Based on the value of r and/or the fit of the regression line, is the regression line a good predictor of a country’s life expectancy? Sheet1 Regression Analysis: Infant Mortality Rate versus Life Expectancy The regression equation is Infant Mortality Rate = 200.8 - 2.434 Life Expectancy Model Summary S R-sq R-sq(adj) 11.5768 85.24% 85.00% Analysis of Variance Source DF SS MS F P Regression 1 47227.4 47227.4 352.38 0.000 Error 61 8175.4 134.0 Total 62 55402.8

Based on the value of r and/or the fit of the regression line, is the regression line a good predictor of a country’s life expectancy? Sheet1 Regression Analysis: Infant Mortality Rate versus Life Expectancy The regression equation is Infant Mortality Rate = 200.8 - 2.434 Life Expectancy Model Summary S R-sq R-sq(adj) 11.5768 85.24% 85.00% Analysis of Variance Source DF SS MS F P Regression 1 47227.4 47227.4 352.38 0.000 Error 61 8175.4 134.0 Total 62 55402.8

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section4.6: Regression And Median-fit Lines

Problem 17HP

See similar textbooks

Regression Analysis: Infant Mortality Rate versus Life Expectancy

The regression equation is
Infant Mortality Rate = 200.8 - 2.434 Life Expectancy

Model Summary

S	R-sq	R-sq(adj)
11.5768	85.24%	85.00%

Analysis of Variance

Source	DF	SS	MS	F	P
Regression	1	47227.4	47227.4	352.38	0.000
Error	61	8175.4	134.0
Total	62	55402.8

Definition Definition Statistical method that estimates the relationship between a dependent variable and one or more independent variables. In regression analysis, dependent variables are called outcome variables and independent variables are called predictors.

Expert Solution

This question has been solved!

Explore an expertly crafted, step-by-step solution for a thorough understanding of key concepts.

This is a popular solution!

SEE SOLUTION Check out a sample Q&A here

Step 1

VIEW

Step 2

VIEW

Trending now

This is a popular solution!

Step by step

Solved in 2 steps

SEE SOLUTION Check out a sample Q&A here

Similar questions

Life Expectancy The following table shows the average life expectancy, in years, of a child born in the given year42 Life expectancy 2005 77.6 2007 78.1 2009 78.5 2011 78.7 2013 78.8 a. Find the equation of the regression line, and explain the meaning of its slope. b. Plot the data points and the regression line. c. Explain in practical terms the meaning of the slope of the regression line. d. Based on the trend of the regression line, what do you predict as the life expectancy of a child born in 2019? e. Based on the trend of the regression line, what do you predict as the life expectancy of a child born in 1580?2300
What is regression analysis? Describe the process of performing regression analysis on a graphing utility.
Would you please assist in answering this question with working out?Thank you. Q. Using a sample of recent university graduates, you estimate a simple linear regression using initial annual salary as the dependent variable and the graduate's weighted average mark (WAM) as the explanatory variable. If the regression model has an estimated intercept of 2200 and an estimated slope coefficient of 550, what is the predicted starting salary of a student with a WAM of 82?
A magazine surveyed graduates, asking about their point of view of the colleges they had attended. One good predictor of early career earnings ($/year) turned out to be the average SAT score of entering students. Here are the regression model and associated plots. Discuss the assumptions and conditions necessary for proceeding with the regression analysis. Do you think the conditions are satisfied? What conditions and assumptions must be satisfied? Select all that apply. A. Extrapolation Assumption B. Uniform Mean Assumption C. Independence Assumption D. Normal Population Assumption E. Linearity Assumption F. Reasonable Outliers Assumption G. Equal Variance Assumption
On the first day of class, an economics professor administers a test to gauge the math preparedness of her students. She believes that the performance on this math test and the number of hours studied per week on the course are the primary factors that predict a student's score on the final exam. Using data from her class of 60 students, she estimates A portion of the regression results is shown in the following table. a. What is the slope coefficient of Hours? b. What is the sample regression equation? c. What is the predicted final exam score for a student who has a math score of 70 and studies 4 hours per week?
Is depression a possible factor in students missing classes? Two of the variables in the study are DepressionScore, which gives score on a standard depression scale with higher numbers indicating greater depression, and ClassesMissed, the number of classes missed during the semester, for a sample of 253 college students. Computer output is shown below. a). Write down the regression equation to predict ClassesMissed using DepressionScore. b). Interpret the coefficient of determination, R-Square. c). Test if DepressionScore is a significant predictor for ClassesMissed at 5% level (write down the hypotheses, p-value, and conclusion). d). Is DepressionScore a significant predictor for ClassesMissed at 1% level? Explain. (You do not need to perform a complete step of hypothesis test, only write the conclusion).
A sociologist was hired by a large city hospital to investigate the relationship between the number of unauthorized days that employees are absent per year and the distance (miles) between home and work for the employee. A sample of 10 employees was chosen, and the following data were collected. A. Is the estimated regression equation appropriate and adequate
A magazine surveyed graduates, asking about their point of view of the colleges they had attended. One good predictor of early career earnings ($/year) turned out to be the average SAT score of entering students. Here are the regression model and associated plots. Test the hypotheses H0: β1=0 vs. HA: β1≠0. What is the test statistic?
Is the price of a smartphone affected by its display size, camera resolution, or internal storage capacity? Perform a regression analysis on the SmartphoneDCS dataset using MegaStat and answer the following questions. The level of significance is .05. When you copy and paste output from MegaStat to answer a question, remember to choose to "Keep Formatting" to paste the text. a. Do the data conform to Evans' Rule? What about Doane's Rule? Show the calculations to explain the reason for your answers.b. What is the critical value for the F test?c. Perform the regression and state the regression equation.d. Copy and paste the ANOVA table from your analysis. Is the overall fit of the model significant or not? Use your answer to part (b) to explain your decision.e. What is the coefficient of determination and the adjusted coefficient of determination for your analysis? What do these two coefficients tell you about the variation of price in your model?
A group of students measure the length and width of a random sample of beans. They are interested in investigating the relationship between the length and width. Their summary statistics are displayed in the table below. All units, if applicable, are millimeters. Mean width: 7.555 Stdev width: 0.914 Mean height: 12.686 Stdev height: 1.634 Correlation coefficient: 0.8203 d) If the students are interested in using the height of the beans to predict the width, calculate the slope of this new regression equation. e) Write the equation of the best-fit line that can be used to predict bean widths. Use x to represent height and y to represent width.
The owner of Showtime Movie Theaters, Inc. would like to predict weekly gross revenue as a function of advertising expenditures. Historical data for a sample of eight weeks follow. Weekly GrossRevenue($1000s) TelevisonAdvertising($1000s) NewspaperAdvertising($1000s) Part a 96 5.0 1.5 Develop an estimated regression equation with the amount of television advertising as the independent variable. 90 2.0 2.0 95 4.0 1.5 92 2.5 2.5 95 3.0 3.3 94 3.5 2.3 94 2.5 4.2 94 3.0 2.5 Part b Develop an estimated regression equation with both television advertising and news paper advertising as independent variables.…
Using a sample of recent university graduates, you estimate a simple linear regression using initial annual salary as the dependent variable and the graduate's weighted average mark (WAM) as the explanatory variable. If the regression model has an estimated intercept of 3200 and an estimated slope coefficient of 550, what is the predicted starting salary of a student with a WAM of 64?