This document describes a two-way factorial ANOVA analysis that examined the effects of caffeine (150mg dose vs none) and exercise (30 minutes vs none) on heart rate using a data set of 16 subjects. It was found that both caffeine and exercise significantly increased heart rate on their own, but there was no interaction between the two factors. Assumptions of independence, normality and equal variance were tested and met. While the analysis provided insights, the small sample size of 16 limits the strength of conclusions that can be drawn.
This randomized trial compared a double dose versus standard dose of clopidogrel and a high dose versus low dose of aspirin in over 25,000 patients undergoing percutaneous coronary intervention. It found that a 7-day double dose of clopidogrel was more effective at preventing cardiovascular death, heart attack, or stroke compared to the standard dose, with no increase in fatal or intracranial bleeding. However, high-dose aspirin did not provide additional benefits over low-dose aspirin for preventing these outcomes.
This study applied doubly robust estimation to assess the causal effect of angiotensin converting enzyme inhibitors (ACEIs) versus angiotensin receptor blockers (ARBs) on follow-up hemoglobin (Hgb) levels. The study found evidence of confounding factors like heart failure status and sex that differed between treatment groups and affected Hgb levels. Doubly robust estimation was used to estimate average causal effects while addressing confounding. The results suggested that average follow-up Hgb levels may be higher when ARBs rather than ACEIs are prescribed, though the mean difference was small and not clearly clinically significant. Further analysis was recommended to refine the models.
Nuts & Bolts of Systematic Reviews, Meta-analyses & Network Meta-analysesOARSI
Director, Applied Health Research Centre (AHRC)
Li Ka Shing Knowledge Institute, St. Michael’s Hospital
Professor, Department of Medicine & IHPME, University of Toronto
Tier 1 Canada Research Chair in Clinical Epidemiology of Chronic Diseases
Comparative study of sympathetic cardiovascular parameters in overweight, nor...IOSR Journals
This study was undertaken to investigate and compare the sympathetic cardio vascular parameters in age matched overweight, underweight and normal weight school going boys in southern Odisha. 75 Boys between age group of 12-16 were subjected to study out of which 25 were overweight (BMI>25), next 25 were underweight(BMI<18.5),rest 25 were control group having normal BMI. Cold pressure test and hand grip dynamometer test were performed and blood pressure was measured during and after the tests as measures of cardiovascular parameter. Baseline SBP and MAP were significantly higher in overweight boys & lower in underweight boys. Maximum rise of SBP, DBP & MAP during hand grip dynamometer test were significantly higher in overweight boys & lower in underweight boys. Increase in SBP & MAP from their basal value during cold pressure test were significantly lower in overweight boys & higher in underweight boys. Thus it is concluded that both overweight & underweight boys have derangement of sympathetic cardiovascular function. SBP- Systolic blood pressure, DBP- Diastolic blood pressure , MAP- Mean arterial pressure
This document discusses the history and evolution of vasopressor use for treating maternal hypotension during spinal anesthesia for cesarean section. It describes how ephedrine was originally used but was found to be associated with worse fetal outcomes compared to phenylephrine or metaraminol. Phenylephrine then emerged as the preferred vasopressor due to studies showing it improved fetal acid-base status. Recent research has focused on optimizing phenylephrine administration, comparing continuous infusions to bolus doses and investigating optimal infusion rates and regimens. However, the ideal method to both control blood pressure and minimize side effects like hypertension is still unclear.
1. A randomized controlled trial found that high-dose allopurinol significantly prolonged exercise capacity in patients with stable angina compared to placebo by prolonging time to chest pain, ST depression, and total exercise time.
2. Allopurinol seems to reduce myocardial oxygen demand without reducing cardiac output through its antioxidant and endothelial protective effects. This may underlie its ability to reduce angina symptoms during exercise.
3. Further research is still needed to fully understand the mechanisms of allopurinol's anti-ischemic effects and define its optimal role in treating angina.
The document summarizes key points from the 2017 ACC/AHA Hypertension Guidelines, including introducing new blood pressure definitions and calculating cardiovascular risk to determine treatment goals. It outlines recommending four classes of antihypertensive medications as first-line treatment, with beta-blockers no longer recommended as first-line unless indicated. The document also reviews blood pressure measurement techniques and emphasizing lifestyle modifications like diet and exercise.
This document provides an overview of basic statistics, including descriptive and inferential statistics. Descriptive statistics such as frequencies, percentages, means and standard deviations are used to summarize single variables. Inferential statistics such as correlation, t-tests, chi-square, and logistic regression are used to test hypotheses, determine associations between variables, and make predictions. The document explains when each statistical test is appropriate and how to interpret and report the results.
This randomized trial compared a double dose versus standard dose of clopidogrel and a high dose versus low dose of aspirin in over 25,000 patients undergoing percutaneous coronary intervention. It found that a 7-day double dose of clopidogrel was more effective at preventing cardiovascular death, heart attack, or stroke compared to the standard dose, with no increase in fatal or intracranial bleeding. However, high-dose aspirin did not provide additional benefits over low-dose aspirin for preventing these outcomes.
This study applied doubly robust estimation to assess the causal effect of angiotensin converting enzyme inhibitors (ACEIs) versus angiotensin receptor blockers (ARBs) on follow-up hemoglobin (Hgb) levels. The study found evidence of confounding factors like heart failure status and sex that differed between treatment groups and affected Hgb levels. Doubly robust estimation was used to estimate average causal effects while addressing confounding. The results suggested that average follow-up Hgb levels may be higher when ARBs rather than ACEIs are prescribed, though the mean difference was small and not clearly clinically significant. Further analysis was recommended to refine the models.
Nuts & Bolts of Systematic Reviews, Meta-analyses & Network Meta-analysesOARSI
Director, Applied Health Research Centre (AHRC)
Li Ka Shing Knowledge Institute, St. Michael’s Hospital
Professor, Department of Medicine & IHPME, University of Toronto
Tier 1 Canada Research Chair in Clinical Epidemiology of Chronic Diseases
Comparative study of sympathetic cardiovascular parameters in overweight, nor...IOSR Journals
This study was undertaken to investigate and compare the sympathetic cardio vascular parameters in age matched overweight, underweight and normal weight school going boys in southern Odisha. 75 Boys between age group of 12-16 were subjected to study out of which 25 were overweight (BMI>25), next 25 were underweight(BMI<18.5),rest 25 were control group having normal BMI. Cold pressure test and hand grip dynamometer test were performed and blood pressure was measured during and after the tests as measures of cardiovascular parameter. Baseline SBP and MAP were significantly higher in overweight boys & lower in underweight boys. Maximum rise of SBP, DBP & MAP during hand grip dynamometer test were significantly higher in overweight boys & lower in underweight boys. Increase in SBP & MAP from their basal value during cold pressure test were significantly lower in overweight boys & higher in underweight boys. Thus it is concluded that both overweight & underweight boys have derangement of sympathetic cardiovascular function. SBP- Systolic blood pressure, DBP- Diastolic blood pressure , MAP- Mean arterial pressure
This document discusses the history and evolution of vasopressor use for treating maternal hypotension during spinal anesthesia for cesarean section. It describes how ephedrine was originally used but was found to be associated with worse fetal outcomes compared to phenylephrine or metaraminol. Phenylephrine then emerged as the preferred vasopressor due to studies showing it improved fetal acid-base status. Recent research has focused on optimizing phenylephrine administration, comparing continuous infusions to bolus doses and investigating optimal infusion rates and regimens. However, the ideal method to both control blood pressure and minimize side effects like hypertension is still unclear.
1. A randomized controlled trial found that high-dose allopurinol significantly prolonged exercise capacity in patients with stable angina compared to placebo by prolonging time to chest pain, ST depression, and total exercise time.
2. Allopurinol seems to reduce myocardial oxygen demand without reducing cardiac output through its antioxidant and endothelial protective effects. This may underlie its ability to reduce angina symptoms during exercise.
3. Further research is still needed to fully understand the mechanisms of allopurinol's anti-ischemic effects and define its optimal role in treating angina.
The document summarizes key points from the 2017 ACC/AHA Hypertension Guidelines, including introducing new blood pressure definitions and calculating cardiovascular risk to determine treatment goals. It outlines recommending four classes of antihypertensive medications as first-line treatment, with beta-blockers no longer recommended as first-line unless indicated. The document also reviews blood pressure measurement techniques and emphasizing lifestyle modifications like diet and exercise.
This document provides an overview of basic statistics, including descriptive and inferential statistics. Descriptive statistics such as frequencies, percentages, means and standard deviations are used to summarize single variables. Inferential statistics such as correlation, t-tests, chi-square, and logistic regression are used to test hypotheses, determine associations between variables, and make predictions. The document explains when each statistical test is appropriate and how to interpret and report the results.
The document describes maintenance free TAB EPzV liquid batteries that are new high performance traction batteries. The batteries are maintenance free, have extremely low self-discharge and gassing, and minimize maintenance errors by preventing electrolyte leakage even if cells are damaged. The batteries can be used for forklift trucks, electric vehicles, cleaning machines, telecom towers, UPS systems, and power generation or distribution applications.
InterTrade Distributors is an importer, exporter, manufacturer's representative, and approved contractor to the government and defense. The document announces that prices of genuine filters from TCM have been reduced by approximately 40-50% due to the low yen value and good discount from TCM. It encourages recipients to book their filter requirements as soon as possible before stock runs out.
This document certifies that Prasanth Ekkoratha has passed the Oracle Certified Professional, Java SE 7 Programmer exam with an identification number of 228090576OCPJSE7 as of June 06, 2014.
O documento descreve os detalhes da 1a etapa do Campeonato Estadual de MTB, incluindo os equipamentos obrigatórios, o local e horários das provas para diferentes categorias, as áreas para atletas e mecânica, e os contatos em caso de dúvidas.
This document advertises high quality Petro Canada lubricants that can be purchased online from InterTrade Distributors. It lists several major car and engine manufacturers that approve of their oils, including Mercedes Benz, Volvo, Toyota, and Cummins. Contact information is provided for InterTrade Distributors' head office in Karachi and branch office in Lahore.
The Capella University Doctor of Education in Curriculum and Instruction program requires learners to complete 15 courses totaling 90 quarter credits. The core courses cover topics such as leadership development, action research, organizational dynamics and change, supervision and evaluation of curriculum programs, collaboration for curriculum improvement, and applying research to improve curriculum and instruction. Learners also complete a capstone project where they apply scholarly research within a professional context to advance their knowledge in curriculum and instruction.
Chapter 6 Environmental Management Plan EIA Report - La Cambuse Hotelaknl-mauritius
The document summarizes the environmental impacts that may occur during construction and operation of a proposed resort hotel at Le Chaland. During construction, impacts include demolition waste, dust and noise from construction activities, runoff of hydrocarbons from equipment, and waste generation from workers. Operation will impact utilities usage and waste generation. Mitigation measures are proposed, such as waste recycling, runoff controls, and a wastewater treatment plant. Impacts on vegetation and site geology during construction are also assessed.
Essay on school family partnerships bid4papersBid4Papers
This document discusses principles of partnerships between schools and families to assist students, especially those with disabilities, in achieving academic excellence. The key principles discussed include communication, learning commitments, equality, respect, advocacy, professional competence, trust, and promoting access to learning materials. Effective communication and commitments from both schools and families are emphasized as being vital to build trust and support students' development and growth.
This document discusses inferential statistics and provides examples to illustrate key concepts. Inferential statistics involves drawing conclusions about populations from sample data using probability and statistical testing. Common situations where inferential statistics are used include comparing differences between two or more samples, estimating population parameters from samples, and assessing correlations. Key steps involve defining a null hypothesis, choosing an appropriate statistical test based on the type of variable (qualitative or quantitative) and sample size, calculating a test statistic, determining the probability, and interpreting results to either reject or fail to reject the null hypothesis. Examples are provided to demonstrate applying concepts like hypothesis testing, choosing between tests, and interpreting outcomes.
1. The following are body mass index (BMI) scores measured in 12.docxjackiewalcutt
1. The following are body mass index (BMI) scores measured in 12 patients who are free of diabetes and participating in a study of risk factors for obesity. Body mass index is measured as the ratio of weight in kilograms to height in meters squared. Generate a 95% confidence interval estimate of the true BMI.
25
27
31
33
26
28
38
41
24
32
35
40
2. Consider the data in Problem 1. How many subjects would be needed to ensure that a 95% confidence interval estimate of BMI had a margin of error not exceeding 2 units?
3. The mean BMI in patients free of diabetes was reported as 28.2. The investigator conducting the study described in Problem 1 hypothesizes that the BMI in patients free of diabetes is higher. Based on the data in Problem 1 is there evidence that the BMI is significantly higher that 28.2? Use a 5% level of significance.
4. Peak expiratory flow (PEF) is a measure of a patient’s ability to expel air from the lungs. Patients with asthma or other respiratory conditions often have restricted PEF. The mean PEF for children free of asthma is 306. An investigator wants to test whether children with chronic bronchitis have restricted PEF. A sample of 40 children with chronic bronchitis are studied and their mean PEF is 279 with a standard deviation of 71. Is there statistical evidence of a lower mean PEF in children with chronic bronchitis? Run the appropriate test at =0.05.
5. Consider again the study in Problem 4, a different investigator conducts a second study to investigate whether there is a difference in mean PEF in children with chronic bronchitis as compared to those without. Data on PEF are collected and summarized below. Based on the data, is there statistical evidence of a lower mean PEF in children with chronic bronchitis as compared to those without? Run the appropriate test at =0.05.
Group
Number of Children
Mean PEF
Std Dev PEF
Chronic Bronchitis
25
281
68
No Chronic Bronchitis
25
319
74
6. Using the data presented in Problem 5,
a) Construct a 95% confidence interval for the mean PEF in children without chronic bronchitis.
b) How many children would be required to ensure that the margin of error in (a) does not exceed 10 units?
7. A clinical trial is run to investigate the effectiveness of an experimental drug in reducing preterm delivery to a drug considered standard care and to placebo. Pregnant women are enrolled and randomly assigned to receive either the experimental drug, the standard drug or placebo. Women are followed through delivery and classified as delivering preterm (< 37 weeks) or not. The data are shown below.
Preterm Delivery
Experimental Drug
Standard Drug
Placebo
Yes
17
23
35
No
83
77
65
Is there a statistically significant difference in the proportions of women delivering preterm among the three treatment groups? Run the test at a 5% level of significance.
8. Using the data in Problem 7, generate a 95% confidence interval for the difference in proportions of women ...
Summary of clinical investigations es teck complex systemES-Teck India
Summary of the clinical investigations E.S.Teck Complex March, 20, 2010
Summary of Clinical Investigations ES Teck Complex system EIS System in adjunct to Treatments’ monitoring and to diagnosis with the conventional methods
The document provides information on using SPSS and PSPP statistical software to analyze data and conduct statistical tests. It includes 4 lessons:
1. How to define and input data into the software.
2. How to generate descriptive statistics like measures of central tendency and variability to describe data.
3. How to examine relationships between variables using correlation, regression, and graphs.
4. How to perform statistical inference tests for means using one-sample t-tests, independent two-sample t-tests, and paired t-tests. Examples of hypotheses testing and interpreting results are provided.
Running head DATA ANALYSIS AND APPLICATION .docxsusanschei
Running head: DATA ANALYSIS AND APPLICATION 1
DATA ANALYSIS AND APPLICATION 12
Data Analysis and Application (DAA): U02A1
MYSTERY STUDENT OF THE WEEK!
Capella University
Data Analysis and Application (DAA): U02A1
In conducting an independent samples t test, it is important to consider the assumptions, and to analyze the data to determine if the assumptions have been met, especially with regard to the variance between group means. Additionally, for the purpose of this assignment, a post-hoc and priori power analysis will assist in determining whether the data can be credibility used in research. Type I and Type II errors can be detrimental to research in the field of psychology and should be carefully avoided; this assignment will address measures that can be taken to avoid such errors.
Section 1: Reporting the t Test Results
In this particular analysis of the bpstudy.sav data set, researchers investigated data from 65 participants. With gender as a predictor variable and heart rate (HR1) as the outcome variable, a t test analysis compared mean heart rates using interval level data for both male and female participants. This data set also contained participant’s smoking status (categorical, nominal data), as well as their weight and systolic/diastolic pressure (interval level data).
Using an independent samples t test, researchers were able to compare mean female heart rates with mean male heart rates, to determine if a significant difference exists among mean heart rates as it relates to the gender variable. Gender, a traditionally dichotomous variable, is a meaningless variable, and as Warner (2013) explains, “it would be nonsense to add up scores for a nominal variable… and calculate a mean… based on the sum of those scores”; therefore, mean gender score was not analyzed in this research (p. 7). Field (2014) explains that variables like gender, ethnicity and other characteristic variables used to identify participants are often collected using nominal, data and in descriptive statistics is not usually relevant to the analysis process (Field, 2013, p. 8). Of the 65 samples collected, 28 were male (N₁) and 36 were female (N₂) with one misidentified gender; for the purpose of this research, participant 11, whose gender is listed as “3”, is automatically selected out and will not be considered for this t test analysis (Warner, 2013, p. 137). As outlined in Table 1 below, the mean heart rate for males was 73.68 beats per minute (BPM) and the mean heart rate for females was 74.97 BPM. The standard deviation for the male heart rate was 9.77 (s₁), rounded to the nearest hundredth, and for the female heart rate it was 7.87 (s₂). The mean difference, (M₁-M₂) is -1.29. These values are essential for computing effect size.
Table 1
Descriptive statistics for heart rate by gender
Group ...
Statistical Analysis is complex part but reporting of data in proper manner with proper selective graphs & interpretations is also necessary part of data analysis !!!
The document discusses choosing appropriate statistical tests for analyzing medical research studies. It provides an overview of commonly used statistical tests such as the t-test, chi-square test, Fisher's exact test, analysis of variance, and Wilcoxon rank sum test. The document outlines the key factors to consider when selecting a statistical test, such as the scale of measurement (continuous, categorical, binary) and study design (paired or unpaired). Algorithms and tables are provided to help readers identify the proper statistical test based on these characteristics.
Chapter 11 Chi-Square Tests and ANOVA 359 Chapter .docxbartholomeocoombs
Chapter 11: Chi-Square Tests and ANOVA
359
Chapter 11: Chi-Square and ANOVA Tests
This chapter presents material on three more hypothesis tests. One is used to determine
significant relationship between two qualitative variables, the second is used to determine
if the sample data has a particular distribution, and the last is used to determine
significant relationships between means of 3 or more samples.
Section 11.1: Chi-Square Test for Independence
Remember, qualitative data is where you collect data on individuals that are categories or
names. Then you would count how many of the individuals had particular qualities. An
example is that there is a theory that there is a relationship between breastfeeding and
autism. To determine if there is a relationship, researchers could collect the time period
that a mother breastfed her child and if that child was diagnosed with autism. Then you
would have a table containing this information. Now you want to know if each cell is
independent of each other cell. Remember, independence says that one event does not
affect another event. Here it means that having autism is independent of being breastfed.
What you really want is to see if they are not independent. In other words, does one
affect the other? If you were to do a hypothesis test, this is your alternative hypothesis
and the null hypothesis is that they are independent. There is a hypothesis test for this
and it is called the Chi-Square Test for Independence. Technically it should be called
the Chi-Square Test for Dependence, but for historical reasons it is known as the test for
independence. Just as with previous hypothesis tests, all the steps are the same except for
the assumptions and the test statistic.
Hypothesis Test for Chi-Square Test
1. State the null and alternative hypotheses and the level of significance
Ho : the two variables are independent (this means that the one variable is not
affected by the other)
HA : the two variables are dependent (this means that the one variable is affected
by the other)
Also, state your α level here.
2. State and check the assumptions for the hypothesis test
a. A random sample is taken.
b. Expected frequencies for each cell are greater than or equal to 5 (The expected
frequencies, E, will be calculated later, and this assumption means E ≥ 5 ).
3. Find the test statistic and p-value
Finding the test statistic involves several steps. First the data is collected and
counted, and then it is organized into a table (in a table each entry is called a cell).
These values are known as the observed frequencies, which the symbol for an
observed frequency is O. Each table is made up of rows and columns. Then each
row is totaled to give a row total and each column is totaled to give a column
total.
Chapter 11: Chi-Squared Tests and ANOVA
360
The null hypothesis is that the variables are independent. Using the multiplication.
This document provides an overview of parametric statistical tests, including the t-test, ANOVA, Pearson's correlation coefficient, and Z-test. It describes the assumptions, calculations, and procedures for each test. The t-test is used to compare means of small samples and can be used for one sample, two independent samples, or paired samples. ANOVA allows comparison of multiple population means and is used when more than two groups are involved. Pearson's correlation measures the strength of association between two continuous variables. The Z-test, which is used for larger samples, can be applied to compare means or proportions.
This document provides a summary of analyses conducted on a secondary dataset about alcohol consumption among UK secondary school students. Descriptive statistics show that the average age was 13, with roughly equal proportions of boys and girls. 43% reported ever drinking alcohol. Hypothesis testing found the proportion who drank was less than the reported UK rate of 45%. Chi-square analysis revealed a weak association between peer pressure and drinking frequency. Correlation found a weak negative relationship between family attitude toward drinking and student drinking frequency. The document describes and interprets the results of these analyses to understand patterns of underage alcohol use.
Nonparametric Test Chi-Square Test for Independence Th.docxpauline234567
Nonparametric Test
Chi-Square Test for Independence
The test is used to determine whether two categorical variables are independent.
Notation for the Chi-Square Test for Independence (Please note that the notation varies
depending on the text)
O represents the observed frequency of an outcome
E represents the expected frequency of an outcome
r represents the number of rows in the contingency table
c represents the number of columns in the contingency table
n represents the total number of trials
Test Statistic
2
2
1 1
O E
E
df r c
The Chi-Square test is a hypothesis test. There are seven steps for a hypothesis test.
1. State the null hypothesis
2. State the alternative hypothesis
3. State the level of significance
4. State the test statistic
5. Calculate
6. Statistical Conclusion
7. Experimental Conclusion
Example
A university is interested to know if the choice of major has a relationship to gender. A
random sample of 200 incoming freshmen students was taken (100 male and 100
female). There major and gender were recorded. The results are shown in the
contingency table below.
Major Female Male
Math 5 15
Nursing 44 10
English 10 10
Pre-Med 17 20
History 4 5
Education 15 20
Undecided 5 20
To determine if there is a relationship between the gender of a freshmen student and
thei declared major perform the hypothesis test (Use level of significance 0.05 ) .
Step 1: Null Hypothesis
0 : Gender and Major of Freshmen students are independentH
Step 2: Alternative Hypothesis
: Gender and Major of Freshmen students are not independentAH
Step 3: Level of Significance
0.05
Step 4: Test Statistic
2
2
1 1
O E
E
df r c
Step 5: Calculations
There are several calculations for this test. We have to find the expected frequency for
each cell in the contingency table. The expected frequency is the probability under the
null hypothesis times the total frequency for the given row. Here the probability under
the null hypothesis is .5, as the probability of being male and female is equal.
rE pn
Major Female Male
Math 1 .5 20 10E 2 .5 20 10E
Nursing 3 .5 54 27E 4 .5 54 27E
English 5 .5 20 10E 6 .5 20 10E
Pre-Med 7 .5 37 18.5E 8 .5 37 18.5E
History 9 .5 9 4.5E 10 .5 9 4.5E
Education 11 .5 35 17.5E 12 .5 35 17.5E
Undecided 13 .5 25 12.5E 14 .5 25 12.5E
Know calculate the test statistic.
2
2
2 2 2 2
2
2 2 2 2
2 2 2 2
2 2
2
5 10 15 10 44 27 10 27
10 10 27 27
10 10 10 10 17 18.5 20 18.5
10 10 18.5 18.5
4 4.5 5 4.5 15 17.5 20 17.5
4.5 4.5 17.5 17.5
5 12.5 20 12.5
12.5 12.5
2.5 2.5 10.7 10.7 0 0 .1216
obs
ob.
The document describes maintenance free TAB EPzV liquid batteries that are new high performance traction batteries. The batteries are maintenance free, have extremely low self-discharge and gassing, and minimize maintenance errors by preventing electrolyte leakage even if cells are damaged. The batteries can be used for forklift trucks, electric vehicles, cleaning machines, telecom towers, UPS systems, and power generation or distribution applications.
InterTrade Distributors is an importer, exporter, manufacturer's representative, and approved contractor to the government and defense. The document announces that prices of genuine filters from TCM have been reduced by approximately 40-50% due to the low yen value and good discount from TCM. It encourages recipients to book their filter requirements as soon as possible before stock runs out.
This document certifies that Prasanth Ekkoratha has passed the Oracle Certified Professional, Java SE 7 Programmer exam with an identification number of 228090576OCPJSE7 as of June 06, 2014.
O documento descreve os detalhes da 1a etapa do Campeonato Estadual de MTB, incluindo os equipamentos obrigatórios, o local e horários das provas para diferentes categorias, as áreas para atletas e mecânica, e os contatos em caso de dúvidas.
This document advertises high quality Petro Canada lubricants that can be purchased online from InterTrade Distributors. It lists several major car and engine manufacturers that approve of their oils, including Mercedes Benz, Volvo, Toyota, and Cummins. Contact information is provided for InterTrade Distributors' head office in Karachi and branch office in Lahore.
The Capella University Doctor of Education in Curriculum and Instruction program requires learners to complete 15 courses totaling 90 quarter credits. The core courses cover topics such as leadership development, action research, organizational dynamics and change, supervision and evaluation of curriculum programs, collaboration for curriculum improvement, and applying research to improve curriculum and instruction. Learners also complete a capstone project where they apply scholarly research within a professional context to advance their knowledge in curriculum and instruction.
Chapter 6 Environmental Management Plan EIA Report - La Cambuse Hotelaknl-mauritius
The document summarizes the environmental impacts that may occur during construction and operation of a proposed resort hotel at Le Chaland. During construction, impacts include demolition waste, dust and noise from construction activities, runoff of hydrocarbons from equipment, and waste generation from workers. Operation will impact utilities usage and waste generation. Mitigation measures are proposed, such as waste recycling, runoff controls, and a wastewater treatment plant. Impacts on vegetation and site geology during construction are also assessed.
Essay on school family partnerships bid4papersBid4Papers
This document discusses principles of partnerships between schools and families to assist students, especially those with disabilities, in achieving academic excellence. The key principles discussed include communication, learning commitments, equality, respect, advocacy, professional competence, trust, and promoting access to learning materials. Effective communication and commitments from both schools and families are emphasized as being vital to build trust and support students' development and growth.
This document discusses inferential statistics and provides examples to illustrate key concepts. Inferential statistics involves drawing conclusions about populations from sample data using probability and statistical testing. Common situations where inferential statistics are used include comparing differences between two or more samples, estimating population parameters from samples, and assessing correlations. Key steps involve defining a null hypothesis, choosing an appropriate statistical test based on the type of variable (qualitative or quantitative) and sample size, calculating a test statistic, determining the probability, and interpreting results to either reject or fail to reject the null hypothesis. Examples are provided to demonstrate applying concepts like hypothesis testing, choosing between tests, and interpreting outcomes.
1. The following are body mass index (BMI) scores measured in 12.docxjackiewalcutt
1. The following are body mass index (BMI) scores measured in 12 patients who are free of diabetes and participating in a study of risk factors for obesity. Body mass index is measured as the ratio of weight in kilograms to height in meters squared. Generate a 95% confidence interval estimate of the true BMI.
25
27
31
33
26
28
38
41
24
32
35
40
2. Consider the data in Problem 1. How many subjects would be needed to ensure that a 95% confidence interval estimate of BMI had a margin of error not exceeding 2 units?
3. The mean BMI in patients free of diabetes was reported as 28.2. The investigator conducting the study described in Problem 1 hypothesizes that the BMI in patients free of diabetes is higher. Based on the data in Problem 1 is there evidence that the BMI is significantly higher that 28.2? Use a 5% level of significance.
4. Peak expiratory flow (PEF) is a measure of a patient’s ability to expel air from the lungs. Patients with asthma or other respiratory conditions often have restricted PEF. The mean PEF for children free of asthma is 306. An investigator wants to test whether children with chronic bronchitis have restricted PEF. A sample of 40 children with chronic bronchitis are studied and their mean PEF is 279 with a standard deviation of 71. Is there statistical evidence of a lower mean PEF in children with chronic bronchitis? Run the appropriate test at =0.05.
5. Consider again the study in Problem 4, a different investigator conducts a second study to investigate whether there is a difference in mean PEF in children with chronic bronchitis as compared to those without. Data on PEF are collected and summarized below. Based on the data, is there statistical evidence of a lower mean PEF in children with chronic bronchitis as compared to those without? Run the appropriate test at =0.05.
Group
Number of Children
Mean PEF
Std Dev PEF
Chronic Bronchitis
25
281
68
No Chronic Bronchitis
25
319
74
6. Using the data presented in Problem 5,
a) Construct a 95% confidence interval for the mean PEF in children without chronic bronchitis.
b) How many children would be required to ensure that the margin of error in (a) does not exceed 10 units?
7. A clinical trial is run to investigate the effectiveness of an experimental drug in reducing preterm delivery to a drug considered standard care and to placebo. Pregnant women are enrolled and randomly assigned to receive either the experimental drug, the standard drug or placebo. Women are followed through delivery and classified as delivering preterm (< 37 weeks) or not. The data are shown below.
Preterm Delivery
Experimental Drug
Standard Drug
Placebo
Yes
17
23
35
No
83
77
65
Is there a statistically significant difference in the proportions of women delivering preterm among the three treatment groups? Run the test at a 5% level of significance.
8. Using the data in Problem 7, generate a 95% confidence interval for the difference in proportions of women ...
Summary of clinical investigations es teck complex systemES-Teck India
Summary of the clinical investigations E.S.Teck Complex March, 20, 2010
Summary of Clinical Investigations ES Teck Complex system EIS System in adjunct to Treatments’ monitoring and to diagnosis with the conventional methods
The document provides information on using SPSS and PSPP statistical software to analyze data and conduct statistical tests. It includes 4 lessons:
1. How to define and input data into the software.
2. How to generate descriptive statistics like measures of central tendency and variability to describe data.
3. How to examine relationships between variables using correlation, regression, and graphs.
4. How to perform statistical inference tests for means using one-sample t-tests, independent two-sample t-tests, and paired t-tests. Examples of hypotheses testing and interpreting results are provided.
Running head DATA ANALYSIS AND APPLICATION .docxsusanschei
Running head: DATA ANALYSIS AND APPLICATION 1
DATA ANALYSIS AND APPLICATION 12
Data Analysis and Application (DAA): U02A1
MYSTERY STUDENT OF THE WEEK!
Capella University
Data Analysis and Application (DAA): U02A1
In conducting an independent samples t test, it is important to consider the assumptions, and to analyze the data to determine if the assumptions have been met, especially with regard to the variance between group means. Additionally, for the purpose of this assignment, a post-hoc and priori power analysis will assist in determining whether the data can be credibility used in research. Type I and Type II errors can be detrimental to research in the field of psychology and should be carefully avoided; this assignment will address measures that can be taken to avoid such errors.
Section 1: Reporting the t Test Results
In this particular analysis of the bpstudy.sav data set, researchers investigated data from 65 participants. With gender as a predictor variable and heart rate (HR1) as the outcome variable, a t test analysis compared mean heart rates using interval level data for both male and female participants. This data set also contained participant’s smoking status (categorical, nominal data), as well as their weight and systolic/diastolic pressure (interval level data).
Using an independent samples t test, researchers were able to compare mean female heart rates with mean male heart rates, to determine if a significant difference exists among mean heart rates as it relates to the gender variable. Gender, a traditionally dichotomous variable, is a meaningless variable, and as Warner (2013) explains, “it would be nonsense to add up scores for a nominal variable… and calculate a mean… based on the sum of those scores”; therefore, mean gender score was not analyzed in this research (p. 7). Field (2014) explains that variables like gender, ethnicity and other characteristic variables used to identify participants are often collected using nominal, data and in descriptive statistics is not usually relevant to the analysis process (Field, 2013, p. 8). Of the 65 samples collected, 28 were male (N₁) and 36 were female (N₂) with one misidentified gender; for the purpose of this research, participant 11, whose gender is listed as “3”, is automatically selected out and will not be considered for this t test analysis (Warner, 2013, p. 137). As outlined in Table 1 below, the mean heart rate for males was 73.68 beats per minute (BPM) and the mean heart rate for females was 74.97 BPM. The standard deviation for the male heart rate was 9.77 (s₁), rounded to the nearest hundredth, and for the female heart rate it was 7.87 (s₂). The mean difference, (M₁-M₂) is -1.29. These values are essential for computing effect size.
Table 1
Descriptive statistics for heart rate by gender
Group ...
Statistical Analysis is complex part but reporting of data in proper manner with proper selective graphs & interpretations is also necessary part of data analysis !!!
The document discusses choosing appropriate statistical tests for analyzing medical research studies. It provides an overview of commonly used statistical tests such as the t-test, chi-square test, Fisher's exact test, analysis of variance, and Wilcoxon rank sum test. The document outlines the key factors to consider when selecting a statistical test, such as the scale of measurement (continuous, categorical, binary) and study design (paired or unpaired). Algorithms and tables are provided to help readers identify the proper statistical test based on these characteristics.
Chapter 11 Chi-Square Tests and ANOVA 359 Chapter .docxbartholomeocoombs
Chapter 11: Chi-Square Tests and ANOVA
359
Chapter 11: Chi-Square and ANOVA Tests
This chapter presents material on three more hypothesis tests. One is used to determine
significant relationship between two qualitative variables, the second is used to determine
if the sample data has a particular distribution, and the last is used to determine
significant relationships between means of 3 or more samples.
Section 11.1: Chi-Square Test for Independence
Remember, qualitative data is where you collect data on individuals that are categories or
names. Then you would count how many of the individuals had particular qualities. An
example is that there is a theory that there is a relationship between breastfeeding and
autism. To determine if there is a relationship, researchers could collect the time period
that a mother breastfed her child and if that child was diagnosed with autism. Then you
would have a table containing this information. Now you want to know if each cell is
independent of each other cell. Remember, independence says that one event does not
affect another event. Here it means that having autism is independent of being breastfed.
What you really want is to see if they are not independent. In other words, does one
affect the other? If you were to do a hypothesis test, this is your alternative hypothesis
and the null hypothesis is that they are independent. There is a hypothesis test for this
and it is called the Chi-Square Test for Independence. Technically it should be called
the Chi-Square Test for Dependence, but for historical reasons it is known as the test for
independence. Just as with previous hypothesis tests, all the steps are the same except for
the assumptions and the test statistic.
Hypothesis Test for Chi-Square Test
1. State the null and alternative hypotheses and the level of significance
Ho : the two variables are independent (this means that the one variable is not
affected by the other)
HA : the two variables are dependent (this means that the one variable is affected
by the other)
Also, state your α level here.
2. State and check the assumptions for the hypothesis test
a. A random sample is taken.
b. Expected frequencies for each cell are greater than or equal to 5 (The expected
frequencies, E, will be calculated later, and this assumption means E ≥ 5 ).
3. Find the test statistic and p-value
Finding the test statistic involves several steps. First the data is collected and
counted, and then it is organized into a table (in a table each entry is called a cell).
These values are known as the observed frequencies, which the symbol for an
observed frequency is O. Each table is made up of rows and columns. Then each
row is totaled to give a row total and each column is totaled to give a column
total.
Chapter 11: Chi-Squared Tests and ANOVA
360
The null hypothesis is that the variables are independent. Using the multiplication.
This document provides an overview of parametric statistical tests, including the t-test, ANOVA, Pearson's correlation coefficient, and Z-test. It describes the assumptions, calculations, and procedures for each test. The t-test is used to compare means of small samples and can be used for one sample, two independent samples, or paired samples. ANOVA allows comparison of multiple population means and is used when more than two groups are involved. Pearson's correlation measures the strength of association between two continuous variables. The Z-test, which is used for larger samples, can be applied to compare means or proportions.
This document provides a summary of analyses conducted on a secondary dataset about alcohol consumption among UK secondary school students. Descriptive statistics show that the average age was 13, with roughly equal proportions of boys and girls. 43% reported ever drinking alcohol. Hypothesis testing found the proportion who drank was less than the reported UK rate of 45%. Chi-square analysis revealed a weak association between peer pressure and drinking frequency. Correlation found a weak negative relationship between family attitude toward drinking and student drinking frequency. The document describes and interprets the results of these analyses to understand patterns of underage alcohol use.
Nonparametric Test Chi-Square Test for Independence Th.docxpauline234567
Nonparametric Test
Chi-Square Test for Independence
The test is used to determine whether two categorical variables are independent.
Notation for the Chi-Square Test for Independence (Please note that the notation varies
depending on the text)
O represents the observed frequency of an outcome
E represents the expected frequency of an outcome
r represents the number of rows in the contingency table
c represents the number of columns in the contingency table
n represents the total number of trials
Test Statistic
2
2
1 1
O E
E
df r c
The Chi-Square test is a hypothesis test. There are seven steps for a hypothesis test.
1. State the null hypothesis
2. State the alternative hypothesis
3. State the level of significance
4. State the test statistic
5. Calculate
6. Statistical Conclusion
7. Experimental Conclusion
Example
A university is interested to know if the choice of major has a relationship to gender. A
random sample of 200 incoming freshmen students was taken (100 male and 100
female). There major and gender were recorded. The results are shown in the
contingency table below.
Major Female Male
Math 5 15
Nursing 44 10
English 10 10
Pre-Med 17 20
History 4 5
Education 15 20
Undecided 5 20
To determine if there is a relationship between the gender of a freshmen student and
thei declared major perform the hypothesis test (Use level of significance 0.05 ) .
Step 1: Null Hypothesis
0 : Gender and Major of Freshmen students are independentH
Step 2: Alternative Hypothesis
: Gender and Major of Freshmen students are not independentAH
Step 3: Level of Significance
0.05
Step 4: Test Statistic
2
2
1 1
O E
E
df r c
Step 5: Calculations
There are several calculations for this test. We have to find the expected frequency for
each cell in the contingency table. The expected frequency is the probability under the
null hypothesis times the total frequency for the given row. Here the probability under
the null hypothesis is .5, as the probability of being male and female is equal.
rE pn
Major Female Male
Math 1 .5 20 10E 2 .5 20 10E
Nursing 3 .5 54 27E 4 .5 54 27E
English 5 .5 20 10E 6 .5 20 10E
Pre-Med 7 .5 37 18.5E 8 .5 37 18.5E
History 9 .5 9 4.5E 10 .5 9 4.5E
Education 11 .5 35 17.5E 12 .5 35 17.5E
Undecided 13 .5 25 12.5E 14 .5 25 12.5E
Know calculate the test statistic.
2
2
2 2 2 2
2
2 2 2 2
2 2 2 2
2 2
2
5 10 15 10 44 27 10 27
10 10 27 27
10 10 10 10 17 18.5 20 18.5
10 10 18.5 18.5
4 4.5 5 4.5 15 17.5 20 17.5
4.5 4.5 17.5 17.5
5 12.5 20 12.5
12.5 12.5
2.5 2.5 10.7 10.7 0 0 .1216
obs
ob.
The document discusses hypothesis testing for continuous variables. It covers the specific logic and main steps of hypothesis testing, including setting up the null and alternative hypotheses, selecting a test statistic, determining the p-value, making a decision to reject or not reject the null hypothesis, and drawing a conclusion. An example is provided to illustrate the process of conducting a t-test for one group of data to test if a sample mean is significantly different from a hypothesized population mean.
The document discusses hypothesis testing for continuous variables. It provides examples to illustrate the specific logic and main steps of hypothesis testing, which include setting up the null and alternative hypotheses, selecting a test statistic, calculating its value, determining the p-value, making a decision to reject or not reject the null hypothesis based on the p-value, and stating a conclusion. The t-test is introduced for testing hypotheses about population means using sample means and standard deviations. Examples are provided to demonstrate applying the t-test to test if a sample mean is significantly different from a hypothesized population mean.
1. The document discusses hypothesis testing for continuous variables using a t-test. It provides an example of using a t-test to determine if the mean blood sedimentation of patients differs from a reported value in literature.
2. The main steps of hypothesis testing are outlined: setting up null and alternative hypotheses, selecting a test statistic, determining the p-value, making a decision to reject or not reject the null hypothesis based on the p-value.
3. An example t-test is provided to determine if the mean pulse of healthy males in a mountainous area differs from the reported national average, finding the means are statistically significantly different.
This document discusses different types of abstracts: descriptive, indicative, informative, and structured. Descriptive abstracts provide a brief overview of a document without substantive details. Indicative abstracts summarize review articles by outlining the objective, data sources, study selection criteria, methods, main results, and conclusions. Informative abstracts summarize original research papers by following the document's structure of introduction, methods, results, and discussion sections. Structured abstracts include standardized headings and may use incomplete sentences to help readers quickly understand a study's findings, methods, and conclusions.
This document describes a study that used business analytics software and statistical analysis to establish new reference intervals for 12 common metabolic analytes using a large dataset of patient results from the laboratory. Over 500 patient results were used for each analyte to calculate the central 95th percentile, far exceeding the recommended minimum of 120 samples. The established reference intervals were compared to package insert and current ranges. The new methodology provided robust reference intervals truly representative of the laboratory's patient population in an efficient manner without relying on traditional, more limited approaches or IT resources.
This document discusses various parametric tests used for hypothesis testing with quantitative data, including:
- One-sample t-test to compare a sample mean to a predefined value
- Two-sample t-test to compare means of two independent groups
- Paired t-test to compare means of two related/matched groups
- ANOVA tests to compare means of three or more groups, including one-way and two-way ANOVA
- Assumptions of parametric tests like normal distribution and additive effects are also outlined.
the role of Cochrane collaboration and specifically the menstrual disorder & subfertility group is illustrated . simple explanation how to use cochrane reviews is done.
This document provides an overview of inferential statistics. It defines inferential statistics as using data to make generalizations about a larger population beyond the available sample data. Key points include:
- Inferential statistics uses hypothesis testing and estimation to analyze data. It involves making inferences about a population from a sample.
- Hypothesis testing involves forming a null and alternative hypothesis, setting a significance level, choosing a statistical test, and making a decision to accept or reject the null hypothesis based on the p-value or critical value.
- Estimation provides point estimates and interval estimates like confidence intervals to describe population parameters based on sample data.
- Common inferential statistical tests covered are z-tests, t
1. Running head: DATA ANALYSIS AND APPLICATION 1
Data Analysis and Application
Todd Hale
Capella University
2. Running head: DATA ANALYSIS AND APPLICATION 2
Introduction
For this assignment, a two way factorial ANOVA is completed on a set of data that seeks
to answer the question as to whether the mean heart rate of test subjects are moderated by a
150mg dose of caffeine and if the mean heart rates of test subjects are moderated by whether the
individual engages in physical exercise or not. In addition, the two-way factorial ANOVA will
analyze any interaction between the independent variables of exercise and caffeine. The analysis
will include a descriptive overview of the data, methods used to analyze the data, and an
interpretation of the results.
Data File Description
The data file was downloaded from the Capella University student website. The sample
contains 16 entries (N = 16). The data set includes three variables; “caffeine”, “exercise”, and
“heartrate”. The heart rate variable is the outcome or dependant variable and is a scale variable.
The caffeine variable is considered a nominal/categorical variable with “1” representing no
caffeine ingested by the subject and “2” representing a 150 mg dose of caffeine ingested by the
subject. The exercise variable is also a nominal/categorical variable with “1” representing that
the subject was excluded from exercise and “2” representing that the subject engaged in 30
minutes of exercise. In this experiment, caffeine will be factor “A” and exercise will be factor
“B”.
Testing Assumptions
3. Running head: DATA ANALYSIS AND APPLICATION 3
The ANOVA procedure contains four main assumptions that must be met prior to
performing the analysis (George & Mallery, 2012; Howell, 2011; Warner, 2013). Ensuring these
assumptions are met is paramount to achieving a logical result using the ANOVA procedure.
Independence of observations
The first assumption involves the method in which samples were taken for entry into the
database (George & Mallery, 2012; Howell, 2011; Warner, 2013). For results to be accurate, all
samples must be taken independently from other samples to ensure that no sample is in any way
dependent on another sample. This assumption is simply verified by the researcher to ensure that
this assumption is met when samples are selected. In this case, the researcher is confident that
this assumption has not been violated.
Normality of the Distribution
A second assumption for the ANOVA procedure to produce accurate results is that the
dependant variable is at least interval or ratio level and normally distributed (George & Mallery,
2012; Howell, 2011; Warner, 2013). One can see a histogram of the heart rate variable as figure
1. A visual inspection shows that the data appear to be normally distributed and symmetric. The
distribution is unimodal and does not appear to have a negative or positive skew. In addition, the
visual inspection shows that the distribution does not contain any outliers.
4. Running head: DATA ANALYSIS AND APPLICATION 4
Figure #1
In addition to the histogram for the outcome variable heart rate, one can see the
descriptive statistics for this variable on table 1. As can be seen in table 1, the heart rate variable
has a mean (M = 80) as well as median of 80. The standard deviation is 10.954 (SD = 10.954)
and the variance is 120.
5. Running head: DATA ANALYSIS AND APPLICATION 5
Table #1
Statistics
Heartrate
N
Valid 16
Missing 0
Mean 80.00
Median 80.00
Mode 75a
Std. Deviation 10.954
Variance 120.000
Skewness .000
Std. Error of Skewness .564
Kurtosis -.491
Std. Error of Kurtosis 1.091
Minimum 60
Maximum 100
a. Multiple modes exist. The smallest
value is shown
The distribution has no skew as is evidenced by the skewness statistic of .000. In addition, the
distribution is slightly platykurtic, but still within normal range, with a kurtosis statistic of -.491
as can be seen in table 1. One can see an additional confirmation of normality of the distribution
for the heart rate variable with the result of the Shapiro-Wilk test of normality found in table 2,
W(16) = .983, p < .983.
Table #2
Tests of Normality
Kolmogorov-Smirnova
Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
Heartrate .113 16 .200*
.983 16 .983
*. This is a lower bound of the true significance.
6. Running head: DATA ANALYSIS AND APPLICATION 6
a. Lilliefors Significance Correction
Homogeneity of Variance
The final major assumption when using the ANOVA procedure is the homogeneity of
variance assumption (Howell, 2011; Warner, 2013). This assumption involves ensuring that there
is no significant difference in the variances in the data should be relatively the same. To test this
assumption, the Levene’s homogeneity test is applied to the data. The test result demonstrates
that the heart rate variable does not violate this assumption, F(3,12) = 0, p < 1.00 as can be seen
in table 3.
Table #3
Levene's Test of Equality of Error Variancesa
Dependent Variable: Heartrate
F df1 df2 Sig.
.000 3 12 1.000
Tests the null hypothesis that the error variance of
the dependent variable is equal across groups.
a. Design: Intercept + Caffeine + Exercise +
Caffeine * Exercise
Research Question, Hypotheses, and Alpha Level
In this study, the main research question is “Does caffeine, at a dose of 150 mg or
physical exercise moderate one’s heart rate?” A second research question would be “Is there any
interaction between the two independent variables of caffeine level and exercise on an
individual’s heart rate?” There are three hypothesis which are: H0: µA1 = µA2, H0: µB1 = µB2, and
finally, H0: No A X B Interaction. The alternative hypothesis will: H1: µA1 ≠ µA2, H1: µB1 ≠ µB2 and
finally, H1: There is an A X B Interaction. The alpha level for all testing will be set at .05.
7. Running head: DATA ANALYSIS AND APPLICATION 7
Interpretation
As can be seen in table 6, the grand mean for all data is 80. This is interpreted to be the
average number for all data included in the analysis.
Table 6
1. Grand Mean
Dependent Variable: Heartrate
Mean Std. Error 95% Confidence Interval
Lower Bound Upper Bound
80.000 1.614 76.484 83.516
The mean estimates for the “A” factor caffeine, include 72.5 for the mean of non-caffeine
participants in the study and a mean of 87.5 for those that did ingest caffeine during the study.
This information can be seen in table 7.
Table 7
Estimates
Dependent Variable: Heartrate
Caffeine Mean Std. Error 95% Confidence Interval
Lower Bound Upper Bound
No Caffeine 72.500 2.282 67.528 77.472
150 mg Caffeine 87.500 2.282 82.528 92.472
8. Running head: DATA ANALYSIS AND APPLICATION 8
Additionally, the means for the “no exercise” respondant’s heart rate was 75 while the mean
heart rate for those that did exercise was 85 as can be seen in table 8.
Table 8
Estimates
Dependent Variable: Heartrate
Exercise Mean Std. Error 95% Confidence Interval
Lower Bound Upper Bound
No Exercise 75.000 2.282 70.028 79.972
Half Hour of Exercise 85.000 2.282 80.028 89.972
The between subject affects descriptive statistics can be seen on table 4. As can be seen
on table 4, the mean heart rate for those subjects with no caffeine and no exercise, according to
the data was 67.50. In addition, the mean hear rate for subjects that consumed 150 mg of caffeine
and exercised regularly is 85. This already seem to imply that as one increases caffeine intake or
one increases one’s exercise regimen, one’s heart rate increases.
9. Running head: DATA ANALYSIS AND APPLICATION 9
Table #4
Descriptive Statistics
Dependent Variable: Heartrate
Caffeine Exercise Mean Std. Deviation N
No Caffeine
No Exercise 67.50 6.455 4
Half Hour of Exercise 77.50 6.455 4
Total 72.50 8.018 8
150 mg Caffeine
No Exercise 82.50 6.455 4
Half Hour of Exercise 92.50 6.455 4
Total 87.50 8.018 8
Total
No Exercise 75.00 10.000 8
Half Hour of Exercise 85.00 10.000 8
Total 80.00 10.954 16
Additional results from the ANOVA analysis can be viewed in table 5. The interpretation
of the result of the factorial ANOVA is as follows: the H0 for the “A” factor, caffeine, is rejected
in favor of H1, meaning that there is a difference in the mean heart rate of subjects based on
whether the test subject consumed a 150 mm dose of caffeine, FA(1, 12) = 21.6, p < .001 as can
be seen on table 5. In addition, H0 will be rejected in favor of H1 for the “B” factor, exercise,
indicating that the mean heart rate was different based on whether or not the test subject engaged
in exercise, FB(1,12) = 9.6, p < .009 as can be seen on table 5. Finally, with respect to any
interaction between factor A and factor B, the null hypothesis H0 will not be rejected based on
the results of the ANOVA, FA*B(1,12) = 0, p < 1.00 as can be seen on table 5. Thus this can be
interpreted to mean that the ANOVA failed to find any evidence that would support an
interaction between the caffeine intake and exercise.
10. Running head: DATA ANALYSIS AND APPLICATION 10
Table #5
Tests of Between-Subjects Effects
Dependent Variable: Heartrate
Source Type III Sum
of Squares
df Mean Square F Sig. Partial Eta
Squared
Noncent.
Parameter
Observed
Powerb
Corrected Model 1300.000a
3 433.333 10.400 .001 .722 31.200 .985
Intercept 102400.000 1 102400.000 2457.600 .000 .995 2457.600 1.000
Caffeine 900.000 1 900.000 21.600 .001 .643 21.600 .989
Exercise 400.000 1 400.000 9.600 .009 .444 9.600 .811
Caffeine *
Exercise
.000 1 .000 .000 1.000 .000 .000 .050
Error 500.000 12 41.667
Total 104200.000 16
Corrected Total 1800.000 15
a. R Squared = .722 (Adjusted R Squared = .653)
b. Computed using alpha = .05
One can visually see the trajectory plots for the caffeine by exercise interaction on image
1. The image demonstrates that the lines are parallel to one another further suggesting that there
is no interaction between the variables of caffeine and exercise.
11. Running head: DATA ANALYSIS AND APPLICATION 11
Image #1
An effect size can be calculated for the “A” factor, “B” factor, and the interaction of “A” and
“B” using the formula ƞ2
A = SSA / SStotal, ƞ2
B = SSB / SStotal, and ƞ2
A*B = SSA*B = SSA*B / SStotal
(Howell, 2011; Warner, 2013). Completing the appropriate equation with the output data from
SPSS results in an effect size for factor “A” of .008 which would be considered a small effect
size. In addition, the calculated effect size for factor “B” is .003 which would also be considered
a small effect size. Finally the “AXB” interaction effect size would be 0 as there was no
significant interaction.
12. Running head: DATA ANALYSIS AND APPLICATION 12
Conclusion
The results of the factorial ANOVA clearly demonstrate a relationship between the
independent variable of caffeine and its affect on one’s heart rate. In addition the test
demonstrated a relationship between exercise and one’s heart rate. However, the test found no
interaction between caffeine intake and exercise as those variables relate to one’s heart rate. The
test does have its limitations including the rather small sample size of only 16 respondents. A
larger sample size might reveal additional insights.
13. Running head: DATA ANALYSIS AND APPLICATION 13
References
George, D., & Mallery, P. (2012). IMB SPSS statistics 19 step by step: A simple guide and
reference (12th. ed.). Boston, MA: Pearson Publishing.
Howell, C. (2011). Fundamental statistics for the behavioral sciences (7th ed. ed.). Belmont,
CA: Wadsworth, Cengage Learning.
Warner, R. (2013). Applied statistics: From bivariate through multivariate techniques (2nd. ed.).
Thousand Oaks, CA.: Sage Publications, Inc.