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A psych research group found that 25 participants who were in a program with physical exercise regimen after therapy showed 32% rate of symptom relapse from depression while the Journal of Health Psychology reported that those who were given only anti-depressants as maintenance showed 40% relapse. Can the psych research group conclude that physical exercise has a lower rate of symptom relapse ata 0.01? A. Given B. Problem C. Hypothesis

A psych research group found that 25 participants who were in a program with physical exercise regimen after therapy showed 32% rate of symptom relapse from depression while the Journal of Health Psychology reported that those who were given only anti-depressants as maintenance showed 40% relapse. Can the psych research group conclude that physical exercise has a lower rate of symptom relapse ata 0.01? A. Given B. Problem C. Hypothesis

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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A psych research group found that 25 participants who were in a program with physical exercise regimen after therapy showed 32% rate of symptom relapse from depression while the Journal of Health Psychology reported that those who were given only anti-depressants as maintenance showed 40% relapse. Can the psych research group conclude that physical exercise has a lower rate of symptom relapse ata 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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