Data analysis

Instructions are in the attached file. Link to Statistics Interactives: [login to view URL] (Links to an external site.)Links to an external site. Code for Data Analysis: In addition to the provided code I recommend the following for Part 4: R for Basic Summary Statistics and Visual Displays R for t procedures for Means R Video Tutorial for a t test (Links to an external site.)Links to an external site. In R go to File< New Script (New Document on MAC) – A new script window will pop up. You should write all of your code in a script window and not directly in the console. The R code can be uploaded directly into R by copying and pasting everything below R Code DA3. Note: Any time you see # this means that R will not read what follows. I will use this to make comments about the following command. R Code DA3 # R code and explanation for part 4 DA#3. # Read in the [login to view URL] file. lensdata = [login to view URL]([login to view URL](), header = TRUE) # Look at the lensdata dataset. Look for the variable names. head(lensdata) # gives first 6 rows of the data and variable names. names(lensdata) # gives variable names. # Make an appropriate visual display for lens thickness. # Recall hist() or boxplot() # Add a title. # Add color and other aesthetics if you like. # See week 3 lessons. # Calculate the mean and standard deviation. mean() and sd() # Perform a t test using the [login to view URL]() command. # The format is [login to view URL](data, mu = mu_0, alternative = "alt") # where data is a quantitative variable mu_0 is the hypothesized mean, # and alt is either less, greater or not equal (default). # Code for Part 5. For your Information. # You don't have to run this section. # Upload student data set st314data = [login to view URL]([login to view URL](), header = TRUE) # creates a side by side boxplot with customized axes. boxplot(st314data$GamingHours ~st314data$International, col = c("lightgreen", "dodgerblue"), axes = FALSE, horizontal = TRUE, main = "Comparison of Gaming Hours between International and US ST314 Students", xlab = "Hours per Week") axis(2, at = c(1,2), c("International", "US")) axis(1, at = seq(0,90,5)) # Calculate means, sd and sample size by group aggregate(st314data$GamingHours~st314data$International, data = st314data, mean) aggregate(st314data$GamingHours~st314data$International, data = st314data, sd) aggregate(st314data$GamingHours~st314data$International, data = st314data, length) # two sample t test [login to view URL](st314data$GamingHours~st314data$International)