Sample Presentation – Mr. Linden

Slides:

Advertisements

Similar presentations

Lesson 10: Linear Regression and Correlation

Advertisements

10-3 Inferences.

Baseball Statistics By Krishna Hajari Faraz Hyder William Walker.

Overview Motivation Data and Sources Methods Results Summary.

Regression Analysis Module 3. Regression Regression is the attempt to explain the variation in a dependent variable using the variation in independent.

Chapter 10 Regression. Defining Regression Simple linear regression features one independent variable and one dependent variable, as in correlation the.

1-1 Regression Models  Population Deterministic Regression Model Y i =  0 +  1 X i u Y i only depends on the value of X i and no other factor can affect.

BA 555 Practical Business Analysis

Correlation 2 Computations, and the best fitting line.

Chapter Topics Types of Regression Models

Correlation and Regression Analysis

Normal and Sampling Distributions A normal distribution is uniquely determined by its mean, , and variance,  2 The random variable Z = (X-  /  is.

1 Chapter 10 Correlation and Regression We deal with two variables, x and y. Main goal: Investigate how x and y are related, or correlated; how much they.

Correlation and Regression

Introduction to Linear Regression and Correlation Analysis

Inference for regression - Simple linear regression

Chapter 11 Simple Regression

STEM Fair Graphs & Statistical Analysis. Objectives: – Today I will be able to: Construct an appropriate graph for my STEM fair data Evaluate the statistical.

Probabilistic and Statistical Techniques 1 Lecture 24 Eng. Ismail Zakaria El Daour 2010.

STAT E100 Section Week 3 - Regression. Review  Descriptive Statistics versus Hypothesis Testing  Outliers  Sample vs. Population  Residual Plots.

Statistical Methods Statistical Methods Descriptive Inferential

Basic Concepts of Correlation. Definition A correlation exists between two variables when the values of one are somehow associated with the values of.

© Copyright McGraw-Hill Correlation and Regression CHAPTER 10.

Chapter 11 Correlation and Simple Linear Regression Statistics for Business (Econ) 1.

Political Science 30: Political Inquiry. Linear Regression II: Making Sense of Regression Results Interpreting SPSS regression output Coefficients for.

Chapter 9: Correlation and Regression Analysis. Correlation Correlation is a numerical way to measure the strength and direction of a linear association.

28. Multiple regression The Practice of Statistics in the Life Sciences Second Edition.

Reminder Remember that both mean and standard deviation are not resistant measures so you want to take that into account when calculating the correlation.

STATISTICS FOR SCIENCE RESEARCH (The Basics). Why Stats? Scientists analyze data collected in an experiment to look for patterns or relationships among.

1.What is Pearson’s coefficient of correlation? 2.What proportion of the variation in SAT scores is explained by variation in class sizes? 3.What is the.

+ Data Analysis Chemistry GT 9/18/14. + Drill The crown that King Hiero of Syracuse gave to Archimedes to analyze had a volume of 575 mL and a mass of.

AP Statistics Final Project Philadelphia Phillies Attendance Kevin Carter, Devon Dundore, Ryan Smith.

AP Statistics Chapter 24 Notes “Comparing Two Sample Means”

Statistics -Descriptive statistics 2013/09/30. Descriptive statistics Numerical measures of location, dispersion, shape, and association are also used.

Research Methods. Define the Milgram experiment An experiment in which Milgram wanted to determine whether participants would administer painful shocks.

Predicting Energy Consumption in Buildings using Multiple Linear Regression Introduction Linear regression is used to model energy consumption in buildings.

Inference about the slope parameter and correlation

Chapter 8  INFERENTIAL STATISTICS: CORRELATION

GS/PPAL Section N Research Methods and Information Systems

EXCEL: Multiple Regression

Regression and Correlation

Inference for Least Squares Lines

Review 1. Describing variables.

STATISTICS FOR SCIENCE RESEARCH

Regression Analysis Module 3.

Computations, and the best fitting line.

Political Science 30: Political Inquiry

Linear Regression and Correlation Analysis

Chapter 11: Simple Linear Regression

Elementary Statistics

Linear Trends and Correlations

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.

The Practice of Statistics in the Life Sciences Fourth Edition

Correlation and Regression

CHAPTER 29: Multiple Regression*

STEM Fair Graphs & Statistical Analysis

Inferences and Conclusions from Data

Keller: Stats for Mgmt & Econ, 7th Ed

AP Statistics, Section 3.3, Part 1

CORRELATION ANALYSIS.

STEM Fair Graphs.

Descriptive vs. Inferential

Simple Linear Regression

AP Statistics Section 3.2 Part 1 AP Statistics

AP Statistics October 1st, 2010 Mr. Calise

Correlation & Trend Lines

Warsaw Summer School 2017, OSU Study Abroad Program

Linear Regression and Correlation

Presentation transcript:

Sample Presentation – Mr. Linden Is there a relationship between a pitcher’s Earned Run Average (ERA) and the number of strikeouts? Sample Presentation – Mr. Linden

Population Population includes all major league pitchers from the 2018 baseball regular season. Pitchers needed to pitch at least 50 innings to be included in the list. The population includes both starting pitchers and relievers. My sample consisted of 30 pitchers.

Description of Data Independent Variable is a pitcher’s Earned Run Average (ERA). Earned run average is calculated by dividing the number of earned runs allowed by the number of innings pitched and multiplying by nine. This is a Numerical – Continuous statistic. Dependent Variable is a pitchers number of Strikeouts. This is a Numerical – Discrete statistic.

Sampling Technique I used a Simple Random Sample technique to collect my data. I went to MLB.com and selected Stats. I made sure to use the 2018 regular season. I selected pitchers only and selected pitching statistics. I sorted the statistics based on IP and organized the list from greatest to least. I used a random number generator to create a sequence of 30 numbers. I used those numbers to record each players name, team, ERA, and strikeouts.

Data Table Collected May, 16, 2019

Scatterplot

Pearson’s Correlation Coefficient R The correlation coefficient for the data: R = -0.376 This indicates that there is a moderately weak, relationship between a player’s ERA and number of strikeouts. The relationship is negative, which makes sense, for the study. The lower the pitcher’s ERA is the more strike outs he has thrown.

Line of Fit & Coefficient of Determination The Line of Best Fit: The y-intercept relates to the number of strikeouts Coefficient of Determination This indicates that only 14% of the variation in the data can be explained by the line of fit. This would not be a good model to predict future behaviors.

Descriptive Statistics Earned Run Average (ERA) Strikeouts Mean 3.917 90 St. Deviation 1.11197 41.40129 Confidence Interval 3.519 < mu < 4.315 75.185 < mu < 104.815

Conclusion In conclusion there does not seem to be a very good relationship between a pitcher’s ERA and the number of strike outs. On average a pitcher has an approximately 3.91 ERA with around 90 strikeouts.