Presentation is loading. Please wait.

Presentation is loading. Please wait.

CALCULATING EQUATION OF LEAST SQUARES REGRESSION LINE

Published byCassandra Johnston Modified over 5 years ago

Similar presentations

Presentation on theme: "CALCULATING EQUATION OF LEAST SQUARES REGRESSION LINE"— Presentation transcript:

1 CALCULATING EQUATION OF LEAST SQUARES REGRESSION LINE
12 Further mathematics CALCULATING EQUATION OF LEAST SQUARES REGRESSION LINE

2 CALCULATING EQUATION OF LEAST SQUARES REGRESSION LINE

3 CALCULATING EQUATION OF LEAST SQUARES REGRESSION LINE
SOLUTION SEE WHITEBOARD

4 CALCULATING EQUATION OF LEAST SQUARES REGRESSION LINE

5 CALCULATING EQUATION OF LEAST SQUARES REGRESSION LINE

6 CALCULATING EQUATION OF LEAST SQUARES REGRESSION LINE

7 CALCULATING EQUATION OF LEAST SQUARES REGRESSION LINE

8 CALCULATING EQUATION OF LEAST SQUARES REGRESSION LINE

9 CALCULATING EQUATION OF LEAST SQUARES REGRESSION LINE

10 Regression: Fitting lines to Data

11 Regression: Fitting lines to Data

12 Exercise 4B Even Numbered Questions
WORK TO BE COMPLETED Exercise 4B Even Numbered Questions

Download ppt "CALCULATING EQUATION OF LEAST SQUARES REGRESSION LINE"

Similar presentations

STROUD Worked examples and exercises are in the text PROGRAMME F6 POLYNOMIAL EQUATIONS.

STROUD Worked examples and exercises are in the text PROGRAMME F6 POLYNOMIAL EQUATIONS.
R Squared. r = -.944 r = -.79 y = -0.8141x + 9.1332y = -0.9402x + 43.721 if x = 15, y = ? y = -0.9402(15) + 43.721 y = 29.6195 if x = 6, y = ? y = -0.8141(6)

R Squared. r = r = -.79 y = x y = x if x = 15, y = ? y = (15) y = if x = 6, y = ? y = (6)
Exercise3.1 6. Exercise3.1 8 Exercise3.1 9 Exercise3.1 10.

Exercise Exercise3.1 8 Exercise3.1 9 Exercise
Exercise 1.1 4. Exercise 1.1 8 Exercise 1.2 6 Exercise 1.2 7.

Exercise Exercise Exercise Exercise
Exercise4.1 10. Exercise4.1 15 Exercise4.1 19 Exercise4.1 23.

Exercise Exercise Exercise Exercise
Exercise6.1 5. Exercise6.1 7 Exercise6.1 8 Exercise6.1 9.

Exercise Exercise6.1 7 Exercise6.1 8 Exercise6.1 9.
Business 205. Review Chi-Square Preview Regression.

Business 205. Review Chi-Square Preview Regression.
Section 8.3 – Systems of Linear Equations - Determinants Using Determinants to Solve Systems of Equations A determinant is a value that is obtained from.

Section 8.3 – Systems of Linear Equations - Determinants Using Determinants to Solve Systems of Equations A determinant is a value that is obtained from.
SOFTBALL EXAMPLE 3 Predict using an equation The table shows the number of participants in U.S. youth softball during the period 1997–2001. Predict the.

SOFTBALL EXAMPLE 3 Predict using an equation The table shows the number of participants in U.S. youth softball during the period 1997–2001. Predict the.
Thinking Mathematically Algebra: Graphs, Functions and Linear Systems 7.3 Systems of Linear Equations In Two Variables.

Thinking Mathematically Algebra: Graphs, Functions and Linear Systems 7.3 Systems of Linear Equations In Two Variables.
Solving Using the Quadratic Formula Each time we solve by completing the square, the procedure is the same. In mathematics, when a procedure is repeated.

Solving Using the Quadratic Formula Each time we solve by completing the square, the procedure is the same. In mathematics, when a procedure is repeated.
1.5 Cont. Warm-up (IN) Learning Objective: to create a scatter plot and use the calculator to find the line of best fit and make predictions. (same as.

1.5 Cont. Warm-up (IN) Learning Objective: to create a scatter plot and use the calculator to find the line of best fit and make predictions. (same as.
Section 5.2: Linear Regression: Fitting a Line to Bivariate Data.

Section 5.2: Linear Regression: Fitting a Line to Bivariate Data.
Using square roots to solve quadratic equations. 2x² = 8 22 x² = 4 The opposite of squaring a number is taking its square root √ 4= ± 2.

Using square roots to solve quadratic equations. 2x² = 8 22 x² = 4 The opposite of squaring a number is taking its square root √ 4= ± 2.
Simple Linear Regression. The term linear regression implies that  Y|x is linearly related to x by the population regression equation  Y|x =  +  x.

Simple Linear Regression. The term linear regression implies that  Y|x is linearly related to x by the population regression equation  Y|x =  +  x.
9.2A- Linear Regression Regression Line = Line of best fit The line for which the sum of the squares of the residuals is a minimum Residuals (d) = distance.

9.2A- Linear Regression Regression Line = Line of best fit The line for which the sum of the squares of the residuals is a minimum Residuals (d) = distance.
STATISTICS 12.0 Correlation and Linear Regression “Correlation and Linear Regression -”Causal Forecasting Method.

STATISTICS 12.0 Correlation and Linear Regression “Correlation and Linear Regression -”Causal Forecasting Method.
Graph and on a number line. Then copy and complete the statement with, or. 2 1 2 Example 2 Comparing Real Numbers 1 ? = 2 1 2 1 SOLUTION Use a calculator.

Graph and on a number line. Then copy and complete the statement with, or Example 2 Comparing Real Numbers 1 ? = SOLUTION Use a calculator.
Math 4030 – 11b Method of Least Squares. Model: Dependent (response) Variable Independent (control) Variable Random Error Objectives: Find (estimated)

Math 4030 – 11b Method of Least Squares. Model: Dependent (response) Variable Independent (control) Variable Random Error Objectives: Find (estimated)
1 Simple Linear Regression and Correlation Least Squares Method The Model Estimating the Coefficients EXAMPLE 1: USED CAR SALES.

1 Simple Linear Regression and Correlation Least Squares Method The Model Estimating the Coefficients EXAMPLE 1: USED CAR SALES.

Similar presentations

Ads by Google