The relationship between cost and home sales

Slides:

Advertisements

Similar presentations

2.4 Writing the Equation of a Line

Advertisements

Linear Equations Review. Find the slope and y intercept: y + x = -1.

Scatter Plots Find the line of best fit In addition to level 3.0 and beyond what was taught in class, the student may:  Make connection with.

5-7: Scatter Plots & Lines of Best Fit. What is a scatter plot?  A graph in which two sets of data are plotted as ordered pairs  When looking at the.

OBJECTIVES 2-2 LINEAR REGRESSION

2.4 Using Linear Models. The Trick: Converting Word Problems into Equations Warm Up: –How many ways can a $50 bill be changed into $5 and $20 bills. Work.

Linear Regression.

Overview 4.2 Introduction to Correlation 4.3 Introduction to Regression.

Objective: I can write linear equations that model real world data.

2-4 Writing Linear Equations Objective: To write an equation of a line in slope intercept form given the slope and one or two points, and to write an equation.

Bivariate data are used to explore the relationship between 2 variables. Bivariate Data involves 2 variables. Scatter plots are used to graph bivariate.

Section 2.6 – Draw Scatter Plots and Best Fitting Lines A scatterplot is a graph of a set of data pairs (x, y). If y tends to increase as x increases,

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

Warm-Up Write the equation of each line. A B (1,2) and (-3, 7)

Correlation The apparent relation between two variables.

Scatter Plots, Correlation and Linear Regression.

7.1 Draw Scatter Plots and Best Fitting Lines Pg. 255 Notetaking Guide Pg. 255 Notetaking Guide.

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.

Lines of Best Fit When data show a correlation, you can estimate and draw a line of best fit that approximates a trend for a set of data and use it to.

Chapter 4 – Graphing Linear Equations and Functions Algebra I A - Meeting 24 Vertical Change Slope – is the ratio of the vertical change to the horizontal.

Scatter Plots & Lines of Best Fit To graph and interpret pts on a scatter plot To draw & write equations of best fit lines.

4-5 Scatter Plots and Lines of Best Fit

2.1a Polynomial Functions Linear Functions Linear Correlation/Modeling

Linear Model Application

Lines of Best Fit When data show a correlation, you can estimate and draw a line of best fit that approximates a trend for a set of data and use it to.

Lines of Best Fit #1 When data show a correlation, you can estimate and draw ____________________ that approximates a trend for a set of data and use it.

F-IF.C.7a: Graphing a Line using Slope-Intercept, Part 2

SLOPES Essential Question: How do we relate the concept of slope as rate of change and how do we determine slopes from graphs, tables and algebraic representations?

The Least-Squares Regression Line

CHAPTER 10 Correlation and Regression (Objectives)

Notes Over 4.7 Solving an Equation Graphically

Basic Algebra 2 Teacher – Mrs. Volynskaya

Investigating Relationships

2.5 Correlation and Best-Fitting Lines

2.6 Draw Scatter Plots and Best-Fitting Lines

Scatter Plots and Lines of best fit

2.4 Writing the Equation of a Line

Lesson 5.3 How do you write linear equations in point-slope form?

Algebra 1 Section 6.6.

The Weather Turbulence

2.4 Writing the Equation of a Line

8/29/12 Writing the Equation of a Line

A B 1 (5,2), (8, 8) (3,4), (2, 1) 2 (-2,1), (1, -11) (-2,3), (-3, 2) 3

Correlation Coefficient

Line of best fit.

Scatterplots 40 points.

Find the line of best fit.

Section 1.4 Curve Fitting with Linear Models

Objective Find slope by using the slope formula..

4-5 Scatter Plots and Lines of Best Fit

Forms of a linear equation

5.6 – point slope formula Textbook pg. 315 Objective:

2-4: Writing Linear Equations Using Slope Intercept Form

y = mx + b Linear Regression line of best fit REMEMBER:

2.6 Draw Scatter plots & Best Fitting Lines

A. Draw a trend line. It will be easier to write an equation

Objective The student will be able to:

Linear equation construction and prediction

Graphing with X- and Y-Intercepts

7.1 Draw Scatter Plots and Best Fitting Lines

2.5 Correlation and Best-Fitting Lines

13.4 – Slope and Rate of Change

2.4 Writing the Equation of a Line

Draw Scatter Plots and Best-Fitting Lines

Can you use scatter plots and prediction equations?

Graphing using Slope-Intercept Form

Presentation transcript:

The relationship between cost and home sales Debbie Lemon and Dan Mollette Algebra I

Our Predictions Since many of the homes in our neighborhood are smaller and older homes, we predicted that homes would not sell for more than $400,000.00. We also reviewed the MLS listings online.

WE EXPLORED THE RELATIONSHIP BETWEEN THE COST OF HOMES IN OUR AREA AND THE NUMBER OF HOMES SOLD. Data was found online at: www.zweigmedia.com

Confirming the Linear Data We plotted the points using x as the price of houses sold and y as the number of houses sold. The scatterplot of the data indicated a strong negative correlation. This relationship was negative because as the cost of the houses increased, the number of houses sold decreased. The line of best fit was drawn and using two points on the line, we calculated the slope.

Writing the Equation Once we found the slope, we easily found the y intercept by substituting into the slope- intercept formula and looking on the graph. y = -.88x + 267.3

Calculating the equation We used the TI-83 to enter the data, graph the relationship and determine the line of best fit. y = -.79x + 249.9

Conclusions 1. Our data was linear because the number of homes sold decreased at a similar rate of change compared to the number of homes sold. 2. The slope is negative because as the y values (cost of homes) increase, the x values (number of homes sold) decreases. 3. The equation we created using the calculations of the points we chose on the line of best fit (y = -.88x + 267.3) and the calculator equation (y = -.79x + 249.9) were closely related since they looked almost the same when graphed.

Conclusions According to our graphs and our equations, we can predict that there will be no homes sold in this neighborhood when the cost of the home reaches approximately $303,000.00. Our prediction was much higher since many of the homes described on the website were listed at higher prices.