BY MICHELLE MILLER GINA SALTZGIVER KEARSTEN CROSSLEY & LINH NGO Math 1040 Project.

Slides:

Advertisements

Similar presentations

Rubric Unit Plan Univariate Bivariate Examples Resources Curriculum Statistically Thinking A study of univariate and bivariate statistics.

Advertisements

CAFFEINE CONSUMPTION VS. HOURS OF SLEEP Amie Radtke, Julie Luckart, Drew Hanson, Sofiya Mykhalska, Melissa Young, Erin Brown.

Using R for Introductory Statistics CHAD BIRGER UNIVERSITY OF SIOUX FALLS.

AP Statistics Mrs Johnson

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

Which Cereals are the Healthiest? Michaela Todd, Halley White, Kalli Rasmussen, Ellie Hagen.

Section 2.6: Draw Scatter Plots & best-Fitting Lines(Linear Regresion)

Group 3: Joscie Barrow, Nicole Devey, Megan Hanson, Shealyn Kwan-Smith, and Gregory Morrison.

Data Handling GCSE coursework. Hypothesis Collection of Data Data Handling GCSE coursework Hypothesis.

Descriptive Statistics: Correlation u Describes the relationship between two or more variables. u Describes the strength of the relationship in terms of.

First Quantitative Variable: Ear Length  The unit of measurement for this variable is INCHES.  A few possible values for this first quantitative variable.

Bivariate Distributions Overview. I. Exploring Data Describing patterns and departures from patterns (20%-30%) Exploring analysis of data makes use of.

Vocabulary regression correlation line of best fit

Group participation McKie Delahunty- created slides 1,4,5,6,7,8,11,12 & compiled all for powerpoint Jenica Hansen- created slides 2,3 Semhar Moges- created.

Then click the box for Normal probability plot. In the box labeled Standardized Residual Plots, first click the checkbox for Histogram, Multiple Linear.

My Favorite Foods. Serving Size: 1 cup Servings Per Container: 16 Calories: 3,040 Fat: 80 grams Carbohydrates: 464 grams Fiber: 0 grams Sugar: 448 grams.

1)Construct a box and whisker plot for the data below that represents the goals in a soccer game. (USE APPROPRIATE SCALE) 7, 0, 2, 5, 4, 9, 5, 0 2)Calculate.

Will how tall you are tell us what size shoe you wear?

Chelsie Guild, Taylor Larsen, Mary Magee, David Smith, Curtis Wilcox TERM PROJECT- VISUAL PRESENTATION.

REGRESSION DIAGNOSTICS Fall 2013 Dec 12/13. WHY REGRESSION DIAGNOSTICS? The validity of a regression model is based on a set of assumptions. Violation.

Exam Review Day 6 Chapters 2 and 3 Statistics of One Variable and Statistics of Two Variable.

Unit 4: Describing Data After 8 long weeks, we have finally finished Unit 3: Linear & Exponential Functions. Now on to Unit 4 which will last 3 weeks.

Investigating Scatter Plots Scatter plots – show correlations (relationships) between two different pieces of data.  dependent variable (y’s or range)

April 1 st, Bellringer-April 1 st, 2015 Video Link Worksheet Link

Height and shoe size GROUP FIVE Shaun A. Nichols Shaleen Teresinski.

Math 145 September 11, Recap  Individuals – are the objects described by a set of data. Individuals may be people, but they may also be animals.

* SCATTER PLOT – GRAPH WITH MANY ORDERS PAIRS * LINE OF BEST FIT – LINE DRAWN THROUGH DATA THAT BEST REPRESENTS IT.

5.4 Line of Best Fit Given the following scatter plots, draw in your line of best fit and classify the type of relationship: Strong Positive Linear Strong.

Is Shoe Size Generally Proportional to Height? Katilyn Pangborn Hilary Christensen Jessica Howsden Jeffrey Ellsworth Tammy Kiholm.

Group 4 Members and Participants Amelia Corey, Angie Coates, Cynthia Bradwisch, Aaron Grow, and Daniel Champion.

Constructing a statistics project Chris Olley

Scatter Diagrams scatter plot scatter diagram A scatter plot is a graph that may be used to represent the relationship between two variables. Also referred.

Group 6 Contributions to Powerpoint made by: Jamie Page Aaron Little Tani Hatch Nicholas Mazzarese.

 Is there a correlation between an adult’s body mass index (BMI) and their systolic blood pressure…

By Mary Strong & Jessica Haws 1. Explanatory variable: Quantitative, age in years and months, example July and 22yrs. = 22.7 Response variable: Weight.

Unit 3: Averages and Variations Week 6 Ms. Sanchez.

Statistics Group 1 Elisabeth Brino Jamie Derbidge Slade Litten Kristen Kidder Jamie.

STATISTICS 1040 TERM PROJECT SPRING THE QUESTION Is a student’s Grade Point Average (GPA) correlated with their age?

Is their a correlation between GPA and number of hours worked? By: Excellent Student #1 Excellent Student #2 Excellent Student #3.

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,

Does Age Effect Grade Point Average? By Amanda Darrow and Dennis de Melo.

.  Relationship between two sets of data  The word Correlation is made of Co- (meaning "together"), and Relation  Correlation is Positive when the.

Group members: Miles, Haylee, David, and Nai. SYSTOLIC BLOOD PRESSURE:WEEKLY HOURS WORKED:

Statistics with TI-Nspire™ Technology Module E Lesson 1: Elementary concepts.

ContentDetail  Two variable statistics involves discovering if two variables are related or linked to each other in some way. e.g. - Does IQ determine.

Lecture PowerPoint Slides Basic Practice of Statistics 7 th Edition.

MATHS CORE AND OPTIONAL ASSESSMENT STANDARDS Found in the Subject Assessment Guidelines (SAG) Dated January 2008.

Creating Graphs By Your Fabulous Math Teacher, the one and only, MS. COLEMAN.

By: Makenzie Quinn, Jamie Rappleye, Joel Reed, Bradley Richins, Heather Roberts and Coty Rohan.

Method 3: Least squares regression. Another method for finding the equation of a straight line which is fitted to data is known as the method of least-squares.

Term Project Math 1040-SU13-Intro to Stats SLCC McGrade-Group 4.

MATH 1040 – FINAL PROJECT. Research Question  For SLCC (Utah) Summer 2013 students, is the number of concurrent class credits a student takes in high.

SYSTOLIC BLOOD PRESSURE VS. WEEKLY HOURS WORKED BY NURSES Group members: Miles, Haylee, David, and Nai.

Math 1040 Term Project Group 2 Stacey Perko Shanda Miller Stephanie Baker Ben Mansell Heidi Kaibetoney.

GOAL: I CAN USE TECHNOLOGY TO COMPUTE AND INTERPRET THE CORRELATION COEFFICIENT OF A LINEAR FIT. (S-ID.8) Data Analysis Correlation Coefficient.

Central Tendency  Key Learnings: Statistics is a branch of mathematics that involves collecting, organizing, interpreting, and making predictions from.

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,

1.6 Modeling Real-World Data with Linear Functions

Objectives Fit scatter plot data using linear models with and without technology. Use linear models to make predictions.

Chapter 12: Regression Diagnostics

Ticket in the Door Find the mean, median and mode of the data.

Describe the association’s Form, Direction, and Strength

1.6 Modeling Real-World Data with Linear Functions

The greatest blessing in life is

Day 12 AGENDA: Quiz 2.1 & minutes Test Ch 1 & 2 is Thursday.

Determine the type of correlation between the variables.

Unit 4: Describing Data After 10 long weeks, we have finally finished Unit 3: Linear & Exponential Functions. Now on to Unit 4 which will last 5 weeks.

Ch 4.1 & 4.2 Two dimensions concept

Arms vs. Feet Group 4 Members and Participants Amelia Corey, Angie Coates, Cynthia Bradwisch, Aaron Grow, and Daniel Champion.

Lesson 2.2 Linear Regression.

Presentation transcript:

BY MICHELLE MILLER GINA SALTZGIVER KEARSTEN CROSSLEY & LINH NGO Math 1040 Project

Research Question Are fat grams per serving related to calories per serving? The four food selections: Mashed Potatoes, Instant Rice, Chips, and Ice Cream Collecting the data

Splitting Work Michelle: Gathering individual data, organizing all data together, creating charts for the data Gina: Gathering individual data, presenting the finished powerpoint Kearsten: Gathering individual data, creating/ editing powerpoint Linh Ngo : Gathering individual data, presenting the finished powerpoint

Mashed Potatoes BrandFlavorTypeCaloriesFat (g) Stouffer’s Type Stouffer’s Type Meal Simple Type Bob Evans Type Country Crock Type Marie Callender’s Type Marie Callender’s Type Marie Callender’s Type Stouffer’s Type Stouffer’s Type Smart One Type TOTAL CALORIES -Mean: Standard Deviation: Five-Number Summary: 150, 190, 270, 330, 470 -Range: 320 -Mode: 320 -Outliers: None FAT GRAMS -Mean: Standard Deviation: Five-Number Summary: 60, 70, 90, 130, 170 -Range: 110 -Mode: 60, 80 -Outliers: None

Instant Rice BrandFlavorTypeCaloriesFat (g) Rice-A-RoniFried RiceType Rice-A-RoniSpanish RiceType Rice-A-RoniRice PilafType Rice-A-RoniNatural long grainType Rice-A-RoniOriginal long grainType Rice-A-RoniChickenType Rice-A-RoniBeefType Rice-A-RoniChicken FajitaType Rice-A-RoniChicken & BroccoliType Rice-A-RoniCreamy CheeseType TOTAL CALORIES -Mean: 206 -Standard Deviation: Five-Number Summary: 180, 180, 200, 230, 240 -Range: 60 -Mode: 180, 230 -Outliers: None FAT GRAMS -Mean: Standard Deviation: Five-Number Summary:.5, 1, 1, 1, 4.5 -Range: 4 -Mode: 1 -Outliers: 4.5

Chips BrandFlavorTypeCaloriesFat (g) Type Type Type Type Type Type Type Type Type Type TOTAL CALORIES -Mean: 142 -Standard Deviation: Five-Number Summary: 110, 140, 150, 150, 160 -Range: 50 -Mode: 150 -Outliers: None FAT GRAMS -Mean: 7.8 -Standard Deviation: Five-Number Summary: 1, 6, 9, 10, 10 -Range: 10 -Mode: 9 -Outliers: 0

Ice Cream BrandFlavorTypeCaloriesFat (g) Great ValueFat Free ChocolateType Great ValueCookies ‘n’ CreamType Great ValueRocky RoadType Great ValueVanilla BeanType BryersCoffee FudgeType BryersBlasts WhoppersType BryersSnickersType BryersOreoType BryersRocky RoadType BryersVanillaType TOTAL CALORIES -Mean: 176 -Standard Deviation: Five-Number Summary: 110, 160, 180, 190, 210 -Range: 100 -Mode: 180 -Outliers: 110 FAT GRAMS -Mean: 7.3 -Standard Deviation: Five-Number Summary: 0, 6, 8.5, 9, 10 -Range: 10 -Mode: 9 -Outliers: 0

Histograms

Scatter Plot

Positive Correlation The Linear Correlation Coefficient:.7883 Least-Squares Regression Line: y=.4863x Slightly Positive Correlation Coefficient

Conclusion Positive Correlation Coefficient Calories per serving somewhat effect how many fat grams there are per serving Importance our group learned