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2 - Clear Mountain State University (CMSU) Survey...
2.4Problem 2.1
2.2Problem 2.2.
23Problem 23.
24Problem 2.4.
25Problem 2.5.
26Problem 2.6.
2:7Problem 2.7.
28Problem 2.8.
3~ Hypothesis Testing for Quality of Shingles. 13
3.1.Problem 3.1 14
3.2Problem 3.2.
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‘smdm Weekly Quiz 3 Weekly Quizi_ Weekly Quiz3
Several items in their stores. across different regions and channels. The data consists of 440 large
retailers’ annual spending on 6 different varieties of products in 3 different regions (Lisbon, Oporto, Other)
and across different sales channel (Hotel, Retal)
‘Summary-This business report provides detailed explanation of approach to each problem given in the
‘assignment and provides relative information with regards to solving the problem.
— Wholesale Customer Data Analysis
We imported the ‘Wholesale Customer data’ dataset in python to analyze the spend under each store
items across regions and channel to find solutions to each problem. Below is the detailed approach and
answer.
4.1Problem 1.1.1. Use methods of descriptive statistics to summarize data
count unique top freq mean std_ min 25% 50% 75% max
BuyeriSpender 440 NaN NaN NaN 2205 127161 1 11075 2205 30025 440
Channet 440-2 Hotel 208 «NaN NON GN NSN NON NaN NaN
Region 4403 Other 318 NaN NaN NQN NaN ONBN NaN NaN
Fresh 440 NaN NaN NaN 120003 126473 3 312775 9504 16938 112151
Mik 440 NaN NaN NaN 5706.27 738038 55 15333627 719025 T3498
Grocery 440 NaN NAN NaN 795128 950316 3215347555 106558 92780
Frozen 440 NeN NaN NaN 307193 405467 25 74225 1526 355425 60860
Detergents_Paper 440 NaN NaN NaN 290149 476785 3 25675 8165 302240027
Delicatessen 440 NaN NaN NaN 152487 282011 3 40825 9655 182025 47043
x
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Channel
Hotel so 28 244
Retail as a9 aes
4.4Problem 1.1.2. & 1.1.3 Which region and channel spend most & least?
Solution:
Using describe function in python we first looked at the basic descriptive statistics of the data set. Using
bar graph with Region and Channel we were able to identify region with maximum spend and minimum
‘spend. Below is the bar graph representation-Looking at the bar graph, Hotel Channel spends more and
Retail spends least.
‘+ Highest spend in the Region is from Others and lowest spend in the region is from Oporto
‘+ Highest spend in the Channelis from Hotel and lowest spendin the Channel is from Retail
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{sate spendin ne Regonis ta Oh nd loves spent te ego om Ort het pend he hanes el ard ved send nthe
Similarly, we grouped totals by region to get totals by region,
Other regions spend amount is the highest spend amount and
Oporto region spend amounts least spend amount by Region,
1.2Problem 1.2 There are 6 different varieties of items are considered. Do all varieties show
similar behavior across Region and Channel? Provide justification for your answer.
Solution:
Using pivot tables for each category and checking spend across Region and Channel we get the following
outputs -
x
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aoe LLL Gan
Looking at the above tables, we see that some categories like Milk, Grocery & Detergents Paperhave
higher spend in the Retail channel versus Hotel, across all ragions. On the other hand, Fresh and Frozen
hhave higher consumption in the Hotel channel versus Retail, across all regions. Also, if we plot a box plot
we can summarize that the spend for Fresh and groceries is the maximum across region and channel
hile for Delicatessen itis the least across region and channel. The output boxplotis below —
rroon | Hi} —awwnee oe .
van) Hf tomen oe ve .
cxocory} Hff}-—temerers 6 oe .
Frozen f}Hmeae « .
Detergents_Paver] [fp +e +
Detcatessen | fae o ’
© 20000-40000 60000-60000 +1000
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| 1.3 Problem On the basis of the descriptive measure of variability, which item shows the most
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1.4 Problem Are there any outliers in the data?
Solution:
To find out outliers we ploted boxplot and the output gives the detals that in all the data there are outers
x
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| 1.5 Problem On the basis of your analysis, what are your recommendations for the business? How
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Problem 2-
‘The Student News Service at Clear Mountain State University (CMSU) has decided to gather data about
the undergraduate students that attend CMSU. CMSU creates and distributes a survey of 14 questions
and receives responses from 62 undergraduates.
‘Summary-This business report provides detailed explanation of approach to each problem given in the
assignment and provides relative information with regards to solving the problem.
2 CMSU Survey Data Analysis
We imported the ‘CMSU Survey-1' dataset in python to analyze the data about the undergraduate students
who attend CMSU. Below is the detailed approach and answer.
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| 2.1Problem. For this data, construct the following contingency tables (Keep Gender as row
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2.1Problem 2.1.2 Gender and Grad Intention
Solution
Below isthe output from Python
Grad intention No Undecided Yes
Gender
Female as "
Male 39 7
2.1Problem 2.1.3 Gender and Employment
Solution
Employment Ful-Time Part-Time Unemployed
Gender
Female 3 24 6
Male 7 19 3
2.1Problem 2.1.4 Gender and Computer
Solution
Below isthe output from Python
Computer Desktop Laptop Tablet
Gender
Female 2 29 2
Male 3 26 °
x
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| 2.2Problem. Assume that the sample is representative of the population of CMSU. Based on the
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cata, answer tne rollawing question:
2.3 Problem 2.3.1 Find the conditional probability of different majors among the male students in
osu.
Solution:
Using contingency tables of Gender and Majors we got the total numbers of males and number of males
‘opting for different majors
Below is the output from Python —
Probability of Males opting for Accounting is 13.79%
Probability of Males opting for CIS. is 3.45%
Probability of Males opting for Economies/Finance. is 13.79%
Probability of Males opting for internationalBusiness. is 6.90%
Probability of Males opting for Management. is 20.69%
Probability of Males opting for Other. is 13.79%
Probability of Males opting for Retailing/Marketing is 17.24%
Probability of Males opting for Undecided. is 10.34%
‘And from this output we can easily say that mostof the males students prefer Management as Majors and CIS
's the least preferred one
2.3 Problem 2.3.2 Find the conditional probability of different majors among the female students
in CMSU,
Solution:
Using contingency tables of Gender and Majors we got the total numbers of females and number of females
‘opting for different majors
Below is the output from Python —
x
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Probability of Females opting for Accounting. is 9.09%
y— TF FTF FTF FF YF
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Using conungency tables of Gender ana Grad intention we got ine total numbers of males anc nuMBEr oF
‘males intends to be graduate
‘And post calculation we find out that - Probability of Males and intends to be Graduate. is 58.62%
2.4Problem 2.4.2 Find the probability that a randomly selected student is a female and does NOT
have a laptop.
Solution:
Using contingency tables of Gender and Computer we got the total numbers of females and number of
females does not have a laptop
‘And post calculation we find out that - Probabilty of randomly selected student is a Female and does NOT
have a laptop. is 13.79%
2.5 Problem. Assume that the sample is representative of the population of CMSU. Based on the
data, answer the following question:
2.5Problem 2.5.1 Find the probability that a randomly chosen student is either a male or has full-
time employment?
Solution:
Using contingency tables of Gender and Employment we got the total numbers of males and number of males
Who are full ime employed
‘And post calculation we find out that - Probabilty of randamly chosen student is either Male or has ful ime
‘employment. is 74.19%
2.5Problem 2.5.2. Find the conditional probability that given a female student is randomly
chosen, she is majoring in international business or management,
Solution:
i x
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| Using contingency tables of Gender and Major we got the total numbers of females and number of females
WT WT A WT Wr A
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Intention | No. yes | total
Gender
Female | 9 it 20
Male [3 47 20
Total [12 28 40,
Is the graduate intention and being female are independent events?
The Probability that a randomly selected student ‘being female’
‘The Probability that a randomly selected student the graduate intention and being female
(Grad Intention Yes) = 28/40 = 0.7
{Grad Intention Yes | female) = 11 /20= 0.55
‘These probabilities are not equal. This suggests that the two events are independent
2.7 Problem. Note that there are four numerical (continuous) variables in the data set, GPA,
Salary, Spending, and Text Messages. Answer the following questions based on the data
2.7Problem 2.7.1 If a student is chosen randomly, what is the probability that his/her GPA is less
than 3?
Solution:
Using contingency tables of Gender and GPA we got the total numbers of students and number of students
GPAless than 3
‘And post calculation we find out that - Probabiliy that student is chosen randomly and that hisiher GPA is
less than 3 is 22.58%
2.7Problem 2.7.2 Find the conditional probability that a randomly selected male earns 0 or more,
Find the conditional probability that a randomly selected female earns 50 or more.
i x
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By these details we confirm that out of the given four data sets ‘GPA’ and ‘Salary’ are following normal
distribution whereas other two ‘Spending’ and “Text Messages’ are not following the normal distribution
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Problem 3 -
‘An important quality characteristic used by the manufacturers of ABC asphatt shingles is the amount of
moisture the shingles contain when they are packaged. Customers may feel that they have purchased a
product lacking in quality i they find moisture and wet shingles inside the packaging. In some cases,
‘excessive moisture can cause the granules attached to the shingles for texture and coloring purposes to
fall off the shingles resulting in appearance problems. To monitor the amount of moisture present, the
company conducts moisture tests. A shingle is weighed and then dried. The shingle is then reweighed,
and based on the amount of moisture taken out of the product; the pounds of moisture per 100 square
feet are calculated. The company would like to show that the mean moisture content is less than 0.35,
pound per 100 square feet.
‘The file (A & B shingles.csv) includes 36 measurements (in pounds per 100 square feet) for A shingles
and 31 for B shingles.
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utout from Python Jupyter
One sample test
{stati 1.4735046258902782 p value: 0.07477693144907513
Since pvalue > 0.05, do not reject HO. There Is not enough evidence to conclude thatthe mean moisture
content for Sampie'A shingles is less than 0.95 pounds per 100 square Yost. p-value =O 0748. I he
population mean moisture content isin fact no less than 0.35 pounds per 100 square feet, the probability
of observing a sample of 36 shingles that will result in a sample mean moisture content of 0.3167 pounds
per 100 square feet or less is 0748,
Input - Python Jupyter
| stalstic, p_value = test_tsamp(dB, 0.35,nan_policy~omit )
Print(One sample t tes int statisti: (0} p value: {1} ‘format statistic, p_value!2))
Cutout from Python Jupyter
One sample t test
{ statistic: -3.1003313069986995 p value: 0,0020904774003191826
‘Since pvalue < 0.05, reject HO . There is enough evidence to conclude that the mean moisture content for
‘Sample B shingles is not ess than 0.35 pounds per 100 square feet. p-value = 0.0021. Ifthe population mean
moisture content i in fact no less than 0.35pounds per 100 square feet, the probability of observing a sample
of 31 shingles that wil result in a sample mean moisture content of 0.2735 pounds per 100 square feet or less
is 0021
3.2 Problem Do you think that the population means for shingles A and B are equal? Form the
hypothesis and conduct the test of the hypothesis. What assumption do you need to check before the
test for equality of means is performed?
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