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| #LINES 3-22 ARE EXAMPLES OF BIVARIATE ANALYSIS AND INTERPRETATION FOR 2 CATEGORICAL VARIABLES; LINES 25-49 ARE FOR QUANTITATIVE DEPENDENT AND CATEGORICAL INDEPENDENT. | |
| ####CHI SQUARE for categorical independent variable and categorical dependent variable | |
| #only change the variable names | |
| data.chisq1 <- chisq.test(wave5addhealth$H5OD2A, wave5addhealth$H5HR2) | |
| data.chisq1 | |
| ##INTERPRETATION OF CHI SQUARE: The chi square test of independence shows that there is a statistically significant | |
| # association between sex at birth and living arrangements among adults in the U.S. (chi squared=14.715; p<.05). |
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| library(ggplot2) | |
| library(dplyr) | |
| library(lsr) | |
| library(descr) | |
| library(Hmisc) | |
| library('lehmansociology') | |
| options(scipen = 999) | |
| data(wave5addhealth) | |
| ########DATA MANAGEMENT####### |
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| # Install if not already installed | |
| install.packages("geepack") | |
| # Load package | |
| library(geepack) | |
| # Make sure 'state' is a factor for clustering | |
| df$state <- as.factor(df$state) | |
| # Fit GEE model |
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| library(ggplot2) | |
| library(dplyr) | |
| library(lsr) | |
| library(descr) | |
| library(Hmisc) | |
| library('lehmansociology') | |
| options(scipen = 999) | |
| data(wave5addhealth) |
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| data.aov1 <- aov(gss123$paeduc ~ gss123$race, data=gss123) | |
| summary(data.aov1) | |
| by(gss123$race, gss123$paeduc, mean, na.rm=T) | |
| TukeyHSD(data.aov1) |
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| ##CHANGE VARNAME TO YOUR VARIABLE NAME; CHANGE TITLES AND X-AXIS LABELS | |
| frequency(wave5addhealth$VARNAME, title= "Frequency Distribution of YOUR VARIABLE DESCRIPTION") | |
| #INTERPRET THE RESULT OF THE LINE ABOVE | |
| frequency(wave5addhealth$VARNAME, cumulative.percent=TRUE, title="YOUR VARIABLE DESCRIPTION") | |
| #INTERPRET THE RESULT OF THE LINE ABOVE | |
| ggplot(data=subset(wave5addhealth, !is.na(VARNAME)), aes(x = VARNAME)) + | |
| geom_bar(color="blue", fill="yellow", aes(y = ((..count..)/sum(..count..)))) + | |
| scale_y_continuous(labels = scales::percent) + | |
| ggtitle("Bar Graph of of YOUR VARIABLE DESCRIPTION, Wave 5 Add Health") + |
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| #if you need to recode any of your variable values, you should do it before you label your dummy codes | |
| wave5addhealth$H5HR2[wave5addhealth$H5HR2 == 6] <- 5 | |
| #the line right above this note changes the old dummy code 6 to a dummy code of 5, which combines the two groups. | |
| #if you need to code out missing data (R calls it NA), you would use the example shown in the line below. | |
| wave5addhealth$VARNAME[wave5addhealth$VARNAME == 97] <- NA | |
| #in the line above, the number inside the ] needs to be the dummy code for missing data on your variable. | |
| # the two lines below tell R to treat your categorical variable as a categorical variable (factor) and label the dummy codes |
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| library(ggplot2) | |
| library(dplyr) | |
| library(lsr) | |
| library(descr) | |
| library(Hmisc) | |
| library('lehmansociology') | |
| options(scipen = 999) | |
| data(wave5addhealth) | |
| wave5addhealth<-wave5addhealth[wave5addhealth$H5TO6<97,] |
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| library(ggplot2) | |
| library(dplyr) | |
| library(lsr) | |
| library(descr) | |
| library(Hmisc) | |
| library('lehmansociology') | |
| options(scipen = 999) | |
| data(wave5addhealth) |
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| #Lab Report 5: Making and Comparing Confidence Intervals | |
| library(ggplot2) | |
| library(dplyr) | |
| library(lsr) | |
| library(descr) | |
| library(Hmisc) | |
| library('lehmansociology') | |
| data(gss123) | |
| options(scipen = 999) |
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