Dengue Cases A local doctor is demanding immediate assistance claiming that the rate of new dengue cases in a certain barangay is higher than the current World Health Organization's rate of 56%. Upon arrival, you found that there were indeed 113 cases of dengue among 190 members of the small village. Is the local doctor correct in his assessment at a 0.01? A. Given B. Problem C. Hypothesis

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Dengue Cases

A local doctor is demanding immediate assistance claiming that the rate of new dengue cases in a certain barangay is higher than the current World Health Organization's rate of 56%. Upon arrival, you found that there were indeed 113 cases of dengue among 190 members of the small village. Is the local doctor correct in his assessment at a 0.01?

A. Given

B. Problem

C. Hypothesis

D. Formula and Test Statistic

E. Decision Basis

Traditional Method                   P-value Method

F. Statistical Decision

Traditional Method                 P-value Method

G. Contextual Decision

 

the attached photos are reference. pls use that as a guide. the answers must be complete especially the solution thanks.

the 2010
A. Given
P = 30% n=500 sample proportion(p)=D 25%
B. Problem
Population proportion vs. sample proportion
C. Hypothesis
Ho: p= 30%
На: ра 30%
D. Formula and Test Statistic
0.25 – 0.30
= -2.43
р-Р
Vpq/n z
V(0.300.70/500
E. Decision Basis
traditional method
p-value method
Two-tailed Test
z = -2.43 has an area of 0.0075
z =+ 1.96 ; 95%
two-tailed test thus p-value is
multiplied by 2 therefore 0.015
F. Statistical Decision
traditional method
p-value method p-value
The test statistic falls in the
vs a
0.015 < 0.05
critical region
Ho is rejected
Ho is rejected
G. Contextual Decision
We are 95% sure that the 2010 FIES is significantly different from the
current proportion of families that live below the poverty line.
Relationship of Interval Estimate and Hypothesis Testing
Remember that the previous example of 95% interval estimate for Family Income
Expenditure Survey (FIES) is between 26 – 34% of families living below the poverty
line. With the same number of samples (n=500), 25% of families living below the
poverty line is outside the confidence interval. The test of hypothesis confirms this
rational observation.
Transcribed Image Text:the 2010 A. Given P = 30% n=500 sample proportion(p)=D 25% B. Problem Population proportion vs. sample proportion C. Hypothesis Ho: p= 30% На: ра 30% D. Formula and Test Statistic 0.25 – 0.30 = -2.43 р-Р Vpq/n z V(0.300.70/500 E. Decision Basis traditional method p-value method Two-tailed Test z = -2.43 has an area of 0.0075 z =+ 1.96 ; 95% two-tailed test thus p-value is multiplied by 2 therefore 0.015 F. Statistical Decision traditional method p-value method p-value The test statistic falls in the vs a 0.015 < 0.05 critical region Ho is rejected Ho is rejected G. Contextual Decision We are 95% sure that the 2010 FIES is significantly different from the current proportion of families that live below the poverty line. Relationship of Interval Estimate and Hypothesis Testing Remember that the previous example of 95% interval estimate for Family Income Expenditure Survey (FIES) is between 26 – 34% of families living below the poverty line. With the same number of samples (n=500), 25% of families living below the poverty line is outside the confidence interval. The test of hypothesis confirms this rational observation.
the 2010
2
Qualitative Data: Testing for Proportions or Frequencies
Learning outcomes:
1. Examine the statistical technique for qualitative data
2. Identify the sample proportions for parametric data
3. describe and compare the equal and unequal frequencies for
the chi-square
. Qualitative Data: Testing for Proportions or Frequencies
Remember that there are two kinds of data; scores and frequ
cies. The
previous discussion on hypothesis testing all involved observations that
were measured quantitatively and we treated the data using its mean.
This time, we focus our discussion on testing hypothesis when
frequencies (we just count the number of observations) or proportions
(percentages) are concerned.
B.2.2.1 Testing a Claim About a Proportion
Sometimes our hypothesis involves making a statement about the
population or simply validating a statistic presented by other people.
The following shows how we can test such proposition with a sample.
tration: The 2010 FIES (Family Income Expenditure Survey) claims that
30% of Filpino Families live below the poverty line. You want to
see if the data has changed since then. Thus, you surveyed 500
families and found that 25% of the families now live below the
poverty line. Is this change significantly different from the FIES
findings at a=0.05?
A. Given
P = 30% n=500
sample proportion(p)= 25%
B. Problem
Population proportion vs. sample proportion
C. Hypothesis
Ho: p = 30%
Ha: p# 30%
D. Formula and Test Statistic
0.25 – 0.30
= -2.43
p-P
Vpq/n z
, ίo 200 70 (500
||
Transcribed Image Text:the 2010 2 Qualitative Data: Testing for Proportions or Frequencies Learning outcomes: 1. Examine the statistical technique for qualitative data 2. Identify the sample proportions for parametric data 3. describe and compare the equal and unequal frequencies for the chi-square . Qualitative Data: Testing for Proportions or Frequencies Remember that there are two kinds of data; scores and frequ cies. The previous discussion on hypothesis testing all involved observations that were measured quantitatively and we treated the data using its mean. This time, we focus our discussion on testing hypothesis when frequencies (we just count the number of observations) or proportions (percentages) are concerned. B.2.2.1 Testing a Claim About a Proportion Sometimes our hypothesis involves making a statement about the population or simply validating a statistic presented by other people. The following shows how we can test such proposition with a sample. tration: The 2010 FIES (Family Income Expenditure Survey) claims that 30% of Filpino Families live below the poverty line. You want to see if the data has changed since then. Thus, you surveyed 500 families and found that 25% of the families now live below the poverty line. Is this change significantly different from the FIES findings at a=0.05? A. Given P = 30% n=500 sample proportion(p)= 25% B. Problem Population proportion vs. sample proportion C. Hypothesis Ho: p = 30% Ha: p# 30% D. Formula and Test Statistic 0.25 – 0.30 = -2.43 p-P Vpq/n z , ίo 200 70 (500 ||
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