SlideShare a Scribd company logo
1 of 9
Download to read offline
Institute of Technology of Cambodia 2016-2017 Case study: Smartphone Preference
1). Khen Chanthorn 2). Kech Sengthai 3). Ken Keomhong 4). Kam Chanreaksmee
5). Kry Reothea
1
Assignment Content
I. PURPOSE OF SURVEY....................................................................................................2
1. Objective.........................................................................................................................2
2. Domain: (ITC students from year 1 to year 5.)...............................................................2
3. Data.................................................................................................................................2
II. ANALYSIS OF DATA ......................................................................................................3
1. THEORY AND ASSUMPTION....................................................................................3
2. TEST HYPOTHESIS AND CONFIDENCE INTERVAL CONDUCTING.................4
III. STATISTICAL ANALYSIS ..........................................................................................5
a) For students in year 𝟏:.................................................................................................5
b) For students in year 2:.................................................................................................6
c) For students in year 3:.................................................................................................7
d) For students in year 4:.................................................................................................7
e) For students in year 5:.................................................................................................8
f) For entire students in ITC: ..........................................................................................8
IV. CONCLUSION...............................................................................................................9
3. Our interest in this case study:........................................................................................9
Institute of Technology of Cambodia 2016-2017 Case study: Smartphone Preference
1). Khen Chanthorn 2). Kech Sengthai 3). Ken Keomhong 4). Kam Chanreaksmee
5). Kry Reothea
2
I. PURPOSE OF SURVEY
1. Objective
We decided to conduct this survey in order to estimate the percentage of students in
ITC using smartphone in various models especially iPhone.
We aim to use the result from this survey to:
 Estimate the true proportion of students in ITC using some smartphone models from
year 1 to 5 as well as the entire institute.
 Estimate the tendency of ITC’s students in using smartphones from first year to fifth
year
 Conduct the hypothesis whether or not the true proportion of students using iPhone in
each year and all students in ITC exceed 50%?
2. Domain: (ITC students from year 1 to year 5.)
3. Data
The data are collected by surveying directly with the students (Survey sheet) and
doing online (Online survey sheet).
Case Study: The Telephone Preference in Institute of Technology of Cambodia
Institute of Technology of Cambodia 2016-2017 Case study: Smartphone Preference
1). Khen Chanthorn 2). Kech Sengthai 3). Ken Keomhong 4). Kam Chanreaksmee
5). Kry Reothea
3
Problems:
In order to know the preference of the student in ITC in using Smartphone, we decided to do
a survey among 307 students from year 1st
to year 5th
randomly. After surveying, the results
showed that the numbers of students who use iPhone, Samsung, Nokia, Sony and other
models is respectively listed as the table below.
Data of students using different phones models
Phone Type
Students
iPhone Samsung Nokia Sony other Total
Year 1 52 18 5 0 7 82
Year 2 45 14 7 4 6 76
Year 3 35 13 6 1 6 61
Year 4 33 6 5 2 5 51
Year 5 31 3 1 0 2 37
Total 196 54 24 7 26 307
* Others= Huawei, LG, ASUS
II. ANALYSIS OF DATA
1. THEORY AND ASSUMPTION
1.1 Estimate the percentage of the students using iPhone in each year and the
entire institute:
The results of the test are in the Binomial’s distribution which hold the pmf
p(x) = 𝑝 𝑥
(1 − p) 𝑛−𝑥
In order to conduct the estimator for the proportion, we apply the Maximum Likelihood
Estimator’s theorem(MLE) for the pmf above, then;
𝑙𝑛[𝑝(𝑥)] = 𝑥𝑙𝑛(𝑝) + (𝑛 − 𝑥)𝑙𝑛(1 − 𝑝)
⇒
𝑑
𝑑𝑝
(ln[𝑝(𝑥)]) =
𝑥
𝑝
−
𝑛 − 𝑥
1 − 𝑝
= 0
52
45
35 33 31
18
14 13
6
35 7 6 5
10
4
1 2 0
7 6 6 5
2
0
10
20
30
40
50
60
Year 1 Year 2 Year 3 Year 4 Year 5
NUMBEROFSTUDENTS
Number of students using various phone models
Iphone Samsung Nokia Sony other
Institute of Technology of Cambodia 2016-2017 Case study: Smartphone Preference
1). Khen Chanthorn 2). Kech Sengthai 3). Ken Keomhong 4). Kam Chanreaksmee
5). Kry Reothea
4
⇔
1
𝑝
− 1 =
𝑛
𝑥
− 1
Thus, from the results in the table we can estimate the proportion of students using iPhone as
following;
The estimations of students using various phones in ITC(%)
Students iPhone Samsung Nokia Sony Others* TOTAL
Year 1 63% 22% 6% 0% 9%
100%
Year 2 59% 18% 9% 5% 8%
Year 3 57% 21% 10% 2% 10%
Year 4 65% 12% 10% 4% 10%
Year 5 84% 8% 3% 0% 5%
Entire ITC 64% 18% 8% 2% 8%
* Others= LG, Huawei, Asus
2. TEST HYPOTHESIS AND CONFIDENCE INTERVAL CONDUCTING
The estimator 𝑝 = 𝑥/𝑛 is unbiased (𝐸(𝑝) = 𝑝) has approximately a normal distribution, and
its standard deviation is 𝜎 𝑝̂ = √𝑝(1 − 𝑝)/𝑛 .
When H0 is true, 𝐸(𝑝) = 𝑝 𝑜 and 𝜎 𝑝̂ = √𝑝 𝑜(1 − 𝑝 𝑜)/𝑛 . so 𝜎 𝑝̂ does not involve any
unknown parameters.
when n is large and H0 is true, the test statistic has
𝑍 =
𝑝 − 𝑝0
√𝑝0(1 − 𝑝0)/𝑛
~𝒩(0,1)
If the alternative hypothesis is 𝐻 𝑎: 𝑝 > 𝑝 𝑎 and the upper-tailed rejection region𝑧 > 𝑧 𝛼 is
used, then
63%
59% 57%
65%
84%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
0 1 2 3 4 5 6Year
The tendancy of students using certain phone models from
year 1 to 5
iPhone Samsung Nokia Sony Others
𝑝 =
𝑥
𝑛
Institute of Technology of Cambodia 2016-2017 Case study: Smartphone Preference
1). Khen Chanthorn 2). Kech Sengthai 3). Ken Keomhong 4). Kam Chanreaksmee
5). Kry Reothea
5
𝑃(𝑡𝑦𝑝𝑒 𝐼 𝑒𝑟𝑟𝑜𝑟 ) = 𝑃(𝑟𝑒𝑗𝑒𝑐𝑡 𝐻0 𝑤ℎ𝑒𝑛 𝐻0 𝑖𝑠 𝑡𝑟𝑢𝑒 )
= 𝑃(𝑧 > 𝑧 𝛼 𝑤ℎ𝑒𝑛 𝑧 ℎ𝑎𝑠 𝑎𝑝𝑝𝑟𝑜𝑥𝑖𝑚𝑎𝑡𝑒𝑙𝑦 𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝑛𝑜𝑟𝑚𝑎𝑙 𝑑𝑖𝑠𝑡𝑟𝑖𝑏𝑢𝑡𝑖𝑜𝑛) = 𝛼
Which, 𝑍 𝛼is the critical value that is calculated by using standard normal table.
𝑃(𝑧 < 𝑧 𝛼) = 1 − 𝛼
𝜙(𝑧 𝛼) = 1 − 𝛼
Note: The assumption above is stated in the condition that 𝒏𝒑 𝒐 > 𝟏𝟎 𝒂𝒏𝒅 (𝟏 − 𝒑 𝟎) > 𝟏𝟎
Standardizing 𝑝 by subtracting p and dividing by 𝜎 𝑝̂ then implies that
𝑃
(
−𝑧 𝛼
2
<
𝑝 − 𝑝
√ 𝑝(1 − 𝑝)
𝑛
< 𝑧 𝛼/2
)
≈ 1 − 𝛼
By simplifying this equation, we get the confidence interval for population proportion p with
confidence level approximately 100(1 − 𝛼)%
𝑝̃ − 𝑧 𝛼
2
√ 𝑝̂𝑞̂
𝑛
+
𝑧 𝛼/2
2
4𝑛2
1+
𝑧 𝛼
2
2
𝑛
< 𝑝 < 𝑝̃ +
√𝑝̂ 𝑞̂/𝑛+𝑧 𝛼/2
2 /4𝑛2
1+𝑧 𝛼/2
2 /𝑛
, where 𝑝̃ =
𝑝̂+𝑧 𝛼/2
2
/2𝑛
1+𝑧 𝛼/2
2 /𝑛
III. STATISTICAL ANALYSIS
1.1 Test the proportion of the students in each year using iPhone whether the true
proportion exceed 50% at level 𝜶 = 𝟎. 𝟎𝟓
Test hypothesis 𝐻 𝑜 ∶ 𝑝0 = 0.5 𝑣𝑠 𝐻 𝑎 ∶ 𝑝 𝑎 > 0.5
Test statistic:
𝑍 =
𝑝 − 𝑝
√𝑝(1 − 𝑝)/𝑛
These test procedures are valid provided that 𝒏𝒑 𝟎 ≥ 𝟏𝟎 and 𝒏(𝟏 − 𝒑 𝟎) ≥ 𝟏𝟎
Since 𝒏𝒊 𝒑𝒊 > 𝟏𝟎 𝒂𝒏𝒅 (𝟏 − 𝒑𝒊) > 𝟏𝟎, so the assumption above is true.
Under 𝐻0 𝑧 =
𝑝̂−0.5
√𝑝0(1−0.5)/𝑛
a) For students in year 𝟏:
𝑝 =
𝑥
𝑛
=
52
82
= 0.64
Or 100(1 − 𝛼)% CI = ( 𝑝̃ ± 𝑧 𝛼/2
√ 𝑝̂ 𝑞̂/𝑛+𝑧 𝛼/2
2 /4𝑛2
1+𝑧 𝛼/2
2 /𝑛
)
Institute of Technology of Cambodia 2016-2017 Case study: Smartphone Preference
1). Khen Chanthorn 2). Kech Sengthai 3). Ken Keomhong 4). Kam Chanreaksmee
5). Kry Reothea
6
𝑧 𝛼 = 1.645
𝛼
𝑧 = 1.61
𝑧 =
𝑝̂−𝑝0
√𝑝(1−𝑝)/𝑛
=
0.64−0.5
√0.5(1−0.5)/82
=2.536
 Find 𝒛∝ at level ∝= 𝟎. 𝟎𝟓:
P (𝑧∝)=1- 𝛼 = 1 − 0.05 = 0.95
From table 𝑧 𝛼 = 1.645
Rejection region RR={𝑧|𝑧 > 𝑧 𝛼}={𝑧|𝑧 > 1.645}
Since 𝑧 = 5.36 > 𝑧 𝛼 = 1.645 , we reject 𝐻0
 Conclusion: There are strong evidence to conclude that the true proportion of
students using iPhone in year 1 exceeds 50%.
 Confidence Interval for proportion at level 𝜶 = 𝟎. 𝟎𝟓
We have;
𝑝̃ ± 𝑧 𝛼/2
√𝑝̂ 𝑞̂/𝑛+𝑧 𝛼/2
2 /4𝑛2
1+𝑧 𝛼/2
2 /𝑛
, where 𝑝̃ =
𝑝̂+𝑧 𝛼/2
2
/2𝑛
1+𝑧 𝛼/2
2 /𝑛
𝑝̃ =
0.63 + 1.962
/2 × 82
1 + 1.962/82
= 0.62
𝑧 𝛼/2
√𝑝̂ 𝑞̂/𝑛+𝑧 𝛼/2
2 /4𝑛2
1+𝑧 𝛼/2
2 /𝑛
= 1.96
√0.63×0.47+1.962/4×822
1+1.962/82
=0.11
b) For students in year 2:
𝑝 =
𝑥
𝑛
=
45
76
= 0.59
𝑧 =
𝑝 − 𝑝0
√𝑝(1 − 𝑝)/𝑛
=
0.59 − 0.5
√0.5(1 − 0.5)/76
= 1.61
 Rejection Region 𝑅𝑅 = {𝑧|𝑧 > 𝑧 𝛼} = {𝑧|𝑧 > 1.645}
Since 𝑧 ∉ 𝑅𝑅, so we do not reject H0
 Conclusion: There is no compelling evidence to conclude that the true proportion of
students in year 2 using iPhone exceed 50% at level 𝛼 = 0.05.
 Confidence Interval for proportion at level 𝜶 = 𝟎. 𝟎𝟓
𝑝̃ =
0.59 + 1.962
/2 × 76
1 + 1.962/76
= 0.58
𝑧 𝛼 = 1.645
𝛼
𝑧 = 2.536
⇒ 95%CI(p) = (𝟎. 𝟓𝟏, 𝟎. 𝟕𝟑)
Institute of Technology of Cambodia 2016-2017 Case study: Smartphone Preference
1). Khen Chanthorn 2). Kech Sengthai 3). Ken Keomhong 4). Kam Chanreaksmee
5). Kry Reothea
7
𝑧 𝛼 = 1.645
𝛼
𝑧 = 2.536
𝑧 𝛼 = 1.645
𝛼
𝑧 = 1.15
𝑧 𝛼/2
√ 𝑝 𝑞̂/𝑛 + 𝑧 𝛼/2
2
/4𝑛2
1 + 𝑧 𝛼/2
2
/𝑛
=
√0.59 × 0.41 + 1.962/4 × 762
1 + 1.962/76
= 0.11
c) For students in year 3:
𝑝 =
𝑥
𝑛
=
35
62
= 0.57
𝑧 =
𝑝 − 𝑝0
√𝑝(1 − 𝑝)/𝑛
=
0.57 − 0.5
√0.5(1 − 0.5)/61
= 1.15
 Rejection Region 𝑅𝑅 = {𝑧|𝑧 > 𝑧 𝛼} = {𝑧|𝑧 > 1.645}
Since 𝑧 ∉ 𝑅𝑅, so we do not reject H0
 Conclusion: There is no compelling evidence to conclude that the true proportion of
students in year 3 using iPhone exceed 50% at level 𝛼 = 0.05.
 Confidence Interval for proportion at level 𝜶 = 𝟎. 𝟎𝟓
𝑝̃ =
0.57 + 1.962
/2 × 61
1 + 1.962/61
= 0.56
𝑧 𝛼/2
√ 𝑝 𝑞̂/𝑛 + 𝑧 𝛼/2
2
/4𝑛2
1 + 𝑧 𝛼/2
2
/𝑛
= 1.96
√0.57 × 0.43 + 1.962/4 × 612
1 + 1.962/61
= 0.12
d) For students in year 4:
𝑝 =
𝑥
𝑛
=
33
51
= 0.65
𝑧 =
𝑝 − 𝑝0
√𝑝(1 − 𝑝)/𝑛
=
0.65 − 0.5
√0.5(1 − 0.5)/51
= 2.10
 Rejection Region 𝑅𝑅 = {𝑧|𝑧 > 𝑧 𝛼} = {𝑧|𝑧 > 1.645}
Since 𝑧 ∈ 𝑅𝑅, so we reject H0
 Conclusion: There is compelling evidence to conclude that the true proportion of
students in year 4 using iPhone exceed 50% at level 𝛼 = 0.05.
 Confidence Interval for proportion at level 𝜶 = 𝟎. 𝟎𝟓
⇒ 95%CI(p) = (𝟎. 𝟒𝟒, 𝟎. 𝟔𝟖)
⇒ 95%CI(p) = (0.47, 0.69)
Institute of Technology of Cambodia 2016-2017 Case study: Smartphone Preference
1). Khen Chanthorn 2). Kech Sengthai 3). Ken Keomhong 4). Kam Chanreaksmee
5). Kry Reothea
8
𝑧 𝛼 = 1.645
𝛼
𝑧 = 4.11
𝑧 𝛼 = 1.645
𝛼
𝑧 = 4.85
𝑝̃ =
0.53 + 1.962
/2 × 82
1 + 1.962/82
= 0.64
𝑧 𝛼/2
√ 𝑝 𝑞̂/𝑛 + 𝑧 𝛼/2
2
/4𝑛2
1 + 𝑧 𝛼/2
2
/𝑛
= 1.96
√0.53 × 0.47 + 1.962/4 × 822
1 + 1.962/82
= 0.13
e) For students in year 5:
𝑝 =
𝑥
𝑛
=
31
37
= 0.84
𝑧 =
𝑝 − 𝑝0
√𝑝(1 − 𝑝)/𝑛
=
0.84 − 0.5
√0.5(1 − 0.5)/37
= 4.11
 Rejection Region 𝑅𝑅 = {𝑧|𝑧 > 𝑧 𝛼} = {𝑧|𝑧 > 1.645}
Since 𝑧 ∈ 𝑅𝑅, so we reject H0
 Conclusion: There is compelling evidence to conclude that the true proportion of
students in year 5 using iPhone exceed 50% at level 𝛼 = 0.05.
 Confidence Interval for proportion at level 𝜶 = 𝟎. 𝟎𝟓
𝑝̃ =
0.84 + 1.962
/2 × 37
1 + 1.962/37
= 0.81
𝑧 𝛼/2
√ 𝑝 𝑞̂/𝑛 + 𝑧 𝛼/2
2
/4𝑛2
1 + 𝑧 𝛼/2
2
/𝑛
= 1.96
√0.84 × 0.16 + 1.962/4 × 372
1 + 1.962/37
= 0.63
f) For entire students in ITC:
𝑝 =
𝑥
𝑛
=
196
307
= 0.64
𝑧 =
𝑝 − 𝑝0
√𝑝(1 − 𝑝)/𝑛
=
0.64 − 0.5
√0.5(1 − 0.5)/307
= 4.85
 Rejection Region 𝑅𝑅 = {𝑧|𝑧 > 𝑧 𝛼} = {𝑧|𝑧 > 1.645}
Since 𝑧 ∈ 𝑅𝑅, so we reject H0
⇒ 95%CI(p) = (𝟎. 𝟔𝟖, 𝟎. 𝟗𝟒)
⇒ 95%CI(p) = (𝟎. 𝟓𝟏, 𝟎. 𝟕𝟕)
Institute of Technology of Cambodia 2016-2017 Case study: Smartphone Preference
1). Khen Chanthorn 2). Kech Sengthai 3). Ken Keomhong 4). Kam Chanreaksmee
5). Kry Reothea
9
 Conclusion: There is enough evidence to conclude that the true proportion of students
in ITC using iPhone from year 1 to year 5 exceed 50% at level 𝛼 = 0.05.
 Confidence Interval for proportion at level 𝜶 = 𝟎. 𝟎𝟓
𝑝̃ =
0.53 + 1.962
/2 × 82
1 + 1.962/82
= 0.64
𝑧 𝛼/2
√ 𝑝 𝑞̂/𝑛 + 𝑧 𝛼/2
2
/4𝑛2
1 + 𝑧 𝛼/2
2
/𝑛
= 1.96
√0.53 × 0.47 + 1.962/4 × 822
1 + 1.962/82
= 0.05
IV. CONCLUSION
After doing survey and conducting the estimation and test hypothesis, we found that the
most popular smartphone model is iPhone. However, the test hypothesis showed that at level
𝛼 = 0.05 _equilibrium the 95% Confidence Interval, the true proportion of students using
iPhone in year 1, year 4 and year 5 exceed 50% whereas there are no enough evidence to
conclude that the true proportion of students in year 2 and year 3 exceed 50%.
3. Our interest in this case study:
Even though this is just a small case study, but it gave us good experiences in doing team
work and the tactic in collecting data. Furthermore, this case study is also the revision of the
lessons that we have learnt.
⇒ 95%CI(p) = (0.59,0.69)

More Related Content

What's hot

STOCK MARKET PRREDICTION WITH FEATURE EXTRACTION USING NEURAL NETWORK TEHNIQUE
STOCK MARKET PRREDICTION WITH FEATURE EXTRACTION USING NEURAL NETWORK TEHNIQUESTOCK MARKET PRREDICTION WITH FEATURE EXTRACTION USING NEURAL NETWORK TEHNIQUE
STOCK MARKET PRREDICTION WITH FEATURE EXTRACTION USING NEURAL NETWORK TEHNIQUERicha Handa
 
Fake news detection project
Fake news detection projectFake news detection project
Fake news detection projectHarshdaGhai
 
Prediction of heart disease using machine learning.pptx
Prediction of heart disease using machine learning.pptxPrediction of heart disease using machine learning.pptx
Prediction of heart disease using machine learning.pptxkumari36
 
Gsm Based Automated Irrigation irrigation system
Gsm Based Automated Irrigation irrigation systemGsm Based Automated Irrigation irrigation system
Gsm Based Automated Irrigation irrigation systemSantanu Mukhopadhyay
 
Farmer Recommendation system
Farmer Recommendation systemFarmer Recommendation system
Farmer Recommendation systemSandeep Wakchaure
 
laptop price prediction presentation
laptop price prediction presentationlaptop price prediction presentation
laptop price prediction presentationNeerajNishad4
 
Big Data in the Real World. Real-time Football Analytics
Big Data in the Real World. Real-time Football AnalyticsBig Data in the Real World. Real-time Football Analytics
Big Data in the Real World. Real-time Football AnalyticsWSO2
 
Integrate Your Amazon Lex Chatbot with Any Messaging Service - AWS Online Tec...
Integrate Your Amazon Lex Chatbot with Any Messaging Service - AWS Online Tec...Integrate Your Amazon Lex Chatbot with Any Messaging Service - AWS Online Tec...
Integrate Your Amazon Lex Chatbot with Any Messaging Service - AWS Online Tec...Amazon Web Services
 
Role of Big Data in Medical Diagnostics
Role of Big Data in Medical DiagnosticsRole of Big Data in Medical Diagnostics
Role of Big Data in Medical DiagnosticsNishant Agarwal
 
IoT Based Garbage Monitoring System ppt
IoT Based Garbage Monitoring System pptIoT Based Garbage Monitoring System ppt
IoT Based Garbage Monitoring System pptRanjan Gupta
 
IRJET- Smart Real Time Manhole Monitoring System
IRJET- Smart Real Time Manhole Monitoring SystemIRJET- Smart Real Time Manhole Monitoring System
IRJET- Smart Real Time Manhole Monitoring SystemIRJET Journal
 
Iot based water quality monitoring system
Iot based water quality monitoring systemIot based water quality monitoring system
Iot based water quality monitoring systemBinayakreddy
 
Crime Analysis & Prediction System
Crime Analysis & Prediction SystemCrime Analysis & Prediction System
Crime Analysis & Prediction SystemBigDataCloud
 
Fnal year project on iot accident detection and tracking system 26-may 21'
 Fnal year project on iot accident detection and tracking system  26-may 21' Fnal year project on iot accident detection and tracking system  26-may 21'
Fnal year project on iot accident detection and tracking system 26-may 21'ankitadeokate
 
Crop prediction using machine learning
Crop prediction using machine learningCrop prediction using machine learning
Crop prediction using machine learningdataalcott
 
Genetic Algorithm for optimization on IRIS Dataset presentation ppt
Genetic Algorithm for optimization on IRIS Dataset presentation pptGenetic Algorithm for optimization on IRIS Dataset presentation ppt
Genetic Algorithm for optimization on IRIS Dataset presentation pptSunil Rajput
 

What's hot (20)

STOCK MARKET PRREDICTION WITH FEATURE EXTRACTION USING NEURAL NETWORK TEHNIQUE
STOCK MARKET PRREDICTION WITH FEATURE EXTRACTION USING NEURAL NETWORK TEHNIQUESTOCK MARKET PRREDICTION WITH FEATURE EXTRACTION USING NEURAL NETWORK TEHNIQUE
STOCK MARKET PRREDICTION WITH FEATURE EXTRACTION USING NEURAL NETWORK TEHNIQUE
 
Fake news detection project
Fake news detection projectFake news detection project
Fake news detection project
 
Prediction of heart disease using machine learning.pptx
Prediction of heart disease using machine learning.pptxPrediction of heart disease using machine learning.pptx
Prediction of heart disease using machine learning.pptx
 
Gsm Based Automated Irrigation irrigation system
Gsm Based Automated Irrigation irrigation systemGsm Based Automated Irrigation irrigation system
Gsm Based Automated Irrigation irrigation system
 
ANTI THEFT PPT
ANTI THEFT PPTANTI THEFT PPT
ANTI THEFT PPT
 
Autonomous Vehicles
Autonomous VehiclesAutonomous Vehicles
Autonomous Vehicles
 
Top data science projects
Top data science projectsTop data science projects
Top data science projects
 
Farmer Recommendation system
Farmer Recommendation systemFarmer Recommendation system
Farmer Recommendation system
 
laptop price prediction presentation
laptop price prediction presentationlaptop price prediction presentation
laptop price prediction presentation
 
Big Data in the Real World. Real-time Football Analytics
Big Data in the Real World. Real-time Football AnalyticsBig Data in the Real World. Real-time Football Analytics
Big Data in the Real World. Real-time Football Analytics
 
Integrate Your Amazon Lex Chatbot with Any Messaging Service - AWS Online Tec...
Integrate Your Amazon Lex Chatbot with Any Messaging Service - AWS Online Tec...Integrate Your Amazon Lex Chatbot with Any Messaging Service - AWS Online Tec...
Integrate Your Amazon Lex Chatbot with Any Messaging Service - AWS Online Tec...
 
Role of Big Data in Medical Diagnostics
Role of Big Data in Medical DiagnosticsRole of Big Data in Medical Diagnostics
Role of Big Data in Medical Diagnostics
 
IoT Based Garbage Monitoring System ppt
IoT Based Garbage Monitoring System pptIoT Based Garbage Monitoring System ppt
IoT Based Garbage Monitoring System ppt
 
IRJET- Smart Real Time Manhole Monitoring System
IRJET- Smart Real Time Manhole Monitoring SystemIRJET- Smart Real Time Manhole Monitoring System
IRJET- Smart Real Time Manhole Monitoring System
 
Iot based water quality monitoring system
Iot based water quality monitoring systemIot based water quality monitoring system
Iot based water quality monitoring system
 
smart helmet
smart helmetsmart helmet
smart helmet
 
Crime Analysis & Prediction System
Crime Analysis & Prediction SystemCrime Analysis & Prediction System
Crime Analysis & Prediction System
 
Fnal year project on iot accident detection and tracking system 26-may 21'
 Fnal year project on iot accident detection and tracking system  26-may 21' Fnal year project on iot accident detection and tracking system  26-may 21'
Fnal year project on iot accident detection and tracking system 26-may 21'
 
Crop prediction using machine learning
Crop prediction using machine learningCrop prediction using machine learning
Crop prediction using machine learning
 
Genetic Algorithm for optimization on IRIS Dataset presentation ppt
Genetic Algorithm for optimization on IRIS Dataset presentation pptGenetic Algorithm for optimization on IRIS Dataset presentation ppt
Genetic Algorithm for optimization on IRIS Dataset presentation ppt
 

Viewers also liked

Electronic Resource Management in 21st Century: Issues & Challenges
Electronic Resource Management in 21st Century: Issues & ChallengesElectronic Resource Management in 21st Century: Issues & Challenges
Electronic Resource Management in 21st Century: Issues & ChallengesKishor Satpathy
 
Oracle EBS R12 Case Study India - Sidhartha Meka
Oracle EBS R12 Case Study   India - Sidhartha MekaOracle EBS R12 Case Study   India - Sidhartha Meka
Oracle EBS R12 Case Study India - Sidhartha MekaSidhartha Meka
 
Report on Change Management at Levis
Report on Change Management at LevisReport on Change Management at Levis
Report on Change Management at LevisDavid Thompson
 
Anti-Brand Virtual Communities and Social Influence
Anti-Brand Virtual Communities and Social InfluenceAnti-Brand Virtual Communities and Social Influence
Anti-Brand Virtual Communities and Social InfluenceKhairunnissa Virani
 
Risk management of telecommunication and engineering laboratory
Risk management of telecommunication and engineering laboratoryRisk management of telecommunication and engineering laboratory
Risk management of telecommunication and engineering laboratorySalam Shah
 
Tipping Point on Emirate Airlines - Kaosarat Animashaun
Tipping Point on Emirate Airlines - Kaosarat AnimashaunTipping Point on Emirate Airlines - Kaosarat Animashaun
Tipping Point on Emirate Airlines - Kaosarat AnimashaunTitilola Animashaun
 
Supermarkets, Bangkok
Supermarkets, BangkokSupermarkets, Bangkok
Supermarkets, BangkokCorin Tan
 
De Beers Gartner e business case study
De Beers Gartner e business case studyDe Beers Gartner e business case study
De Beers Gartner e business case studyJames AH Campbell
 
Full paper technologies and strategies for providing education through (july 14)
Full paper technologies and strategies for providing education through (july 14)Full paper technologies and strategies for providing education through (july 14)
Full paper technologies and strategies for providing education through (july 14)Isdianto Isdianto
 
Undergraduate Project written by EBERE on ANALYSIS OF VARIATION IN GSK
Undergraduate Project written by EBERE on ANALYSIS OF VARIATION IN GSKUndergraduate Project written by EBERE on ANALYSIS OF VARIATION IN GSK
Undergraduate Project written by EBERE on ANALYSIS OF VARIATION IN GSKEbere Uzowuru
 
Budgeting forecasting and cost control management techniques september, lagos
Budgeting forecasting and cost control management techniques september, lagosBudgeting forecasting and cost control management techniques september, lagos
Budgeting forecasting and cost control management techniques september, lagosPetro Nomics
 
Psap 13-plk-blu-ver-ksap-final
Psap 13-plk-blu-ver-ksap-finalPsap 13-plk-blu-ver-ksap-final
Psap 13-plk-blu-ver-ksap-finalEnvaPya
 
Final_Report_Summer_Internship.PDF
Final_Report_Summer_Internship.PDFFinal_Report_Summer_Internship.PDF
Final_Report_Summer_Internship.PDFAmit Singh
 

Viewers also liked (19)

Electronic Resource Management in 21st Century: Issues & Challenges
Electronic Resource Management in 21st Century: Issues & ChallengesElectronic Resource Management in 21st Century: Issues & Challenges
Electronic Resource Management in 21st Century: Issues & Challenges
 
Oracle EBS R12 Case Study India - Sidhartha Meka
Oracle EBS R12 Case Study   India - Sidhartha MekaOracle EBS R12 Case Study   India - Sidhartha Meka
Oracle EBS R12 Case Study India - Sidhartha Meka
 
Report on Change Management at Levis
Report on Change Management at LevisReport on Change Management at Levis
Report on Change Management at Levis
 
Anti-Brand Virtual Communities and Social Influence
Anti-Brand Virtual Communities and Social InfluenceAnti-Brand Virtual Communities and Social Influence
Anti-Brand Virtual Communities and Social Influence
 
LIBRARY CASE STUDY DRAFT
LIBRARY CASE STUDY DRAFTLIBRARY CASE STUDY DRAFT
LIBRARY CASE STUDY DRAFT
 
White Paper
White PaperWhite Paper
White Paper
 
Risk management of telecommunication and engineering laboratory
Risk management of telecommunication and engineering laboratoryRisk management of telecommunication and engineering laboratory
Risk management of telecommunication and engineering laboratory
 
Tipping Point on Emirate Airlines - Kaosarat Animashaun
Tipping Point on Emirate Airlines - Kaosarat AnimashaunTipping Point on Emirate Airlines - Kaosarat Animashaun
Tipping Point on Emirate Airlines - Kaosarat Animashaun
 
Supermarkets, Bangkok
Supermarkets, BangkokSupermarkets, Bangkok
Supermarkets, Bangkok
 
De Beers Gartner e business case study
De Beers Gartner e business case studyDe Beers Gartner e business case study
De Beers Gartner e business case study
 
Case Study #5 Appple
Case Study #5 ApppleCase Study #5 Appple
Case Study #5 Appple
 
Full paper technologies and strategies for providing education through (july 14)
Full paper technologies and strategies for providing education through (july 14)Full paper technologies and strategies for providing education through (july 14)
Full paper technologies and strategies for providing education through (july 14)
 
Visiondocument
VisiondocumentVisiondocument
Visiondocument
 
Undergraduate Project written by EBERE on ANALYSIS OF VARIATION IN GSK
Undergraduate Project written by EBERE on ANALYSIS OF VARIATION IN GSKUndergraduate Project written by EBERE on ANALYSIS OF VARIATION IN GSK
Undergraduate Project written by EBERE on ANALYSIS OF VARIATION IN GSK
 
Budgeting forecasting and cost control management techniques september, lagos
Budgeting forecasting and cost control management techniques september, lagosBudgeting forecasting and cost control management techniques september, lagos
Budgeting forecasting and cost control management techniques september, lagos
 
Psap 13-plk-blu-ver-ksap-final
Psap 13-plk-blu-ver-ksap-finalPsap 13-plk-blu-ver-ksap-final
Psap 13-plk-blu-ver-ksap-final
 
Final_Report_Summer_Internship.PDF
Final_Report_Summer_Internship.PDFFinal_Report_Summer_Internship.PDF
Final_Report_Summer_Internship.PDF
 
Different types of loom
Different types of loomDifferent types of loom
Different types of loom
 
UX Case Study
UX Case StudyUX Case Study
UX Case Study
 

Similar to Case study: Probability and Statistic

Sampling-and-Sampling-Distribution .pptx
Sampling-and-Sampling-Distribution .pptxSampling-and-Sampling-Distribution .pptx
Sampling-and-Sampling-Distribution .pptxKayraTheressGubat
 
Analyzing experimental research data
Analyzing experimental research dataAnalyzing experimental research data
Analyzing experimental research dataAtula Ahuja
 
Central tendency and dispersion
Central tendency and dispersionCentral tendency and dispersion
Central tendency and dispersionAnisur Rahman
 
Final Project ScenarioA researcher has administered an anxiety.docx
Final Project ScenarioA researcher has administered an anxiety.docxFinal Project ScenarioA researcher has administered an anxiety.docx
Final Project ScenarioA researcher has administered an anxiety.docxAKHIL969626
 
OWA BASED MAGDM TECHNIQUE IN EVALUATING DIAGNOSTIC LABORATORY UNDER FUZZY ENV...
OWA BASED MAGDM TECHNIQUE IN EVALUATING DIAGNOSTIC LABORATORY UNDER FUZZY ENV...OWA BASED MAGDM TECHNIQUE IN EVALUATING DIAGNOSTIC LABORATORY UNDER FUZZY ENV...
OWA BASED MAGDM TECHNIQUE IN EVALUATING DIAGNOSTIC LABORATORY UNDER FUZZY ENV...ijfls
 
Math 221 Massive Success / snaptutorial.com
Math 221 Massive Success / snaptutorial.comMath 221 Massive Success / snaptutorial.com
Math 221 Massive Success / snaptutorial.comStephenson164
 
Paper presentation on 'Understanding Balck-box Predictions via Influence Func...
Paper presentation on 'Understanding Balck-box Predictions via Influence Func...Paper presentation on 'Understanding Balck-box Predictions via Influence Func...
Paper presentation on 'Understanding Balck-box Predictions via Influence Func...Zabir Al Nazi Nabil
 
Understanding Black-box Predictions via Influence Functions
Understanding Black-box Predictions via Influence FunctionsUnderstanding Black-box Predictions via Influence Functions
Understanding Black-box Predictions via Influence FunctionsZabir Al Nazi Nabil
 
Analyzing experimental research data
Analyzing experimental research dataAnalyzing experimental research data
Analyzing experimental research dataAtula Ahuja
 
UG_B.Sc._Psycology_11933 –PSYCHOLOGICAL STATISTICS.pdf
UG_B.Sc._Psycology_11933 –PSYCHOLOGICAL STATISTICS.pdfUG_B.Sc._Psycology_11933 –PSYCHOLOGICAL STATISTICS.pdf
UG_B.Sc._Psycology_11933 –PSYCHOLOGICAL STATISTICS.pdfManjariPalani1
 
8 2008-normative data for the letter cancellation task in school children
8 2008-normative data for the letter cancellation task in school children8 2008-normative data for the letter cancellation task in school children
8 2008-normative data for the letter cancellation task in school childrenElsa von Licy
 
Sequence-to-Sequence Modeling for Time Series
Sequence-to-Sequence Modeling for Time SeriesSequence-to-Sequence Modeling for Time Series
Sequence-to-Sequence Modeling for Time SeriesArun Kejariwal
 

Similar to Case study: Probability and Statistic (20)

Sampling-and-Sampling-Distribution .pptx
Sampling-and-Sampling-Distribution .pptxSampling-and-Sampling-Distribution .pptx
Sampling-and-Sampling-Distribution .pptx
 
SAMPLING-PROCEDURE.pdf
SAMPLING-PROCEDURE.pdfSAMPLING-PROCEDURE.pdf
SAMPLING-PROCEDURE.pdf
 
FORECASTING MODELS
FORECASTING MODELSFORECASTING MODELS
FORECASTING MODELS
 
Test pp
Test ppTest pp
Test pp
 
Analyzing experimental research data
Analyzing experimental research dataAnalyzing experimental research data
Analyzing experimental research data
 
Central tendency and dispersion
Central tendency and dispersionCentral tendency and dispersion
Central tendency and dispersion
 
Final Project ScenarioA researcher has administered an anxiety.docx
Final Project ScenarioA researcher has administered an anxiety.docxFinal Project ScenarioA researcher has administered an anxiety.docx
Final Project ScenarioA researcher has administered an anxiety.docx
 
OWA BASED MAGDM TECHNIQUE IN EVALUATING DIAGNOSTIC LABORATORY UNDER FUZZY ENV...
OWA BASED MAGDM TECHNIQUE IN EVALUATING DIAGNOSTIC LABORATORY UNDER FUZZY ENV...OWA BASED MAGDM TECHNIQUE IN EVALUATING DIAGNOSTIC LABORATORY UNDER FUZZY ENV...
OWA BASED MAGDM TECHNIQUE IN EVALUATING DIAGNOSTIC LABORATORY UNDER FUZZY ENV...
 
Math 221 Massive Success / snaptutorial.com
Math 221 Massive Success / snaptutorial.comMath 221 Massive Success / snaptutorial.com
Math 221 Massive Success / snaptutorial.com
 
Paper presentation on 'Understanding Balck-box Predictions via Influence Func...
Paper presentation on 'Understanding Balck-box Predictions via Influence Func...Paper presentation on 'Understanding Balck-box Predictions via Influence Func...
Paper presentation on 'Understanding Balck-box Predictions via Influence Func...
 
Understanding Black-box Predictions via Influence Functions
Understanding Black-box Predictions via Influence FunctionsUnderstanding Black-box Predictions via Influence Functions
Understanding Black-box Predictions via Influence Functions
 
Analyzing experimental research data
Analyzing experimental research dataAnalyzing experimental research data
Analyzing experimental research data
 
UNIT 3 .docx
UNIT 3 .docxUNIT 3 .docx
UNIT 3 .docx
 
Applied math sba
Applied math sbaApplied math sba
Applied math sba
 
B STAT PROJECTmmocx
B STAT PROJECTmmocxB STAT PROJECTmmocx
B STAT PROJECTmmocx
 
UG_B.Sc._Psycology_11933 –PSYCHOLOGICAL STATISTICS.pdf
UG_B.Sc._Psycology_11933 –PSYCHOLOGICAL STATISTICS.pdfUG_B.Sc._Psycology_11933 –PSYCHOLOGICAL STATISTICS.pdf
UG_B.Sc._Psycology_11933 –PSYCHOLOGICAL STATISTICS.pdf
 
Ch2
Ch2Ch2
Ch2
 
CH2.pdf
CH2.pdfCH2.pdf
CH2.pdf
 
8 2008-normative data for the letter cancellation task in school children
8 2008-normative data for the letter cancellation task in school children8 2008-normative data for the letter cancellation task in school children
8 2008-normative data for the letter cancellation task in school children
 
Sequence-to-Sequence Modeling for Time Series
Sequence-to-Sequence Modeling for Time SeriesSequence-to-Sequence Modeling for Time Series
Sequence-to-Sequence Modeling for Time Series
 

More from Smee Kaem Chann

More from Smee Kaem Chann (20)

stress-and-strain
stress-and-strainstress-and-strain
stress-and-strain
 
Robot khmer engineer
Robot khmer engineerRobot khmer engineer
Robot khmer engineer
 
15 poteau-2
15 poteau-215 poteau-2
15 poteau-2
 
14 poteau-1
14 poteau-114 poteau-1
14 poteau-1
 
12 plancher-Eurocode 2
12 plancher-Eurocode 212 plancher-Eurocode 2
12 plancher-Eurocode 2
 
Matlab_Prof Pouv Keangsé
Matlab_Prof Pouv KeangséMatlab_Prof Pouv Keangsé
Matlab_Prof Pouv Keangsé
 
Vocabuary
VocabuaryVocabuary
Vocabuary
 
Journal de bord
Journal de bordJournal de bord
Journal de bord
 
8.4 roof leader
8.4 roof leader8.4 roof leader
8.4 roof leader
 
Rapport de stage
Rapport de stage Rapport de stage
Rapport de stage
 
Travaux Pratique Matlab + Corrige_Smee Kaem Chann
Travaux Pratique Matlab + Corrige_Smee Kaem ChannTravaux Pratique Matlab + Corrige_Smee Kaem Chann
Travaux Pratique Matlab + Corrige_Smee Kaem Chann
 
Td triphasé
Td triphaséTd triphasé
Td triphasé
 
Tp2 Matlab
Tp2 MatlabTp2 Matlab
Tp2 Matlab
 
Cover matlab
Cover matlabCover matlab
Cover matlab
 
New Interchange 3ed edition Vocabulary unit 8
New Interchange 3ed edition Vocabulary unit 8 New Interchange 3ed edition Vocabulary unit 8
New Interchange 3ed edition Vocabulary unit 8
 
Matlab Travaux Pratique
Matlab Travaux Pratique Matlab Travaux Pratique
Matlab Travaux Pratique
 
The technologies of building resists the wind load and earthquake
The technologies of building resists the wind load and earthquakeThe technologies of building resists the wind load and earthquake
The technologies of building resists the wind load and earthquake
 
Devoir d'électricite des bêtiment
Devoir d'électricite des bêtimentDevoir d'électricite des bêtiment
Devoir d'électricite des bêtiment
 
Rapport topographie 2016-2017
Rapport topographie 2016-2017  Rapport topographie 2016-2017
Rapport topographie 2016-2017
 
Hydrologie générale
Hydrologie générale Hydrologie générale
Hydrologie générale
 

Recently uploaded

Call Girls Delhi {Jodhpur} 9711199012 high profile service
Call Girls Delhi {Jodhpur} 9711199012 high profile serviceCall Girls Delhi {Jodhpur} 9711199012 high profile service
Call Girls Delhi {Jodhpur} 9711199012 high profile servicerehmti665
 
Introduction-To-Agricultural-Surveillance-Rover.pptx
Introduction-To-Agricultural-Surveillance-Rover.pptxIntroduction-To-Agricultural-Surveillance-Rover.pptx
Introduction-To-Agricultural-Surveillance-Rover.pptxk795866
 
Architect Hassan Khalil Portfolio for 2024
Architect Hassan Khalil Portfolio for 2024Architect Hassan Khalil Portfolio for 2024
Architect Hassan Khalil Portfolio for 2024hassan khalil
 
Current Transformer Drawing and GTP for MSETCL
Current Transformer Drawing and GTP for MSETCLCurrent Transformer Drawing and GTP for MSETCL
Current Transformer Drawing and GTP for MSETCLDeelipZope
 
OSVC_Meta-Data based Simulation Automation to overcome Verification Challenge...
OSVC_Meta-Data based Simulation Automation to overcome Verification Challenge...OSVC_Meta-Data based Simulation Automation to overcome Verification Challenge...
OSVC_Meta-Data based Simulation Automation to overcome Verification Challenge...Soham Mondal
 
Microscopic Analysis of Ceramic Materials.pptx
Microscopic Analysis of Ceramic Materials.pptxMicroscopic Analysis of Ceramic Materials.pptx
Microscopic Analysis of Ceramic Materials.pptxpurnimasatapathy1234
 
Electronically Controlled suspensions system .pdf
Electronically Controlled suspensions system .pdfElectronically Controlled suspensions system .pdf
Electronically Controlled suspensions system .pdfme23b1001
 
power system scada applications and uses
power system scada applications and usespower system scada applications and uses
power system scada applications and usesDevarapalliHaritha
 
Heart Disease Prediction using machine learning.pptx
Heart Disease Prediction using machine learning.pptxHeart Disease Prediction using machine learning.pptx
Heart Disease Prediction using machine learning.pptxPoojaBan
 
What are the advantages and disadvantages of membrane structures.pptx
What are the advantages and disadvantages of membrane structures.pptxWhat are the advantages and disadvantages of membrane structures.pptx
What are the advantages and disadvantages of membrane structures.pptxwendy cai
 
HARMONY IN THE HUMAN BEING - Unit-II UHV-2
HARMONY IN THE HUMAN BEING - Unit-II UHV-2HARMONY IN THE HUMAN BEING - Unit-II UHV-2
HARMONY IN THE HUMAN BEING - Unit-II UHV-2RajaP95
 
APPLICATIONS-AC/DC DRIVES-OPERATING CHARACTERISTICS
APPLICATIONS-AC/DC DRIVES-OPERATING CHARACTERISTICSAPPLICATIONS-AC/DC DRIVES-OPERATING CHARACTERISTICS
APPLICATIONS-AC/DC DRIVES-OPERATING CHARACTERISTICSKurinjimalarL3
 
CCS355 Neural Network & Deep Learning UNIT III notes and Question bank .pdf
CCS355 Neural Network & Deep Learning UNIT III notes and Question bank .pdfCCS355 Neural Network & Deep Learning UNIT III notes and Question bank .pdf
CCS355 Neural Network & Deep Learning UNIT III notes and Question bank .pdfAsst.prof M.Gokilavani
 
complete construction, environmental and economics information of biomass com...
complete construction, environmental and economics information of biomass com...complete construction, environmental and economics information of biomass com...
complete construction, environmental and economics information of biomass com...asadnawaz62
 
chaitra-1.pptx fake news detection using machine learning
chaitra-1.pptx  fake news detection using machine learningchaitra-1.pptx  fake news detection using machine learning
chaitra-1.pptx fake news detection using machine learningmisbanausheenparvam
 

Recently uploaded (20)

young call girls in Green Park🔝 9953056974 🔝 escort Service
young call girls in Green Park🔝 9953056974 🔝 escort Serviceyoung call girls in Green Park🔝 9953056974 🔝 escort Service
young call girls in Green Park🔝 9953056974 🔝 escort Service
 
Call Girls Delhi {Jodhpur} 9711199012 high profile service
Call Girls Delhi {Jodhpur} 9711199012 high profile serviceCall Girls Delhi {Jodhpur} 9711199012 high profile service
Call Girls Delhi {Jodhpur} 9711199012 high profile service
 
Introduction-To-Agricultural-Surveillance-Rover.pptx
Introduction-To-Agricultural-Surveillance-Rover.pptxIntroduction-To-Agricultural-Surveillance-Rover.pptx
Introduction-To-Agricultural-Surveillance-Rover.pptx
 
Architect Hassan Khalil Portfolio for 2024
Architect Hassan Khalil Portfolio for 2024Architect Hassan Khalil Portfolio for 2024
Architect Hassan Khalil Portfolio for 2024
 
Current Transformer Drawing and GTP for MSETCL
Current Transformer Drawing and GTP for MSETCLCurrent Transformer Drawing and GTP for MSETCL
Current Transformer Drawing and GTP for MSETCL
 
young call girls in Rajiv Chowk🔝 9953056974 🔝 Delhi escort Service
young call girls in Rajiv Chowk🔝 9953056974 🔝 Delhi escort Serviceyoung call girls in Rajiv Chowk🔝 9953056974 🔝 Delhi escort Service
young call girls in Rajiv Chowk🔝 9953056974 🔝 Delhi escort Service
 
OSVC_Meta-Data based Simulation Automation to overcome Verification Challenge...
OSVC_Meta-Data based Simulation Automation to overcome Verification Challenge...OSVC_Meta-Data based Simulation Automation to overcome Verification Challenge...
OSVC_Meta-Data based Simulation Automation to overcome Verification Challenge...
 
Microscopic Analysis of Ceramic Materials.pptx
Microscopic Analysis of Ceramic Materials.pptxMicroscopic Analysis of Ceramic Materials.pptx
Microscopic Analysis of Ceramic Materials.pptx
 
Call Us -/9953056974- Call Girls In Vikaspuri-/- Delhi NCR
Call Us -/9953056974- Call Girls In Vikaspuri-/- Delhi NCRCall Us -/9953056974- Call Girls In Vikaspuri-/- Delhi NCR
Call Us -/9953056974- Call Girls In Vikaspuri-/- Delhi NCR
 
🔝9953056974🔝!!-YOUNG call girls in Rajendra Nagar Escort rvice Shot 2000 nigh...
🔝9953056974🔝!!-YOUNG call girls in Rajendra Nagar Escort rvice Shot 2000 nigh...🔝9953056974🔝!!-YOUNG call girls in Rajendra Nagar Escort rvice Shot 2000 nigh...
🔝9953056974🔝!!-YOUNG call girls in Rajendra Nagar Escort rvice Shot 2000 nigh...
 
Electronically Controlled suspensions system .pdf
Electronically Controlled suspensions system .pdfElectronically Controlled suspensions system .pdf
Electronically Controlled suspensions system .pdf
 
power system scada applications and uses
power system scada applications and usespower system scada applications and uses
power system scada applications and uses
 
Heart Disease Prediction using machine learning.pptx
Heart Disease Prediction using machine learning.pptxHeart Disease Prediction using machine learning.pptx
Heart Disease Prediction using machine learning.pptx
 
What are the advantages and disadvantages of membrane structures.pptx
What are the advantages and disadvantages of membrane structures.pptxWhat are the advantages and disadvantages of membrane structures.pptx
What are the advantages and disadvantages of membrane structures.pptx
 
HARMONY IN THE HUMAN BEING - Unit-II UHV-2
HARMONY IN THE HUMAN BEING - Unit-II UHV-2HARMONY IN THE HUMAN BEING - Unit-II UHV-2
HARMONY IN THE HUMAN BEING - Unit-II UHV-2
 
APPLICATIONS-AC/DC DRIVES-OPERATING CHARACTERISTICS
APPLICATIONS-AC/DC DRIVES-OPERATING CHARACTERISTICSAPPLICATIONS-AC/DC DRIVES-OPERATING CHARACTERISTICS
APPLICATIONS-AC/DC DRIVES-OPERATING CHARACTERISTICS
 
CCS355 Neural Network & Deep Learning UNIT III notes and Question bank .pdf
CCS355 Neural Network & Deep Learning UNIT III notes and Question bank .pdfCCS355 Neural Network & Deep Learning UNIT III notes and Question bank .pdf
CCS355 Neural Network & Deep Learning UNIT III notes and Question bank .pdf
 
complete construction, environmental and economics information of biomass com...
complete construction, environmental and economics information of biomass com...complete construction, environmental and economics information of biomass com...
complete construction, environmental and economics information of biomass com...
 
Exploring_Network_Security_with_JA3_by_Rakesh Seal.pptx
Exploring_Network_Security_with_JA3_by_Rakesh Seal.pptxExploring_Network_Security_with_JA3_by_Rakesh Seal.pptx
Exploring_Network_Security_with_JA3_by_Rakesh Seal.pptx
 
chaitra-1.pptx fake news detection using machine learning
chaitra-1.pptx  fake news detection using machine learningchaitra-1.pptx  fake news detection using machine learning
chaitra-1.pptx fake news detection using machine learning
 

Case study: Probability and Statistic

  • 1. Institute of Technology of Cambodia 2016-2017 Case study: Smartphone Preference 1). Khen Chanthorn 2). Kech Sengthai 3). Ken Keomhong 4). Kam Chanreaksmee 5). Kry Reothea 1 Assignment Content I. PURPOSE OF SURVEY....................................................................................................2 1. Objective.........................................................................................................................2 2. Domain: (ITC students from year 1 to year 5.)...............................................................2 3. Data.................................................................................................................................2 II. ANALYSIS OF DATA ......................................................................................................3 1. THEORY AND ASSUMPTION....................................................................................3 2. TEST HYPOTHESIS AND CONFIDENCE INTERVAL CONDUCTING.................4 III. STATISTICAL ANALYSIS ..........................................................................................5 a) For students in year 𝟏:.................................................................................................5 b) For students in year 2:.................................................................................................6 c) For students in year 3:.................................................................................................7 d) For students in year 4:.................................................................................................7 e) For students in year 5:.................................................................................................8 f) For entire students in ITC: ..........................................................................................8 IV. CONCLUSION...............................................................................................................9 3. Our interest in this case study:........................................................................................9
  • 2. Institute of Technology of Cambodia 2016-2017 Case study: Smartphone Preference 1). Khen Chanthorn 2). Kech Sengthai 3). Ken Keomhong 4). Kam Chanreaksmee 5). Kry Reothea 2 I. PURPOSE OF SURVEY 1. Objective We decided to conduct this survey in order to estimate the percentage of students in ITC using smartphone in various models especially iPhone. We aim to use the result from this survey to:  Estimate the true proportion of students in ITC using some smartphone models from year 1 to 5 as well as the entire institute.  Estimate the tendency of ITC’s students in using smartphones from first year to fifth year  Conduct the hypothesis whether or not the true proportion of students using iPhone in each year and all students in ITC exceed 50%? 2. Domain: (ITC students from year 1 to year 5.) 3. Data The data are collected by surveying directly with the students (Survey sheet) and doing online (Online survey sheet). Case Study: The Telephone Preference in Institute of Technology of Cambodia
  • 3. Institute of Technology of Cambodia 2016-2017 Case study: Smartphone Preference 1). Khen Chanthorn 2). Kech Sengthai 3). Ken Keomhong 4). Kam Chanreaksmee 5). Kry Reothea 3 Problems: In order to know the preference of the student in ITC in using Smartphone, we decided to do a survey among 307 students from year 1st to year 5th randomly. After surveying, the results showed that the numbers of students who use iPhone, Samsung, Nokia, Sony and other models is respectively listed as the table below. Data of students using different phones models Phone Type Students iPhone Samsung Nokia Sony other Total Year 1 52 18 5 0 7 82 Year 2 45 14 7 4 6 76 Year 3 35 13 6 1 6 61 Year 4 33 6 5 2 5 51 Year 5 31 3 1 0 2 37 Total 196 54 24 7 26 307 * Others= Huawei, LG, ASUS II. ANALYSIS OF DATA 1. THEORY AND ASSUMPTION 1.1 Estimate the percentage of the students using iPhone in each year and the entire institute: The results of the test are in the Binomial’s distribution which hold the pmf p(x) = 𝑝 𝑥 (1 − p) 𝑛−𝑥 In order to conduct the estimator for the proportion, we apply the Maximum Likelihood Estimator’s theorem(MLE) for the pmf above, then; 𝑙𝑛[𝑝(𝑥)] = 𝑥𝑙𝑛(𝑝) + (𝑛 − 𝑥)𝑙𝑛(1 − 𝑝) ⇒ 𝑑 𝑑𝑝 (ln[𝑝(𝑥)]) = 𝑥 𝑝 − 𝑛 − 𝑥 1 − 𝑝 = 0 52 45 35 33 31 18 14 13 6 35 7 6 5 10 4 1 2 0 7 6 6 5 2 0 10 20 30 40 50 60 Year 1 Year 2 Year 3 Year 4 Year 5 NUMBEROFSTUDENTS Number of students using various phone models Iphone Samsung Nokia Sony other
  • 4. Institute of Technology of Cambodia 2016-2017 Case study: Smartphone Preference 1). Khen Chanthorn 2). Kech Sengthai 3). Ken Keomhong 4). Kam Chanreaksmee 5). Kry Reothea 4 ⇔ 1 𝑝 − 1 = 𝑛 𝑥 − 1 Thus, from the results in the table we can estimate the proportion of students using iPhone as following; The estimations of students using various phones in ITC(%) Students iPhone Samsung Nokia Sony Others* TOTAL Year 1 63% 22% 6% 0% 9% 100% Year 2 59% 18% 9% 5% 8% Year 3 57% 21% 10% 2% 10% Year 4 65% 12% 10% 4% 10% Year 5 84% 8% 3% 0% 5% Entire ITC 64% 18% 8% 2% 8% * Others= LG, Huawei, Asus 2. TEST HYPOTHESIS AND CONFIDENCE INTERVAL CONDUCTING The estimator 𝑝 = 𝑥/𝑛 is unbiased (𝐸(𝑝) = 𝑝) has approximately a normal distribution, and its standard deviation is 𝜎 𝑝̂ = √𝑝(1 − 𝑝)/𝑛 . When H0 is true, 𝐸(𝑝) = 𝑝 𝑜 and 𝜎 𝑝̂ = √𝑝 𝑜(1 − 𝑝 𝑜)/𝑛 . so 𝜎 𝑝̂ does not involve any unknown parameters. when n is large and H0 is true, the test statistic has 𝑍 = 𝑝 − 𝑝0 √𝑝0(1 − 𝑝0)/𝑛 ~𝒩(0,1) If the alternative hypothesis is 𝐻 𝑎: 𝑝 > 𝑝 𝑎 and the upper-tailed rejection region𝑧 > 𝑧 𝛼 is used, then 63% 59% 57% 65% 84% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 0 1 2 3 4 5 6Year The tendancy of students using certain phone models from year 1 to 5 iPhone Samsung Nokia Sony Others 𝑝 = 𝑥 𝑛
  • 5. Institute of Technology of Cambodia 2016-2017 Case study: Smartphone Preference 1). Khen Chanthorn 2). Kech Sengthai 3). Ken Keomhong 4). Kam Chanreaksmee 5). Kry Reothea 5 𝑃(𝑡𝑦𝑝𝑒 𝐼 𝑒𝑟𝑟𝑜𝑟 ) = 𝑃(𝑟𝑒𝑗𝑒𝑐𝑡 𝐻0 𝑤ℎ𝑒𝑛 𝐻0 𝑖𝑠 𝑡𝑟𝑢𝑒 ) = 𝑃(𝑧 > 𝑧 𝛼 𝑤ℎ𝑒𝑛 𝑧 ℎ𝑎𝑠 𝑎𝑝𝑝𝑟𝑜𝑥𝑖𝑚𝑎𝑡𝑒𝑙𝑦 𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝑛𝑜𝑟𝑚𝑎𝑙 𝑑𝑖𝑠𝑡𝑟𝑖𝑏𝑢𝑡𝑖𝑜𝑛) = 𝛼 Which, 𝑍 𝛼is the critical value that is calculated by using standard normal table. 𝑃(𝑧 < 𝑧 𝛼) = 1 − 𝛼 𝜙(𝑧 𝛼) = 1 − 𝛼 Note: The assumption above is stated in the condition that 𝒏𝒑 𝒐 > 𝟏𝟎 𝒂𝒏𝒅 (𝟏 − 𝒑 𝟎) > 𝟏𝟎 Standardizing 𝑝 by subtracting p and dividing by 𝜎 𝑝̂ then implies that 𝑃 ( −𝑧 𝛼 2 < 𝑝 − 𝑝 √ 𝑝(1 − 𝑝) 𝑛 < 𝑧 𝛼/2 ) ≈ 1 − 𝛼 By simplifying this equation, we get the confidence interval for population proportion p with confidence level approximately 100(1 − 𝛼)% 𝑝̃ − 𝑧 𝛼 2 √ 𝑝̂𝑞̂ 𝑛 + 𝑧 𝛼/2 2 4𝑛2 1+ 𝑧 𝛼 2 2 𝑛 < 𝑝 < 𝑝̃ + √𝑝̂ 𝑞̂/𝑛+𝑧 𝛼/2 2 /4𝑛2 1+𝑧 𝛼/2 2 /𝑛 , where 𝑝̃ = 𝑝̂+𝑧 𝛼/2 2 /2𝑛 1+𝑧 𝛼/2 2 /𝑛 III. STATISTICAL ANALYSIS 1.1 Test the proportion of the students in each year using iPhone whether the true proportion exceed 50% at level 𝜶 = 𝟎. 𝟎𝟓 Test hypothesis 𝐻 𝑜 ∶ 𝑝0 = 0.5 𝑣𝑠 𝐻 𝑎 ∶ 𝑝 𝑎 > 0.5 Test statistic: 𝑍 = 𝑝 − 𝑝 √𝑝(1 − 𝑝)/𝑛 These test procedures are valid provided that 𝒏𝒑 𝟎 ≥ 𝟏𝟎 and 𝒏(𝟏 − 𝒑 𝟎) ≥ 𝟏𝟎 Since 𝒏𝒊 𝒑𝒊 > 𝟏𝟎 𝒂𝒏𝒅 (𝟏 − 𝒑𝒊) > 𝟏𝟎, so the assumption above is true. Under 𝐻0 𝑧 = 𝑝̂−0.5 √𝑝0(1−0.5)/𝑛 a) For students in year 𝟏: 𝑝 = 𝑥 𝑛 = 52 82 = 0.64 Or 100(1 − 𝛼)% CI = ( 𝑝̃ ± 𝑧 𝛼/2 √ 𝑝̂ 𝑞̂/𝑛+𝑧 𝛼/2 2 /4𝑛2 1+𝑧 𝛼/2 2 /𝑛 )
  • 6. Institute of Technology of Cambodia 2016-2017 Case study: Smartphone Preference 1). Khen Chanthorn 2). Kech Sengthai 3). Ken Keomhong 4). Kam Chanreaksmee 5). Kry Reothea 6 𝑧 𝛼 = 1.645 𝛼 𝑧 = 1.61 𝑧 = 𝑝̂−𝑝0 √𝑝(1−𝑝)/𝑛 = 0.64−0.5 √0.5(1−0.5)/82 =2.536  Find 𝒛∝ at level ∝= 𝟎. 𝟎𝟓: P (𝑧∝)=1- 𝛼 = 1 − 0.05 = 0.95 From table 𝑧 𝛼 = 1.645 Rejection region RR={𝑧|𝑧 > 𝑧 𝛼}={𝑧|𝑧 > 1.645} Since 𝑧 = 5.36 > 𝑧 𝛼 = 1.645 , we reject 𝐻0  Conclusion: There are strong evidence to conclude that the true proportion of students using iPhone in year 1 exceeds 50%.  Confidence Interval for proportion at level 𝜶 = 𝟎. 𝟎𝟓 We have; 𝑝̃ ± 𝑧 𝛼/2 √𝑝̂ 𝑞̂/𝑛+𝑧 𝛼/2 2 /4𝑛2 1+𝑧 𝛼/2 2 /𝑛 , where 𝑝̃ = 𝑝̂+𝑧 𝛼/2 2 /2𝑛 1+𝑧 𝛼/2 2 /𝑛 𝑝̃ = 0.63 + 1.962 /2 × 82 1 + 1.962/82 = 0.62 𝑧 𝛼/2 √𝑝̂ 𝑞̂/𝑛+𝑧 𝛼/2 2 /4𝑛2 1+𝑧 𝛼/2 2 /𝑛 = 1.96 √0.63×0.47+1.962/4×822 1+1.962/82 =0.11 b) For students in year 2: 𝑝 = 𝑥 𝑛 = 45 76 = 0.59 𝑧 = 𝑝 − 𝑝0 √𝑝(1 − 𝑝)/𝑛 = 0.59 − 0.5 √0.5(1 − 0.5)/76 = 1.61  Rejection Region 𝑅𝑅 = {𝑧|𝑧 > 𝑧 𝛼} = {𝑧|𝑧 > 1.645} Since 𝑧 ∉ 𝑅𝑅, so we do not reject H0  Conclusion: There is no compelling evidence to conclude that the true proportion of students in year 2 using iPhone exceed 50% at level 𝛼 = 0.05.  Confidence Interval for proportion at level 𝜶 = 𝟎. 𝟎𝟓 𝑝̃ = 0.59 + 1.962 /2 × 76 1 + 1.962/76 = 0.58 𝑧 𝛼 = 1.645 𝛼 𝑧 = 2.536 ⇒ 95%CI(p) = (𝟎. 𝟓𝟏, 𝟎. 𝟕𝟑)
  • 7. Institute of Technology of Cambodia 2016-2017 Case study: Smartphone Preference 1). Khen Chanthorn 2). Kech Sengthai 3). Ken Keomhong 4). Kam Chanreaksmee 5). Kry Reothea 7 𝑧 𝛼 = 1.645 𝛼 𝑧 = 2.536 𝑧 𝛼 = 1.645 𝛼 𝑧 = 1.15 𝑧 𝛼/2 √ 𝑝 𝑞̂/𝑛 + 𝑧 𝛼/2 2 /4𝑛2 1 + 𝑧 𝛼/2 2 /𝑛 = √0.59 × 0.41 + 1.962/4 × 762 1 + 1.962/76 = 0.11 c) For students in year 3: 𝑝 = 𝑥 𝑛 = 35 62 = 0.57 𝑧 = 𝑝 − 𝑝0 √𝑝(1 − 𝑝)/𝑛 = 0.57 − 0.5 √0.5(1 − 0.5)/61 = 1.15  Rejection Region 𝑅𝑅 = {𝑧|𝑧 > 𝑧 𝛼} = {𝑧|𝑧 > 1.645} Since 𝑧 ∉ 𝑅𝑅, so we do not reject H0  Conclusion: There is no compelling evidence to conclude that the true proportion of students in year 3 using iPhone exceed 50% at level 𝛼 = 0.05.  Confidence Interval for proportion at level 𝜶 = 𝟎. 𝟎𝟓 𝑝̃ = 0.57 + 1.962 /2 × 61 1 + 1.962/61 = 0.56 𝑧 𝛼/2 √ 𝑝 𝑞̂/𝑛 + 𝑧 𝛼/2 2 /4𝑛2 1 + 𝑧 𝛼/2 2 /𝑛 = 1.96 √0.57 × 0.43 + 1.962/4 × 612 1 + 1.962/61 = 0.12 d) For students in year 4: 𝑝 = 𝑥 𝑛 = 33 51 = 0.65 𝑧 = 𝑝 − 𝑝0 √𝑝(1 − 𝑝)/𝑛 = 0.65 − 0.5 √0.5(1 − 0.5)/51 = 2.10  Rejection Region 𝑅𝑅 = {𝑧|𝑧 > 𝑧 𝛼} = {𝑧|𝑧 > 1.645} Since 𝑧 ∈ 𝑅𝑅, so we reject H0  Conclusion: There is compelling evidence to conclude that the true proportion of students in year 4 using iPhone exceed 50% at level 𝛼 = 0.05.  Confidence Interval for proportion at level 𝜶 = 𝟎. 𝟎𝟓 ⇒ 95%CI(p) = (𝟎. 𝟒𝟒, 𝟎. 𝟔𝟖) ⇒ 95%CI(p) = (0.47, 0.69)
  • 8. Institute of Technology of Cambodia 2016-2017 Case study: Smartphone Preference 1). Khen Chanthorn 2). Kech Sengthai 3). Ken Keomhong 4). Kam Chanreaksmee 5). Kry Reothea 8 𝑧 𝛼 = 1.645 𝛼 𝑧 = 4.11 𝑧 𝛼 = 1.645 𝛼 𝑧 = 4.85 𝑝̃ = 0.53 + 1.962 /2 × 82 1 + 1.962/82 = 0.64 𝑧 𝛼/2 √ 𝑝 𝑞̂/𝑛 + 𝑧 𝛼/2 2 /4𝑛2 1 + 𝑧 𝛼/2 2 /𝑛 = 1.96 √0.53 × 0.47 + 1.962/4 × 822 1 + 1.962/82 = 0.13 e) For students in year 5: 𝑝 = 𝑥 𝑛 = 31 37 = 0.84 𝑧 = 𝑝 − 𝑝0 √𝑝(1 − 𝑝)/𝑛 = 0.84 − 0.5 √0.5(1 − 0.5)/37 = 4.11  Rejection Region 𝑅𝑅 = {𝑧|𝑧 > 𝑧 𝛼} = {𝑧|𝑧 > 1.645} Since 𝑧 ∈ 𝑅𝑅, so we reject H0  Conclusion: There is compelling evidence to conclude that the true proportion of students in year 5 using iPhone exceed 50% at level 𝛼 = 0.05.  Confidence Interval for proportion at level 𝜶 = 𝟎. 𝟎𝟓 𝑝̃ = 0.84 + 1.962 /2 × 37 1 + 1.962/37 = 0.81 𝑧 𝛼/2 √ 𝑝 𝑞̂/𝑛 + 𝑧 𝛼/2 2 /4𝑛2 1 + 𝑧 𝛼/2 2 /𝑛 = 1.96 √0.84 × 0.16 + 1.962/4 × 372 1 + 1.962/37 = 0.63 f) For entire students in ITC: 𝑝 = 𝑥 𝑛 = 196 307 = 0.64 𝑧 = 𝑝 − 𝑝0 √𝑝(1 − 𝑝)/𝑛 = 0.64 − 0.5 √0.5(1 − 0.5)/307 = 4.85  Rejection Region 𝑅𝑅 = {𝑧|𝑧 > 𝑧 𝛼} = {𝑧|𝑧 > 1.645} Since 𝑧 ∈ 𝑅𝑅, so we reject H0 ⇒ 95%CI(p) = (𝟎. 𝟔𝟖, 𝟎. 𝟗𝟒) ⇒ 95%CI(p) = (𝟎. 𝟓𝟏, 𝟎. 𝟕𝟕)
  • 9. Institute of Technology of Cambodia 2016-2017 Case study: Smartphone Preference 1). Khen Chanthorn 2). Kech Sengthai 3). Ken Keomhong 4). Kam Chanreaksmee 5). Kry Reothea 9  Conclusion: There is enough evidence to conclude that the true proportion of students in ITC using iPhone from year 1 to year 5 exceed 50% at level 𝛼 = 0.05.  Confidence Interval for proportion at level 𝜶 = 𝟎. 𝟎𝟓 𝑝̃ = 0.53 + 1.962 /2 × 82 1 + 1.962/82 = 0.64 𝑧 𝛼/2 √ 𝑝 𝑞̂/𝑛 + 𝑧 𝛼/2 2 /4𝑛2 1 + 𝑧 𝛼/2 2 /𝑛 = 1.96 √0.53 × 0.47 + 1.962/4 × 822 1 + 1.962/82 = 0.05 IV. CONCLUSION After doing survey and conducting the estimation and test hypothesis, we found that the most popular smartphone model is iPhone. However, the test hypothesis showed that at level 𝛼 = 0.05 _equilibrium the 95% Confidence Interval, the true proportion of students using iPhone in year 1, year 4 and year 5 exceed 50% whereas there are no enough evidence to conclude that the true proportion of students in year 2 and year 3 exceed 50%. 3. Our interest in this case study: Even though this is just a small case study, but it gave us good experiences in doing team work and the tactic in collecting data. Furthermore, this case study is also the revision of the lessons that we have learnt. ⇒ 95%CI(p) = (0.59,0.69)