Calories vs. Fat In French Fries Emily Conger Megan Dautel Lacy Allred Jennifer Knight Bernadette Martinez.

Slides:



Advertisements
Similar presentations
From the Carnegie Foundation math.mtsac.edu/statway/lesson_3.3.1_version1.5A.
Advertisements

AP Statistics Mrs Johnson
Regression, Residuals, and Coefficient of Determination Section 3.2.
Scatter Plots and Line of Best Fit. DETERMINING THE CORRELATION OF X AND Y In this scatter plot, x and y have a positive correlation, which means that.
Linear Regression.
Today  Grades-1 Week Left   Reminder: Snack Attack-Friday Morning  Questions? Reminders….  Only “Teaching”=Nutrients  Heavy on Content though! 
Box Plots. Box Plot A box plot shows a box over the interval containing the middle half of the data. The whiskers extend to the maximum and minimum value.
Relationships between Variables. Two variables are related if they move together in some way Relationship between two variables can be strong, weak or.
Least-Squares Regression Section 3.3. Why Create a Model? There are two reasons to create a mathematical model for a set of bivariate data. To predict.
A student wonders if tall women tend to date taller men than do short women. She measures herself, her dormitory roommate, and the women in the adjoining.
2.8Exploring Data: Quadratic Models Students will classify scatter plots. Students will use scatter plots and a graphing utility to find quadratic models.
 Restaurants have been around in some form for most of human civilization. › Although McDonald's was the first restaurant to use the assembly-line system,
Fast Food Fun Can you effectively use technology to find information and create an informative presentation? Lesson created by Karen Work Richardson Web.
Spreadsheet Project.
Vocabulary regression correlation line of best fit
Chicken Parmesan Calories Total Fat (g) Sat. Fat (g) Sodium (mg) Carbs (g) Protein (g) Fiber (g) Olive Garden Homemade
Jessica Walker Ashley Gastil Jeremy Lundberg Vaifed John Fabella.
Regression Regression relationship = trend + scatter
12.1 WS Solutions. (b) The y-intercept says that if there no time spent at the table, we would predict the average number of calories consumed to be
By: Clarissa Martin Vika Pasechnik Rachel Hernandez Emily Trost Alexandra Campbell.
Line of Best Fit.
Chapter 8 Linear Regression *The Linear Model *Residuals *Best Fit Line *Correlation and the Line *Predicated Values *Regression.
Analyzing Residuals Grade 9 Lesson 17. Learning Intentions ›We are learning to analyze residuals.
Get out the Notes from Monday Feb. 4 th, Example 2: Consider the table below displaying the percentage of recorded music sales coming from music.
WARM-UP Do the work on the slip of paper (handout)
Transformations.  Although linear regression might produce a ‘good’ fit (high r value) to a set of data, the data set may still be non-linear. To remove.
Creating a Residual Plot and Investigating the Correlation Coefficient.
5.7 Predicting with Linear Models Objective : Deciding when to use a linear model Objective : Use a linear model to make a real life prediction. 1 2 A.
Objective Find the line of regression. Use the Line of Regression to Make Predictions.
Review #2.
2.5 Using Linear Models A scatter plot is a graph that relates two sets of data by plotting the data as ordered pairs. You can use a scatter plot to determine.
Scientific Method. Experiment A process used to gather observations and test hypothesis.
Chapter 8 Linear Regression HOW CAN A MODEL BE CREATED WHICH REPRESENTS THE LINEAR RELATIONSHIP BETWEEN TWO QUANTITATIVE VARIABLES?
AP Statistics HW: p. 165 #42, 44, 45 Obj: to understand the meaning of r 2 and to use residual plots Do Now: On your calculator select: 2 ND ; 0; DIAGNOSTIC.
A P STATISTICS LESSON 3 – 3 (DAY 3) A P STATISTICS LESSON 3 – 3 (DAY 3) RISIDUALS.
Unit 2 Special centers; Trimmed & Weighted Mean. Let’s look at some data How Fast Food Compare Company Fast FoodTotalFatCholesterolSodium Calories(g)(mg)
Simple Linear Regression The Coefficients of Correlation and Determination Two Quantitative Variables x variable – independent variable or explanatory.
Lecture PowerPoint Slides Basic Practice of Statistics 7 th Edition.
Section 6 – 6 Scatter Plots & Equations of Lines Objective: To write an equation for a trend line and use it to make predictions.
The following data represents the amount of Profit (in thousands of $) made by a trucking company dependent on gas prices. Gas $
Scatter Plots & Lines of Best Fit To graph and interpret pts on a scatter plot To draw & write equations of best fit lines.
Best Fast Food French Fries?. Fry facts
Part II Exploring Relationships Between Variables.
Going Crackers! Do crackers with more fat content have greater energy content? Can knowing the percentage total fat content of a cracker help us to predict.
Bell Ringer CountryMarijuana (%) Other Drugs (%) Czech Rep.224 Denmark173 England4021 Finland51 Ireland3716 Italy198 Ireland2314 Norway63 Portugal73 Scotland5331.
Chapter 8 Part I Answers The explanatory variable (x) is initial drop, measured in feet, and the response variable (y) is duration, measured in seconds.
Trail Mix Investigation
Warm up… Page 297 practice quiz at the bottom of the page.
Lesson 4.5 Topic/ Objective: To use residuals to determine how well lines of fit model data. To use linear regression to find lines of best fit. To distinguish.
Chapter 11 Simple Linear Regression and Correlation.
Finding the Best Fit Line
Chapter 5 Lesson 5.3 Summarizing Bivariate Data
The following data represents the amount of Profit (in thousands of $) made by a trucking company dependent on gas prices. Gas $
Objectives Fit scatter plot data using linear models with and without technology. Use linear models to make predictions.
Describe the association’s Form, Direction, and Strength
Chapter 8 – Linear Regression
1) A residual: a) is the amount of variation explained by the LSRL of y on x b) is how much an observed y-value differs from a predicted y-value c) predicts.
Finding the Best Fit Line
Scatter Plots and Regression Lines Without a Calculator
Scientific Method.
Energy Content of Foods
Scatter Diagrams Objectives: D Grade Draw a line of best fit
Examining Relationships
25 Algebra 1B – Notebook Entry _____ Name:__________________________
Residuals and Residual Plots
Bell Ringer 1. Write the equation of the regression line for the data. Explain the meaning of the y-intercept and the slope of the line. 2. A new burger.
Scatter Plot: A graph that shows the relationship between 2 data sets.
Examining Relationships
Which has more fat?.
Lesson 2.2 Linear Regression.
Presentation transcript:

Calories vs. Fat In French Fries Emily Conger Megan Dautel Lacy Allred Jennifer Knight Bernadette Martinez

Purpose of the Study To find the relation between grams of fat and calories in a large order of french fries at 11 fast food restaurants. Hypothesis- The more grams of fat in a large order of french fries will increase the amount of calories.

Best Fit Line FriesFat (g)Calories DQ21500 Chick-fil-a23430 Artic Circle30564 Sonic18450 McDonald's25500 Wendy's25540 Burger King27540 What-a- Burger36640 Carl's Jr Jack in the Box29621 In-n-Out Burger18400 Least-Square Regression Line Y=12.63x

Residual Plot Our Information R=.902 Table II in Textbook R=.602

Analysis Our scatter plot indicates that there is a strong linear relation that exists between fat content and calories in fast-food french fries. Our residual plot demonstrates that R=0.902 is close to 1, indicating linear relationship.

Predictions To test that our hypothesis, we looked at how many calories were in 30 grams of fat. – Y=12.63(30 grams of fat) = about 579 calories.

Conclusion As a group, we have decided that the more grams of fat within fast food french fries, the calories increase substantially. From the 11 fast food restaurants we observed, we came to the conclusion that In- and-Out Burger has the lowest calories and grams of fat.