a. (1 point) Open a do file and name it DataAnalysis-1.
b. (1 point) Open a log file and name it DA-1.
c. (1 point) Enter the data for each of the four variables. For the three categorical variables, create a numeric code for each response. For example, for TV source, 1 = Amazon Prime, 2 = Cable, 3 = Hulu Plus, 4 = iTunes, and 5 = Netflix.
(Instruction: open “Data Editor” from the menu on top of the results pane, and directly enter the data into the cells. You do not need to include any commands in the do file for part a).
d. (1 point) Once you have entered the data, rename each variable.
e. (1 point) Give each variable a variable label.
f. (1 point) Give each numeric code a value label.
g. (1 point) Save your data file and name it DA1-TV.
h. (1 point) What percentages of the sample identify as males and females?
i. (1 point) What type of TV service is used most frequently?
j. (1 point) What percentage of students binge watch TV frequently?
k. (1 point) Identify both the variable type and scale of measurement for each of the four variables (TV Source, Hours per week, Binge frequency, and Sex) in your dataset.
l. (1 point) What is the mod for “Binge Frequency”?
m. (1 point) .
n. (1 point) What is the variance for “Hours per week”?
o. (1 point) Make a table that shows “Sex” in the rows and the mean and median of “Hours per week” in the columns.
p. (1 point) Generate a cross tabulation of “Sex” and “Binge frequency”. Be sure to think about whether the rows or columns should add up to 100%. Based on your table, what percentage of women binge watch frequently and what percentage of men binge watch frequently?
q. (3 point) Generate a bar chart that shows the average “Hours per week” that men and women binge watch TV. Explain what this means.
r. (3 point) Generate a histogram of “Hours per week”. Explain what this means.
s. (3 point) Generate a pie chart of “Binge frequency”. Label each slice of the chart with the percentage value. Explain what this means.
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