Question

In: Statistics and Probability

. Descriptive information and graphical displays of the top three reasons coming to the university (use...

.

Descriptive information and graphical displays of the top three reasons coming to the university (use Statcrunch)

[GO to Stat, Tables, Frequency, Select columns (use SHIFT Key to choose the block of variables).

Select Statistics: Choose all five (use Ctrl key), Compute]. You see the output. Look for the top three reasons to report in (a).

Use table to summarize the frequency of ‘reasons’ for choosing the university. What are the top three reasons:

      [1: Yes, and 0: No]

Top

Reason

Frequency out of 200 students?

%

1

2

3

For the top reason you found in (a), fill the follow table to find out the frequency and % of males and females students chose this top reason:

[GO to Stat, Tables, Frequency, select the Top Reason Column, Group by ‘Gender’, Compute]. Report your results in (b)

Top Reason

Yes (give frequency and % )

No (frequency and %)

Total (frequency)

Female

Male

Total (Frequency)

Draw a Bar Graph for the Reason ‘Right_Distance’ for Female and Male, separately, but, on the same graph. Copy and Paste the graph here.

[Graph, Bar Plot, With Data, Select Column ‘Right Distance’, Group by ‘Gender’ , Grouping options: Split bars’, Type: Relative Frequency’, Check Display Value above bar, Check ‘Use Same Y-axis’, Compute]. On the resulting plot, left-upper corner, click on options to ‘Copy’ the graph. Ctrl+Alt+V, choose ‘Device Independent Bitmap, Ok.]

How many % of females choose ‘Right_Distance (‘Yes’ (=1) ) as their reason: _________________

How many % of males chooses ’Right_distance’ as their reason: _______________

Draw a Pie graph for the ‘Grade’ variable to see the % of students in each grade level.

Copy and paste the graph here.

[Graph, Pie Chart, With data, Select Gender, Display both Count and Percent of Total, Compute]. Copy and paste the chart below.]

What is the % of Senior students in this data? ____________________

Prior to answer the remaining questions, check if there are unusual distance data by conducting a Dot Plot of the variable ‘Miles’.

You should notice there are some cases with distance over 3000 miles. It is clear they are international students.

Let’s copy and paste the ‘Miles’ into a new column and delete the Miles over ‘1000’ miles. The new variable is named as ‘Miles_1’.

[Use StatCrunch to create Miles_1: Go to Edit, Columns, Duplicate, Select column: Miles, Compute. On the data sheet (move the cursor to the most right column: DUP(Miles), Change the name to Miles_1, Go through the data values of Miles_1 variable to delete the Miles_1 > 1000. (Note: after the clean up, the maximum is 996).]

Construct a histogram of the variable Miles_1 for male and female, separately, on the same graph in different panels and paste them here.

[Go to Graph, Histogram, Select column Miles_1, Group by ‘Gender’, Choose the Type: Relative Frequency, Check ‘Value above bar’, For multiple graph: Columns per page: 2, check ‘Use same Y-axis’. Compute]. To copy and paste the graph: Click on ‘Options’, Copy, Right-click , Copy Image. Move cursor to your Word File: Ctrl+Alt+V , Choose Device Independent Bitmap, OK to paste the image.

Compare the shapes of the distributions for the Miles_1 between Female and Male students:

Which one is more skewed (Female or Male distribution): ________________

(c ) Which distribution (Male or Female) of Miles_1 shows larger variation: _________________

User_type Gender Grade Miles Region U_size Area Right_Distance Expense Reputation Friends Scholarship Friendly Size Small_Community Right_University In_State Recommendation Alumni
student female sophomore 140 mw 20000_30000 rural 1 1 1 0 1 0 0 0 0 0 0 0
student female freshman 57 mw 10000_20000 rural 1 0 0 0 0 1 1 0 0 1 0 0
student male sophomore 72 mw 20000_30000 rural 1 1 1 1 0 1 0 0 1 0 0 0
student female junior 275 mw 10000_20000 rural 0 0 0 0 1 1 1 0 0 0 0 0
student male junior 100 mw 20000_30000 rural 1 0 0 0 0 1 0 0 0 0 1 0
student male junior 125 mw 20000_30000 rural 1 0 1 0 0 0 0 0 0 0 1 0
student male junior 200 se 10000_20000 rural 1 1 1 0 0 1 1 0 0 0 0 0
student female sophomore 123 mw 20000_30000 rural 1 0 0 0 0 0 0 0 0 0 0 0
student female senior 150 mw 10000_20000 rural 1 0 0 0 0 1 1 0 1 1 0 0
student male junior 65 mw 10000_20000 rural 1 0 0 0 1 0 1 0 0 0 1 0
student male sophomore 170 mw 20000_30000 rural 1 0 0 0 0 1 1 0 0 0 1 1
student male freshman 120 wc 10000_20000 rural 1 0 1 0 0 1 0 0 0 1 1 0
student male sophomore 375 mw 10000_20000 rural 1 0 0 0 1 0 0 0 0 0 0 0
student male junior 10 mw 10000_20000 rural 0 1 0 0 0 0 0 0 0 0 1 1
student male sophomore 62 mw 10000_20000 rural 1 0 0 0 0 0 0 0 0 0 0 0
student female graduate 20 mw 20000_30000 rural 1 0 0 1 0 0 0 0 0 1 0 0
student female senior 142 mw 20000_30000 rural 0 0 0 0 1 0 1 0 0 0 0 1
student male junior 151 mw 20000_30000 rural 1 0 1 0 0 0 0 0 0 0 0 0
student female sophomore 200 mw 10000_20000 rural 0 0 1 1 0 1 1 0 0 0 0 0
student male junior 132 mw 20000_30000 rural 0 0 1 0 0 1 0 0 0 0 1 1
student female junior 41.6 mw 10000_20000 rural 1 0 1 0 0 0 0 0 0 0 0 1
student female other 200 se 20000_30000 rural 1 1 1 0 0 1 1 0 0 0 1 0
student female senior 33 mw 20000_30000 rural 1 0 1 0 0 0 0 0 0 1 0 1
student male sophomore 20 mw 10000_20000 rural 1 0 0 1 1 0 0 0 0 1 0 1
student male sophomore 328 ne 10000_20000 urban 0 0 0 0 0 0 0 0 0 0 1 0
student male freshman 9000 mw 20000_30000 rural 0 0 0 1 0 0 0 0 0 0 0 0
student female senior 130 se 10000_20000 urban 0 1 1 0 0 0 0 0 0 1 0 0
student female freshman 180 mw 20000_30000 rural 0 0 1 0 1 1 1 0 0 0 0 1
student male sophomore 40 mw 10000_20000 rural 0 0 1 1 0 1 1 0 0 1 1 0
student female junior 100 mw 10000_20000 rural 1 1 1 1 1 1 0 0 0 0 1 0
student female sophomore 210 mw 20000_30000 rural 0 0 1 0 0 0 0 0 0 1 1 0
student male junior 200 mw 20000_30000 rural 0 0 1 0 0 1 0 0 0 1 1 0
student male junior 100 ne 10000_20000 urban 1 0 0 0 1 0 0 0 0 0 0 0
student male senior 150 mw 20000_30000 rural 0 1 0 0 0 0 0 0 0 1 0 1
student male senior 103 mw 20000_30000 rural 0 0 1 0 0 0 0 0 0 0 0 0
student male sophomore 143 mw 20000_30000 rural 1 0 0 0 0 0 0 0 1 1 0 0
student female junior 550 mw 10000_20000 rural 0 0 1 0 0 0 0 0 0 0 0 0
student male sophomore 140 mw 10000_20000 rural 0 0 0 1 0 0 1 0 0 0 0 1
student male graduate 136 mw 10000_20000 rural 0 0 0 0 0 0 0 0 0 0 0 1
student female freshman 171 mw 20000_30000 rural 1 0 0 0 1 0 0 0 0 0 0 0
student male graduate 4.1 mw 20000_30000 rural 1 0 1 0 0 1 1 1 0 1 0 0
student male senior 8 mw 10000_20000 urban 1 0 0 0 0 0 1 1 0 1 0 0
student male junior 200 mw 20000_30000 rural 1 1 1 1 0 0 1 1 1 0 0 1
student male junior 140 mw 10000_20000 rural 1 0 0 1 0 1 1 0 0 1 0 0
student female sophomore 130 mw 10000_20000 rural 1 0 0 0 0 0 1 0 0 0 0 0
student male junior 65 mw 20000_30000 rural 0 1 1 0 0 0 0 0 0 1 0 0
student male freshman 137 mw 10000_20000 rural 1 0 1 0 0 0 0 0 1 0 0 0
student male junior 140 mw 20000_30000 rural 1 0 0 0 0 0 0 0 0 0 0 0
student male junior 10 mw 10000_20000 rural 0 0 1 0 1 0 0 0 0 0 0 1
student female junior 100 mw 10000_20000 rural 0 1 0 0 0 1 1 1 0 0 0 0
student female senior 150 mw 10000_20000 rural 0 0 1 0 1 0 0 0 0 0 0 0
student male freshman 8.7 mw 20000_30000 rural 0 0 0 0 1 0 0 0 0 0 0 0
student male junior 200 mw 10000_20000 null 1 0 1 1 0 1 1 0 1 1 0 0
student male sophomore 50 mw 10000_20000 rural 1 0 0 0 1 0 0 0 0 0 0 0
student male junior 120 mw 20000_30000 rural 0 1 1 0 0 0 1 1 1 0 0 1
student female sophomore 56 mw 10000_20000 rural 1 0 0 0 0 0 0 0 1 0 0 1
student male senior 2 ne 20000_30000 rural 0 0 0 0 0 0 0 0 0 1 0 0
student female junior 170 mw 10000_20000 rural 1 1 0 0 1 0 0 0 0 0 0 0
student male freshman 133 mw 20000_30000 rural 1 1 0 0 1 0 1 0 0 0 0 0
student male sophomore 125 mw 20000_30000 rural 1 0 0 0 0 0 1 1 0 0 0 0
student male junior 163 ne 10000_20000 rural 1 0 0 0 0 0 0 0 0 0 0 1
student male junior 90 mw 20000_30000 rural 1 1 0 0 0 0 1 1 1 0 1 1
instructor female senior 150 mw 20000_30000 rural 0 0 0 0 1 0 1 0 0 0 0 0
student male senior 45 mw 10000_20000 rural 1 0 0 0 0 0 0 0 0 1 0 0
student female sophomore 139 mw 20000_30000 rural 1 0 0 1 1 1 0 0 0 1 0 0
student male sophomore 160 mw 20000_30000 rural 1 0 0 1 1 0 1 0 0 0 0 0
student male sophomore 100 mw 10000_20000 rural 1 0 0 1 0 0 0 0 1 0 0 0
student male sophomore 50 mw 10000_20000 rural 0 0 0 0 1 0 0 0 0 0 0 0
student male graduate 115 mw 10000_20000 rural 0 0 0 1 0 0 0 0 0 0 0 1
student male senior 30 mw 20000_30000 rural 0 0 1 0 0 0 0 0 0 0 0 0
student male junior 40 mw 20000_30000 rural 0 0 0 0 1 0 0 0 0 0 0 0
student male junior 0 mw 20000_30000 rural 0 0 0 1 0 0 0 0 1 1 0 0
student male junior 2100 mw 20000_30000 rural 0 0 0 0 1 0 0 0 0 0 0 0
student female junior 45 mw 10000_20000 rural 1 0 0 1 1 1 1 1 0 0 0 1
student male junior 8 mw 10000_20000 rural 0 1 1 0 1 1 1 0 1 0 0 0
student female senior 125 mw 20000_30000 rural 1 1 0 1 1 0 1 0 0 1 0 0
student male graduate 19 mw 20000_30000 rural 0 0 1 1 0 1 1 0 0 1 0 1
student female junior 110 mw 10000_20000 rural 1 0 1 0 0 0 0 0 0 1 0 0
student female junior 60 ne 20000_30000 rural 1 0 0 0 1 0 1 0 0 1 0 1
student male junior 85 mw 20000_30000 urban 1 1 1 1 0 1 0 0 1 1 0 0
student female sophomore 72 mw 20000_30000 rural 1 1 0 0 1 1 0 0 0 1 0 0
student male graduate 145 mw 20000_30000 rural 0 0 1 1 1 1 1 1 0 0 0 0
instructor male other 50 mw 20000_30000 rural 0 0 0 0 0 0 0 0 0 0 0 0
student female sophomore 9999.99 mw 20000_30000 rural 0 0 0 0 1 0 0 0 0 0 0 0
student female junior 15 mw 20000_30000 rural 0 0 0 0 0 0 0 0 0 0 0 1
student male sophomore 145 mw 10000_20000 rural 1 0 0 0 0 0 1 0 1 0 0 0
student male sophomore 155 mw 20000_30000 rural 1 1 1 1 0 0 1 0 0 1 0 1
student female other 125 mw 20000_30000 rural 0 0 1 0 0 0 0 0 0 0 0 0
student male sophomore 120 mw 10000_20000 rural 1 0 1 0 0 1 0 0 1 0 0 0
student male junior 160 mw 20000_30000 rural 1 1 1 0 0 1 1 0 0 0 0 0
student male sophomore 130 mw 10000_20000 rural 1 1 1 1 0 1 1 1 0 0 1 1
student female senior 0 mw 20000_30000 rural 0 0 0 0 0 0 0 0 0 0 0 0
student female freshman 120 mw 20000_30000 rural 1 0 0 1 1 1 1 0 0 1 0 1
student male freshman 170 mw 20000_30000 urban 1 0 0 0 1 1 0 0 0 1 0 0
student male junior 5 mw 10000_20000 rural 0 1 0 1 1 0 0 0 0 1 0 0
student male junior 120 mw 20000_30000 rural 1 1 0 0 0 0 1 1 0 1 0 0
student female junior 150 mw 10000_20000 rural 1 1 0 1 0 1 1 1 1 0 0 0
student male sophomore 70 mw 20000_30000 rural 0 1 1 1 0 0 1 0 0 0 0 0
student female junior 86.8 mw 10000_20000 rural 1 1 1 0 1 1 1 0 0 1 0 0
student female sophomore 105 mw 10000_20000 rural 0 0 0 0 1 0 0 0 0 0 0 0
student female freshman 8000 ne 5000_10000 rural 0 0 0 0 0 0 1 0 0 0 0 0
student male senior 996 se 20000_30000 rural 0 1 1 0 0 0 0 0 0 0 0 1
student male freshman 80 mw 20000_30000 rural 1 1 1 0 1 1 0 0 0 0 0 0
student female sophomore 124 mw 20000_30000 rural 1 1 1 0 1 1 0 0 0 0 0 0
student male senior 155 mw 20000_30000 rural 0 0 0 0 0 0 1 1 0 0 0 0
student male junior 50 mw 20000_30000 urban 1 0 1 1 0 1 0 0 0 1 1 0
student female sophomore 70 mw 10000_20000 urban 0 0 1 1 1 1 1 1 0 1 0 0
student male senior 55 mw 10000_20000 rural 0 1 0 0 0 0 1 0 1 1 0 0
student male sophomore 150 mw 20000_30000 rural 1 1 1 1 1 1 0 0 0 1 0 1
student female senior 80 mw 20000_30000 rural 1 1 1 0 0 0 1 1 0 1 0 0
student male junior 112 mw 20000_30000 rural 1 1 1 1 0 0 0 0 0 0 0 0
student female senior 150 mw 20000_30000 rural 1 0 1 0 0 1 1 1 0 1 0 0
student female sophomore 160 ne 10000_20000 rural 1 1 1 0 1 1 1 0 0 1 0 0
student male freshman 168 mw 20000_30000 rural 0 0 0 0 0 0 0 0 0 0 0 0
student female freshman 67.7 mw 20000_30000 rural 0 1 1 0 0 1 0 1 0 0 0 0
student male junior 125 mw 10000_20000 rural 0 0 1 0 0 0 0 0 0 0 1 0
student male freshman 9999.99 mw 20000_30000 rural 0 1 0 0 1 1 0 0 1 0 0 0
student male sophomore 113 so 20000_30000 rural 1 0 1 1 1 1 0 0 0 0 0 0
student male sophomore 120 mw 10000_20000 rural 1 0 0 0 0 1 1 0 0 0 1 1
student male sophomore 2184 wc 20000_30000 rural 0 0 0 1 0 1 0 0 1 0 0 0
student female junior 153 se 10000_20000 rural 1 0 0 1 0 1 1 0 0 1 0 0
student female sophomore 55 ne 10000_20000 rural 1 1 1 1 1 0 1 1 0 1 1 0
student female sophomore 2 mw 10000_20000 rural 0 0 1 0 0 1 0 0 0 0 1 0
student male senior 25 mw 10000_20000 rural 1 0 0 0 0 0 1 0 0 0 0 0
student male junior 22 ne 20000_30000 rural 1 1 1 0 0 1 0 0 0 1 1 0
student male senior 150 se 10000_20000 rural 0 1 1 0 0 0 0 0 0 0 0 0
student male junior 73 mw 10000_20000 rural 0 1 0 0 1 1 1 1 0 0 0 1
student male senior 65 mw 20000_30000 rural 0 1 0 0 0 0 1 1 0 0 0 0
student female freshman 3 mw 10000_20000 rural 0 0 0 0 0 0 0 0 0 0 0 0
student male junior 100 null 20000_30000 rural 1 0 0 0 0 0 0 0 1 0 0 0
student male junior 90 mw 20000_30000 rural 1 1 0 1 0 0 0 0 0 1 0 0
student female senior 30 mw 20000_30000 rural 0 0 0 0 0 0 0 0 0 0 1 0
student female sophomore 238 mw 20000_30000 rural 1 0 0 0 0 0 0 0 1 0 0 1
student male freshman 9000 ne 20000_30000 rural 0 0 0 0 0 0 1 1 0 0 0 0
student male senior 110 so 10000_20000 rural 0 0 0 1 0 1 1 0 0 1 0 1
student female freshman 45 mw 20000_30000 rural 1 0 1 0 1 1 0 0 0 1 0 0
student male junior 155 mw 20000_30000 rural 1 1 0 1 0 1 1 1 0 0 1 0
student female senior 15 mw 10000_20000 rural 0 1 0 0 1 1 0 1 0 1 0 1
student male sophomore 20 mw 5000_10000 rural 1 0 1 1 0 0 0 0 1 0 0 0
student male graduate 5 mw 20000_30000 rural 1 0 1 0 0 1 0 0 0 0 0 0
student female graduate 1.5 mw 20000_30000 rural 0 0 0 0 0 0 1 0 0 0 0 0
student female graduate 12 mw 20000_30000 rural 0 1 0 0 0 0 0 0 0 0 0 0
student female sophomore 140 mw 10000_20000 rural 1 1 1 0 1 1 1 0 0 0 1 0
student male sophomore 61 mw 10000_20000 rural 1 1 0 0 0 0 1 1 0 0 0 1
student female senior 47 mw 10000_20000 rural 0 0 0 0 1 0 0 0 0 0 0 0
student male senior 132 mw 20000_30000 rural 0 0 0 0 0 0 1 0 0 0 0 0
student male sophomore 130 mw 20000_30000 rural 1 1 1 1 0 1 0 1 0 0 1 0
student male junior 120 mw 10000_20000 rural 0 0 1 1 0 0 0 0 0 0 0 0
student male junior 45 mw 20000_30000 urban 1 0 0 0 1 0 0 0 0 0 0 0
student male junior 35 mw 20000_30000 rural 0 1 1 0 1 1 0 0 0 0 1 0
student female junior 85 ne 10000_20000 rural 1 1 0 1 0 0 0 0 0 1 0 0
student male junior 110 ne 10000_20000 rural 0 1 0 0 1 0 0 0 0 0 0 0
student female sophomore 110 mw 10000_20000 rural 1 0 0 0 0 0 0 0 0 0 0 0
student female junior 100 mw 10000_20000 rural 1 1 1 0 1 0 0 0 0 1 0 1
student female junior 110 mw 20000_30000 rural 1 1 0 0 0 1 0 0 0 0 0 0
student female junior 105 mw 20000_30000 rural 1 0 0 0 1 1 0 0 0 1 1 0
student male sophomore 120 mw 20000_30000 rural 0 1 0 1 0 0 0 0 0 0 0 0
student male junior 8 mw 20000_30000 rural 0 1 0 0 0 0 0 0 0 0 0 0
student male senior 1 mw 20000_30000 rural 0 0 1 1 0 1 0 0 0 0 1 1
student male senior 250 mw 10000_20000 rural 1 1 0 0 1 1 0 0 0 0 0 0
student male senior 40 ne 20000_30000 rural 1 0 1 1 1 0 1 0 0 0 0 1
student male senior 65 mw 20000_30000 rural 0 1 0 0 0 0 1 0 0 0 0 0
student male senior 12 se 5000_10000 rural 0 1 1 0 0 1 0 1 0 1 1 0
student male senior 160 mw 20000_30000 rural 1 1 1 0 1 0 0 0 0 0 0 0
student female junior 122 mw 20000_30000 rural 1 0 0 0 1 0 0 0 0 0 0 0
student male senior 130 mw 20000_30000 rural 0 1 1 1 0 1 1 1 0 1 0 0
student female sophomore 250 mw 10000_20000 rural 0 0 1 0 0 1 0 0 0 0 1 1
student female freshman 6 mw 20000_30000 urban 1 0 0 0 0 0 0 0 0 0 0 1
student male sophomore 130 mw 20000_30000 rural 1 1 0 0 0 1 1 0 0 0 0 0
student male sophomore 130 mw 10000_20000 rural 0 0 0 0 0 0 1 0 0 0 0 0
student male sophomore 154 mw 20000_30000 rural 1 1 1 1 1 1 0 0 0 0 1 0
student female sophomore 150 mw 20000_30000 rural 1 0 0 1 1 1 1 0 0 0 1 1
student male junior 100 mw 20000_30000 rural 1 0 1 0 1 1 0 0 0 0 0 0
student male junior 280 mw 10000_20000 rural 0 0 0 0 0 0 1 0 0 0 0 0
student male sophomore 121 mw 20000_30000 rural 1 0 1 0 0 1 0 0 0 1 0 0
student female junior 60 ne 20000_30000 rural 1 0 0 0 0 0 0 0 1 0 0 0
student male junior 150 mw 20000_30000 rural 1 1 0 0 0 0 0 0 0 0 0 0
student male junior 5 ne 10000_20000 urban 0 0 0 0 0 0 0 0 0 0 1 0
student female sophomore 93 mw 10000_20000 rural 0 0 1 0 1 0 0 0 0 1 0 0
student female junior 26 mw 20000_30000 rural 1 0 1 0 1 0 0 0 0 0 0 1
student male senior 65 mw 10000_20000 rural 1 0 0 0 0 0 0 0 0 0 0 1
student male sophomore 155 mw 20000_30000 rural 1 0 0 0 0 1 1 0 1 1 1 0
student female junior 160 se 20000_30000 rural 1 1 1 0 0 0 1 0 0 0 0 0
student male senior 30 mw 20000_30000 rural 1 0 0 0 1 0 0 0 0 0 0 0
student male sophomore 100 mw 10000_20000 rural 1 0 1 0 0 0 1 0 0 1 1 0
student male sophomore 60 mw 10000_20000 rural 0 0 1 1 0 0 0 0 0 0 0 1
student female sophomore 150 mw 10000_20000 rural 1 1 1 1 0 0 0 0 0 0 0 0
student female sophomore 2 mw 20000_30000 rural 0 0 1 1 0 0 0 0 0 1 0 1
student female junior 0 mw 10000_20000 rural 0 0 0 1 0 0 0 0 0 1 0 1
student male sophomore 100 mw 20000_30000 urban 1 1 1 0 1 1 1 0 0 0 0 0
student female senior 63 ne 20000_30000 rural 1 1 0 0 1 0 1 0 0 0 0 0
student male junior 90 mw 20000_30000 rural 1 1 0 0 0 0 1 1 0 1 0 0
student female junior 160 mw 20000_30000 rural 1 0 1 0 0 0 1 0 0 0 0 0
student male senior 100 mw 10000_20000 rural 1 1 0 1 0 1 0 0 0 0 0 1
student female senior 143 mw 20000_30000 rural 0 0 0 0 0 1 1 0 0 0 0 0
student female sophomore 85 mw 20000_30000 rural 1 0 0 0 0 0 0 0 0 0 0 1
student female senior 50 ne 20000_30000 urban 1 0 1 1 0 0 0 0 0 0 0 0
student female graduate 15 mw 10000_20000 rural 1 1 1 1 1 1 1 0 0 1 0 0
student female senior 1490 so 20000_30000 rural 0 0 0 0 1 0 0 0 0 0 0 0
student male sophomore 75 mw 20000_30000 rural 1 1 0 0 1 0 1 0 0 1 1 0

Solutions

Expert Solution

This problem is done with the help of SPSS

a)The 3 top reasons for students coming to the university

Top Reason Frequency out of 200 students %
1 Right Distance 116 58%
2 Reeputation 85 42.5
3 Size 77 38.5

b)For the top reasons found in a), the frequency of male and female students are,

Right Distance
Yes(frequency and %) No(frequency and %) Total(frequency)
Male(Frequency) 68 (56.2%) 53 (43.8%) 121
Female(Frequency) 48 (60.8%) 31 (39.2%) 79
Total(frequency) 116 84 200
Reputation
Yes(frequency and %) No(frequency and %) Total(frequency)
Male(Frequency) 51 (42.1%) 70 (57.9%) 121
Female(Frequency) 34 (43%) 45 (57%) 79
Total(frequency) 85 115 200
Size
Yes(frequency and %) No(frequency and %) Total(frequency)
Male(Frequency) 46 (38%) 75 (62%) 121
Female(Frequency) 31 (39.2%) 48 (60.8%) 79
Total(frequency) 77 123 200

c)Barplot

How many % of females choose ‘Right_Distance (‘Yes’ (=1) ) as their reason: ____60.8%__________

How many % of males chooses ’Right_distance’ as their reason: _______56.2%________

d)Pie Chart


What is the % of Senior students in this data? ____20.5%______________

Dot Plot of the variable Miles

Histogram of new variable Mile_1

Male distribution is more skewed

Male distribution of Miles_1 shows larger variation

orchestra answered 2 years ago
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