How many times can you write statistics in a minute? By: Madeline Stenken and Tara Levine.

Slides:



Advertisements
Similar presentations
Very simple to create with each dot representing a data value. Best for non continuous data but can be made for and quantitative data 2004 US Womens Soccer.
Advertisements

Copyright © 2011 Pearson Education, Inc. Putting Statistics to Work.
Statistical Reasoning for everyday life
Describing Quantitative Variables
DESCRIBING DISTRIBUTION NUMERICALLY
CHAPTER 1 Exploring Data
Steve and Torsten We Do Math in a Minute. Introduction In our experiment we had two version of a basic multiplication table. One was in order starting.
Our project dealt with seeing how many quarters a student could toss into a jar within one minute. After one minute, the student was no longer able to.
Chapter 5: Understanding and Comparing Distributions
By the end of this lesson you will be able to explain/calculate the following: 1. Histogram 2. Frequency Polygons.
Learning Goal: To be able to describe the general shape of a distribution in terms of its number of modes, skewness, and variation. 4.2 Shapes of Distributions.
Jan Shapes of distributions… “Statistics” for one quantitative variable… Mean and median Percentiles Standard deviations Transforming data… Rescale:
Understanding and Comparing Distributions
The Five-Number Summary And Boxplots. Chapter 3 – Section 5 ●Learning objectives  Compute the five-number summary  Draw and interpret boxplots 1 2.
Numerical Measures of Central Tendency. Central Tendency Measures of central tendency are used to display the idea of centralness for a data set. Most.
Unit 3 Sections 3-2 – Day : Properties and Uses of Central Tendency The Mean  One computes the mean by using all the values of the data.  The.
Describing distributions with numbers
Have out your calculator and your notes! The four C’s: Clear, Concise, Complete, Context.
Chris Torgalski Jin Lee Block One. Introduction Our experiment was to see how many basketball shots a person could make in one minute. To keep the experiment.
What is Statistics? Statistics is the science of collecting, analyzing, and drawing conclusions from data –Descriptive Statistics Organizing and summarizing.
Analyzing Graphs Section 2.3. Important Characteristics of Data Center: a representative or average value that indicates where the middle of the data.
Categorical vs. Quantitative…
Warm-up The number of deaths among persons aged 15 to 24 years in the United States in 1997 due to the seven leading causes of death for this age group.
Bellwork 1. If a distribution is skewed to the right, which of the following is true? a) the mean must be less than the.
1. 2 * Introduction & Thoughts Behind The Experiment - We wanted to chose an experiment that was easy to complete, organized, and some what entertaining.
Quarter Spin: How long does your spin last? Bridget Sanelli Kim Lor Mrs. McNelis Block 3 AP Statistics.
Revision Analysing data. Measures of central tendency such as the mean and the median can be used to determine the location of the distribution of data.
We would like for you to take a seat and answer a few questions before your minute to win it challenge! We asked how frequently you traveled and what.
Chapter 2 Describing Distributions with Numbers. Numerical Summaries u Center of the data –mean –median u Variation –range –quartiles (interquartile range)
1.3 Describing Quantitative Data with Numbers Pages Objectives SWBAT: 1)Calculate measures of center (mean, median). 2)Calculate and interpret measures.
ANALYZING THE SHAPE OF DATA (CC-37) PURPOSE: TO CHOOSE APPROPRIATE STATISTICS BASED ON THE SHAPE OF THE DATA DISTRIBUTION. ADRIAN, KARLA, ALLEN, DENISSE.
LIS 570 Summarising and presenting data - Univariate analysis.
Numerical descriptions of distributions
Chapter 3: Displaying and Summarizing Quantitative Data Part 1 Pg
NOTES #9 CREATING DOT PLOTS & READING FREQUENCY TABLES.
Describing & Comparing Data Unit 7 - Statistics. Describing Data  Shape Symmetric or Skewed or Bimodal  Center Mean (average) or Median  Spread Range.
Chapter 5 Describing Distributions Numerically Describing a Quantitative Variable using Percentiles Percentile –A given percent of the observations are.
Describing Data Week 1 The W’s (Where do the Numbers come from?) Who: Who was measured? By Whom: Who did the measuring What: What was measured? Where:
1. 2 * Introduction & Thoughts Behind The Experiment - We wanted to chose an experiment that was easy to complete, organized, and some what entertaining.
Warm Up! Write down objective and homework in agenda Lay out homework (Box Plot & Outliers wkst) Homework (comparing data sets) Get a Calculator!!
Introduction To compare data sets, use the same types of statistics that you use to represent or describe data sets. These statistics include measures.
Chapter 5 : Describing Distributions Numerically I
Describing Distributions Numerically
1st Semester Final Review Day 1: Exploratory Data Analysis
Bell Ringer Create a stem-and-leaf display using the Super Bowl data from yesterday’s example
Jeopardy Final Jeopardy Chapter 1 Chapter 2 Chapter 3 Chapter 4
CHAPTER 1 Exploring Data
Box and Whisker Plots Algebra 2.
Chapter 5: Describing Distributions Numerically
Warm-up 8/25/14 Compare Data A to Data B using the five number summary, measure of center and measure of spread. A) 18, 33, 18, 87, 12, 23, 93, 34, 71,
Probability & Statistics Describing Quantitative Data
Lesson 1: Summarizing and Interpreting Data
Unit 6A Characterizing Data Ms. Young.
CHAPTER 1 Exploring Data
The Range Chapter Data Analysis Learning Goal: To be able to describe the general shape of a distribution in terms of its.
CHAPTER 1 Exploring Data
CHAPTER 1 Exploring Data
Describing Distributions Numerically
Vocabulary for Feb. 20-Mar
CHAPTER 1 Exploring Data
CHAPTER 1 Exploring Data
CHAPTER 1 Exploring Data
CHAPTER 1 Exploring Data
CHAPTER 1 Exploring Data
Lindsay Liebert & Julia Calabrese March 26, 2018 Block 2
Advanced Algebra Unit 1 Vocabulary
CHAPTER 1 Exploring Data
CHAPTER 1 Exploring Data
Lesson Plan Day 1 Lesson Plan Day 2 Lesson Plan Day 3
Analyze Data: IQR and Outliers
Presentation transcript:

How many times can you write statistics in a minute? By: Madeline Stenken and Tara Levine

Introduction Gave choice of pencil or pen Asked if they were right or left-handed Had to write statistics completely and correctly in one minute Wanted to see how writing utensils and dominant hands affect how many times you can write statistics in a minute

# Of Times Written In One Minute The mean of the number of times students wrote “statistics” in a minute was while the median was 17, so the averages were close. The shape is clustered and slightly left skewed. Because it is left skewed and not symmetric, it does not fit the normal model even though it is unimodal. Also, there is an outlier at 10 which we calculated because the lower fence of the data is 13, and ten is below that. 20, however, is not an outlier because the upper fence is at 21, and 20 is not above that fence. The range is 10, with the max at 20 and the min at 10.

Partner Comparison Both of our means were close to 16 and our medians were both 17, showing that we had the same centers. However, Tara’s data had a wider range, with being 10 with an outlier, and Madeline’s was only 4. Tara’s and Madeline’s data were both left skewed, but Madeline’s was unimodal and clustered while Tara’s was uniform in the middle with a wide peak for being unimodal and was more spread out.

Comparing Dominant Hand With # of Times Written The data for left-handed people was uniform and spread out, with the range being 8 (10-18), which was wider than right-handed people’s range which was 7 (13-20). Additionally, left handed people’s center was around 14.8 while right handed was around Right-handed data was more symmetric. We conclude that the statistics for this category are difficult to analyze because there were not many left-handed people in the class, so the data was only drawn from very few students.

Comparison of Writing Utensil in Relation to # of Times Written The graph for people using a pen was unimodal and roughly symmetric and the graph for people using a pencil was also unimodal, but it seems to have a very slight left skew. The pen graph has a center of about 16.67, while the center for the pencil graph is about 16. The range for the pen graph is 7 (13-20) which is lower than the range for the pencil graph which is 10 (10-20). The only reason that the range is so large for pencil though is because of the outlier at 10. Both graphs have the same maximum of 20.

Marginal Distributions Dominant Hand: – Left: 5/31=16.13% – Right: 26/31=83.87% Writing Utensil: – Pen: 16/31=51.61% – Pencil: 15/31=48.39%

Conditional Distributions Dominant Hand: – Left: Pen: 3/5=60% Pencil: 2/5=40% – Right: Pen: 13/26=50% Pencil: 13/26=50% Writing Utensil: – Pen: Left: 3/16=18.75% Right: 13/16=81.25% – Pencil: Left: 2/15=13.33% Right: 13/15=86.67%

Marginal and Conditional Distributions It is clear that there are mostly right-handed people in the class, but there was almost an equal amount of people who chose to use pen or pencil. For left handed people it was pretty even on their writing utensil choice and for right handed people it actually was even. And since none of the calculations matched up, the variables are dependent from one another.

Bias, Errors, and Variability One discrepancy in our data was that there were so few left-handed people, so the data from them was not necessarily an effective sample for this category. Also, it may not make much of a difference but if we counted the times people wrote half of the word, the results may have been different. Also, if we would have taken legibility into account, the results may have differed.

Conclusion From our data we can conclude that whether you are left handed or right handed and whether you use pen or pencil can affect the amount of times you can write statistics in a minute. We also concluded that there are a lot more kids in our class that are right handed than left handed, but there is an even distribution of kids who like to use pen or pencil.