Created by Mr. Barnard People have asked themselves and others this question countless times. We spend an enormous amount of time, energy, and money.

Slides:



Advertisements
Similar presentations
Forecasting Using the Simple Linear Regression Model and Correlation
Advertisements

MAT 105 SPRING 2009 Quadratic Equations
Chapter 10 Regression. Defining Regression Simple linear regression features one independent variable and one dependent variable, as in correlation the.
Lesson 5.7- Statistics: Scatter Plots and Lines of Fit, pg. 298 Objectives: To interpret points on a scatter plot. To write equations for lines of fit.
10.1 Scatter Plots and Trend Lines
© 2000 Prentice-Hall, Inc. Chap Forecasting Using the Simple Linear Regression Model and Correlation.
Linear, Exponential, and Quadratic Functions. Write an equation for the following sequences.
Correlational Research Strategy. Recall 5 basic Research Strategies Experimental Nonexperimental Quasi-experimental Correlational Descriptive.
§ 9.6 Exponential Growth and Decay; Modeling Data.
Linear Regression.
Copyright © Cengage Learning. All rights reserved.
Quadratic t n = an 2 + bn + c Ex: Find the generating function for the following: A) 2, 7, 16, 29, 46, 67, /5 students can do a Quad Reg on the.
SHOWTIME! STATISTICAL TOOLS IN EVALUATION CORRELATION TECHNIQUE SIMPLE PREDICTION TESTS OF DIFFERENCE.
Copyright © Cengage Learning. All rights reserved. 3 Exponential and Logarithmic Functions.
Correlation with a Non - Linear Emphasis Day 2.  Correlation measures the strength of the linear association between 2 quantitative variables.  Before.
Chapter 14 – Correlation and Simple Regression Math 22 Introductory Statistics.
Chapter 4 Correlation and Regression Understanding Basic Statistics Fifth Edition By Brase and Brase Prepared by Jon Booze.
Correlation Coefficient Negative No Positive Correlation Correlation Correlation.
C. A. Warm Up 1/28/15 SoccerBasketballTotal Boys1812 Girls1614 Total Students were asked which sport they would play if they had to choose. 1)Fill in the.
Lecture 22 Dustin Lueker.  The sample mean of the difference scores is an estimator for the difference between the population means  We can now use.
1. Graph 4x – 5y = -20 What is the x-intercept? What is the y-intercept? 2. Graph y = -3x Graph x = -4.
Fitting Exponentials and Polynomials to Data Lesson 11.7.
Physics Toolkit Graphing Data. Physics Toolkit  Objectives  Graph the relationship between independent and dependent variables  Interpret graphs 
10/18/2015 V. J. Motto 1 Chapter 1: Models V. J. Motto MAT 112 Short Course in Calculus Data Sets and the “STAT” Function.
Statistical Reasoning for everyday life Intro to Probability and Statistics Mr. Spering – Room 113.
Exam Review Day 6 Chapters 2 and 3 Statistics of One Variable and Statistics of Two Variable.
April 1 st, Bellringer-April 1 st, 2015 Video Link Worksheet Link
* 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.
Financial Statistics Unit 2: Modeling a Business Chapter 2.2: Linear Regression.
WARM – UP #5 1. Graph 4x – 5y = -20 What is the x-intercept? What is the y-intercept? 2. Graph y = -3x Graph x = -4.
Holt Algebra Modeling Real-World Data Warm Up quadratic: y ≈ 2.13x 2 – 2x x35813 y Use a calculator to perform quadratic and.
Unit 6 Review. Median-Median Line Find the median-median line for the following data: (1, 4) (6, 8) (7, 11) (7.5, 10) (8, 9) (9, 12) (9.5, 17) (10, 14)
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.
Correlation – Recap Correlation provides an estimate of how well change in ‘ x ’ causes change in ‘ y ’. The relationship has a magnitude (the r value)
What is Calculus ? Three Basic Concepts Lesson 2.1.
Chapter 10: Determining How Costs Behave 1 Horngren 13e.
1 Beginning & Intermediate Algebra – Math 103 Math, Statistics & Physics.
Aim: Graph of Best Fit Course: Alg. 2 & Trig. Aim: How do we model real-world data with polynomial and other functions? Do Now: 6 pt. Regents Question.
Residual Plots Unit #8 - Statistics.
Exponential Functions. When do we use them? Exponential functions are best used for population, interest, growth/decay and other changes that involve.
Scatterplots and Linear Regressions Unit 8. Warm – up!! As you walk in, please pick up your calculator and begin working on your warm – up! 1. Look at.
Math 8C Unit 3 – Statistics. Unit 3 – Day 1 (U3D1) Standards Addressed: – Create a scatter plot and label any trends and clustering. – Explain why a linear.
1.5 Exponential Functions Math 150 Introduction to Calculus.
12.8 Exponential and Logarithmic Equations and Problem Solving Math, Statistics & Physics 1.
Regression and Median Fit Lines
Graphing Data A variable is any factor that might affect the behavior of an experimental setup. Identifying Variables Section 1.3 The independent variable.
P REVIEW TO 6.7: G RAPHS OF P OLYNOMIAL. Identify the leading coefficient, degree, and end behavior. Example 1: Determining End Behavior of Polynomial.
REGRESSION MODELS OF BEST FIT Assess the fit of a function model for bivariate (2 variables) data by plotting and analyzing residuals.
Scatter Plots & Lines of Best Fit To graph and interpret pts on a scatter plot To draw & write equations of best fit lines.
UNIT 8 Regression and Correlation. Correlation Correlation describes the relationship between two variables. EX: How much you study verse how well you.
15.7 Curve Fitting. With statistical applications, exact relationships may not exist  Often an average relationship is used Regression Analysis: a collection.
Week 2 Normal Distributions, Scatter Plots, Regression and Random.
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.
SCATTERPLOTS, ASSOCIATION AND RELATIONSHIPS
Warm-up 1) Write the equation of the line passing through (4,5)(3,2) in: Slope Intercept Form:   Standard Form: Graph: Find intercepts.
Linear Equations Y X y = x + 2 X Y Y = 0 Y =1 Y = 2 Y = 3 Y = (0) + 2 Y = 2 1 Y = (1) + 2 Y = 3 2 Y = (2) + 2 Y = 4 X.
Correlation and Regression
Regression.
Mathematical relationships, science, and vocabulary
MATH 1314 Lesson 3.
Residuals and Residual Plots
Section 1.4 Curve Fitting with Linear Models
Regression.
Learning Targets Students will be able to: Compare linear, quadratic, and exponential models and given a set of data, decide which type of function models.
Quadratic Graphs.
Integrated Math 3 – Mod 3 Test Review
Multiple Regression Copyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.
15.7 Curve Fitting.
Presentation transcript:

Created by Mr. Barnard

People have asked themselves and others this question countless times. We spend an enormous amount of time, energy, and money trying to predict: ‘what will happen if…?’

We predict the weather...

We predict the success rate of a new drug:

We predict the success of new herbicides and of new hybrids:

We predict the outcome of an election:

We predict the winner of a sporting event:

How do we predict? LogicLogic Tools in the realms of math and scienceTools in the realms of math and science Past experiences and past eventsPast experiences and past events Common senseCommon sense

But what do we need to be able to predict? DATA!!!

To be able to make an accurate prediction, we need information collected called data. The more data one has, the more accurate of a prediction that can be made.

Calculus is the math of change. We make predictions based on the changes in data over time. Therefore, we are going to predict the...

FUTURE POPULATIONS OF COMMUNITIES IN NEBRASKA

Data helps us create models which can be used to predict what will happen next. We try to find mathematical models (equations) that best fit the data.

How do we know which mathematical model to use? For one, it depends on the shape of the data when it is plotted on a graph.

Mathematical Models (Equations) Linear (x...) Quadratic (x 2...)

Mathematical Models (Equations) Cubic (x 3...) Quartic (x 4...)

Mathematical Models (Equations) Logarithmic (ln(x)...) Exponential (n x )

Mathematical Models (Equations) Power (x n ) (Graph varies depending on the value of n)

Secondly, there are various statistical analysis items that can be done with the data to determine which model is the best predictor.

Correlation Coefficient (‘r’) The closer ‘r’ is to -1 (negative correlation) or to 1 (positive correlation), the stronger the relationship between time and population (thus the better the predictor).

Coefficient of Determination (‘r 2 ’) The closer ‘r 2 ’ is to 1, the stronger the relationship between time and population (thus the better the predictor).

Our task is to select two Nebraska incorporated communities, predict their future populations based on data using technology, and present our findings.

You will not be using your crystal ball......rather you will use the information presented and some upcoming guidelines for you to follow!!!

Enjoy looking into the future!!!