Question

1. What does the five number tell us about the time spent on email (Hint, interpret the five number summary in plain English) and what does the Boxplot and the normality test show? Explain.
2. Use the 1.5xIQR rule to identify possible outliers. List the cutoff points for outliers, Show your workings. Explain what you found out. (Hint: Are there any excessive time spent on email for Male(1) or Female(2) or both).

GET DATA /TYPE-XLS /FILE='C: \Users\rmanda 1 \ Desktop\homework! . xls' SHEET-name 'gss2004 /CELLRANGE=FULL / READNAMES=ON DATATYPEMIN PERCENTAGE- 95.0 EXECUTE DATASET NAME DataSetl WINDOW-FRONT GRAPH /BAR (SIMPLE )=COUNT BY sex. Graph Notes Output Created Comments Input 16-JAN-2019 12:21:47 Active Dataset Filter Weight Split File N of Rows in Working Data File DataSet1 <none <none <none> 575 Syntax GRAPH BAR(SIMPLE)-COUNT BY sex Resources Processor Time 00:00:01.14 Elapsed Time 00:00:00.77 [DataSet1]

Answer 1

(first part)

There are many descriptive statistics. Numbers such as the mean, median, mode, skewness, kurtosis, standard deviation, first q and third quartile, to name a few, each tell us something about our data.The five-number summary is a set of descriptive statistics of the given frequency distribution that provide information about a dataset. It consists of the five most important sample percentiles:

1. the sample minimum (smallest observation)
2. the lower quartile or first quartile
3. the median (the middle value)
4. the upper quartile or third quartile
5. the sample maximum (largest observation)

(second part) In descriptive statistics, a box plot or boxplot is a method for graphically depicting frequency distribution through their 5 number summary. Box plots may also have lines extending vertically from the boxes indicating variability outside the upper and lower quartiles, Box plots display variation in samples of a statistical population without making any assumptions of the underlying statistical distribution . The spacings between the different parts of the box indicate the degree of spreadness and skewness in the data, and show outliers.

(third part) normality tests are used to determine if a data set is coming from a normal distribution and to compute how likely it is for a random variable underlying the data set to be normally distributed.

(fourth) here the first quartile Q1, third quartile Q3 is not given, it is difficult to find the outlier. also raw data is also not given.

however observation below the Q1-IQR*1.5 and above Q3+1.5*IQR will be known as outlier