 856472 Female 96 100 90 146 66.0 878897 Female 83 71 96 135 68.0 865363 Female 132 132 120 127 68.5 852244 Male 100 96 102 178 73.5 945088 Female 101 112 84 136 66.3 808020 Male 80 77 86 180 70.0 889083 Male 83 83 86 ¥ ¥ 892420 Male 97 107 84 186 76.5 905940 Female 135 129 134 122 62.0 790619 Male 139 145 128 132 68.0 955003 Female 91 86 102 114 63.0 831772 Male 141 145 131 171 72.0 935494 Female 85 90 84 140 68.0 798612 Male 103 96 110 187 77.0 1062462 Female 77 83 72 106 63.0 793549 Female 130 126 124 159 66.5 866662 Female 133 126 132 127 62.5 857782 Male 144 145 137 191 67.0 949589 Male 103 96 110 192 75.5 997925 Male 90 96 86 181 69.0 879987 Female 83 90 81 143 66.5 834344 Female 133 129 128 153 66.5 948066 Male 140 150 124 144 70.5 949395 Female 88 86 94 139 64.5 893983 Male 81 90 74 148 74.0 930016 Male 89 91 89 179 75.5 935863

6. Descriptive Statistics in Calc

7. Load Dataset into Calc Import Dataset Create BMI Column =(weight/(height^2))*703 Categorize BMI Underweight <18.5 Normal < 25 Overweight < 30 Obese > 30 =IF(BMI<18.5,”Underweight”, IF(BMI<25,”Normal”, IF(BMI<30,”Overweight”,”Obese”)))

8. Use DataPilot Feature Data → DataPilot → Start..

9. Charts and Graphs • Bar chart of Male IQ vs Female IQ • XY Scatter Plot of IQ vs MRI pixel count • XY Scatter Plot of Weight vs Height

10. Descriptive Statistics Using R

11. Processing Data in R Import Data > data =read.table("brain-size.csv",1,"\t"); Add BMI Data Columns > data$bmi=data$Weight/(data$Height^2)*703;

12. Descriptive Statistics in R Mean IQ > mean(data$FSIQ) [1] 113.45 Standard Deviation of IQ > sd(data$FSIQ) [1] 24.08207 Summary > summary(data$FSIQ) Min. 1st Qu. Median Mean 3rd Qu. Max. 77.00 89.75 116.50 113.40 135.50 144.00

13. T-Test in R > t.test(data$FSIQ[data$Gender=="Female"],data$FSIQ[data$Gender=="Male"]) Welch Two Sample t-test data: data$FSIQ[data$Gender == "Female"] and data$FSIQ[data$Gender == "Male"] t = -0.4027, df = 37.892, p-value = 0.6895 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -18.68639 12.48639 sample estimates: mean of x mean of y 111.9 115.0

14. Correlation in R > cor.test(data$Weight,data$Height) Pearson's product-moment correlation data: data$Weight and data$Height t = 5.8748, df = 36, p-value = 1.021e-06 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: 0.4893837 0.8329941 sample estimates: cor 0.699614

15. Plots in R XY Scatter Plot between Verbal IQ and Total IQ > plot(data$FSIQ,data$VIQ)

16. Charts in R Bar Graph of Means of Male vs Female Iqs >barplot(c(mean(data$FSIQ[data$Gender=="Female"]),mean(data$FSIQ[data$Gender=="Male"])),names.arg=levels(data$Gender))

17. PSPP

18. Descriptive Statistics in PSPP Analyze → Descriptive Statistics → Descriptives

19. Tests in PSPP Independent Sample T-Test Analyze → Compare Means → Independent Sample T Test

20. Conclusion There are many open source software packages for statistical analysis While some packages are completely analogous to their respective non-free alternatives, others are still work in progress The important thing is to know what is out there For rest there is always Google.