Purpose of the study To find out using statistical analysis if the amount of daily caffeine intake is related to weight of adult females.

Slides:



Advertisements
Similar presentations
Inference for Regression
Advertisements

CAFFEINE CONSUMPTION VS. HOURS OF SLEEP Amie Radtke, Julie Luckart, Drew Hanson, Sofiya Mykhalska, Melissa Young, Erin Brown.
Correlation and Regression
Alan Mangus Math April 15,  Purpose of the study (the research question): Can the age of adult male humans be used as a reliable predictor.
Topics: Significance Testing of Correlation Coefficients Inference about a population correlation coefficient: –Testing H 0 :  xy = 0 or some specific.
Chapter 13: Inference in Regression
Means Tests Hypothesis Testing Assumptions Testing (Normality)
Regression. Height Weight How much would an adult female weigh if she were 5 feet tall? She could weigh varying amounts – in other words, there is a distribution.
Regression. Height Weight Suppose you took many samples of the same size from this population & calculated the LSRL for each. Using the slope from each.
Statistical Analysis Topic – Math skills requirements.
Take out homework and a pencil to prepare for the homework quiz! Check the file folder for your class to pick up graded work.
Does time spent on Facebook affect your grades? Study results presented by: Mary Vietti : Power Point Creator Justin Price : Editor & Conclusion Jacob.
Will how tall you are tell us what size shoe you wear?
Height and shoe size GROUP FIVE Shaun A. Nichols Shaleen Teresinski.
Group 6 Contributions to Powerpoint made by: Jamie Page Aaron Little Tani Hatch Nicholas Mazzarese.
Heaven Kummer, Curtis Bryant, Bonni Patterson, Amy Evans.
 Is there a correlation between an adult’s body mass index (BMI) and their systolic blood pressure…
For adult men, is the amount of money spent per week on fast food related to body weight? By: Chad Vigil, Jeannette Watson, Jason Williams, Amanda Webster,
Scatterplots Association and Correlation Chapter 7.
Purpose Data Collection Results Conclusion Sources We are evaluating to see if there is a significant linear correlation between the shoe size and height.
Is there a correlation between the number of hours a student works and the number of credit hours they are enrolled in? Data Compiled and presented by:
Carli Young Annaliese Prusse Stephanie Harper Hannah Minkus Patrica Molyneux Is Birth weight Related To Gestational Age?
Term Project Math 1040-SU13-Intro to Stats SLCC McGrade-Group 4.
Group 13 Wingspan vs. Height
FOR TEEN AND YOUNG ADULT MALES (13 TO 29) IS AGE RELATED TO THE NUMBER OF HOURS SPENT PLAYING VIDEO/COMPUTER GAMES? By Amanda Webster, Jennifer Burgoyne,
Mountain Dew versus Mountain Lightning Michael J. Barattini
Chapter 1 – Statistics I 01 Learning Outcomes
The Central Limit Theorem
An Overview of Statistical Inference – Learning from Data
Regression.
Regression Inferential Methods
Background: The age old question to Mr
Warm Up Check your understanding P. 586 (You have 5 minutes to complete) I WILL be collecting these.
Measuring Evidence with p-values
Inference for Regression
Hypothesis Tests Small Sample Mean
Introductory Statistics
Regression.
Elementary Statistics
An Overview of Statistical Inference – Learning from Data
Quantitative Data Analysis P6 M4
Lisa Wilkinson Krystal Carvalho
NO ONE leaves the room during testing!!
Chapter 12 Regression.
Comparing Two Means: Paired Data
Regression.
Regression.
Regression.
Therefore, the Age variable is a categorical variable.
Chapter 1: Exploring Data
Introduction & 1.1: Analyzing categorical data
Regression Chapter 8.
Regression.
Does time spent on Facebook affect your grades?
The Use and Misuse of Statistics
WARM - UP The following represents snowfall amounts for 8 random winters in Detroit. 78” 130” 140” 120” 108” 120” 156” 101” 1. Fill in all four.
Chapter 1: Exploring Data
Which of the popular two drugs – Lipitor or Pravachol – helps lower.
Chapter 1: Exploring Data
Regression.
Comparing Two Means: Paired Data
Correlation and Regression Lecture 1 Sections: 10.1 – 10.2
Summarizing Bivariate Data
Chapter 1: Exploring Data
Describing Relationships
Spearman’s Rank Correlation Coefficient
Chapter 1: Exploring Data
Chapter 1: Exploring Data
Warm Up A 2011 paper asked a random sample of students from Stanford University to consider the following statement: “The meeting next Wednesday has been.
Chapter 3: Describing Relationships
Presentation transcript:

Is the amount of daily caffeine intake related to weight of adult females?

Purpose of the study To find out using statistical analysis if the amount of daily caffeine intake is related to weight of adult females.

Study design Using the quantitative variables weight (in pounds) and amount of caffeine consumed per day (in milligrams), we collected data from 40 randomly selected adult females in Salt Lake City. To ensure randomness, we made sure our data came from adult females with different occupation e.g. Office personnel, manufacturing personnel, students etc.

Data collection To ensure anonymity, the team has used an online survey that was created. To avoid non-response bias, the team has also used a form with some marked dummy data to help with anonymity where subjects were able to fill in their information. Note: Dummy data has been discarded during analysis

Data translation Since the subjects did not know the amount of caffeine they consume per day, we asked them to tell us how many caffeinated drinks they consume per day. That data has later been translated into milligrams of caffeine using: http://www.energyfiend.com/the-caffeine-database Example: 8oz Green Tea = 25mg caffeine

Weight statistics (lb) (lb)

Caffeine statistics

Scatterplot y = 162.27783 – 0.024031745 (Caffeine Intake)

Results Although weight in the sample size seems to be normally distributed, the caffeine intake is skewed to the right. It would be misleading to say that there is a direct correlation between the two variables by looking at this sample size. A greater sample size could give a better resolution and ultimately a more accurate picture of the correlation between the two variables, but as this statistical analysis is showing, there is no solid correlation between these two variables.

Conclusion Critical Value = 0.304 Linear Correlation Coefficient (r) = -0.1011 Since r = -0.1011 is less than the critical value of 0.304, we can conclude that there is no significant relationship between daily caffeine intake and the adult female weight, therefore, we can say that the daily caffeine intake is not related to the adult female weight.

Contributions Daneila Sarah Shannon Muris Graphs Data Collection Results/Conclusion PowerPoint Sarah Shannon Muris