(a) An estimated regression equation was developed relating the average number of runs given up per inning pitched given the average number of strikeouts per inning pitched and the average number of home runs per inning pitched. What are the values of R and R,? (Round your answers to four decimal places.) (b) Does the estimated regression equation provide a good fit to the data? Explain. Considering the nature of the data, being able to explain slightly -Select- v than 50% of the variability in the number of runs given up per inning pitched using just two independent variables is Select (c) Suppose the earned run average (ERA) is used as the dependent variable in the estimated regression equation from (a) instead of the average number of runs given up per inning pitched. Does the estimated regression equation that uses the ERA provide a good fit to the data? Explain. Since Select v than 50% of the variability in the ERA can be explained by the linear effect of home runs per inning pitched (HR/IP) and strike-outs per inning pitched (50/IP), this is Select- v considering the complexity of predicting pitching performance.

(a) An estimated regression equation was developed relating the average number of runs given up per inning pitched given the average number of strikeouts per inning pitched and the average number of home runs per inning pitched. What are the values of R and R,? (Round your answers to four decimal places.) (b) Does the estimated regression equation provide a good fit to the data? Explain. Considering the nature of the data, being able to explain slightly -Select- v than 50% of the variability in the number of runs given up per inning pitched using just two independent variables is Select (c) Suppose the earned run average (ERA) is used as the dependent variable in the estimated regression equation from (a) instead of the average number of runs given up per inning pitched. Does the estimated regression equation that uses the ERA provide a good fit to the data? Explain. Since Select v than 50% of the variability in the ERA can be explained by the linear effect of home runs per inning pitched (HR/IP) and strike-outs per inning pitched (50/IP), this is Select- v considering the complexity of predicting pitching performance.

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Related questions

Q: Hello, The following data was collected for the country Spain for the years 2007-1993. Using the…

A: The following multiple regression equation is given,From the given data, it is required to validate…

Q: If a new independent variable is added to a regression equation, the adjusted R2 increases only if…

A: The regression equation is used to make a prediction equation. It explains the relationship between…

Q: Based on the regression output, if a printer can print 4.8 pages per minute and copies in color,…

A: It is given that The reqression equation is Price = 238.3 + 26.8 Speed + 57.7 Color

Q: Based on the firm's data and the regression line, complete the following. (a) For these data, values…

A: The given regression line equation is as follows: ŷ=6.19+0.17x where x represents campaign cost and…

Q: What does a regression equation measure?

Q: The following table shows worldwide sales of a type of phòne and Year 2012 2013 2017 Selling Pricep…

Q: The accompanying data are the number of wins and the earned run averages (mean number of earned…

A: Solution: To fit the regression equation y^= β0^+β1^x Where β0^ intercept of the regression…

Q: The accompanying data are the number of wins and the earned run averages (mean number of earned…

A: Note: Hey there! Thank you for the question. As you have posted a question with multiple sub-parts,…

Q: A researcher determines that the correlation between number of siblings X and parental satisfaction…

A: The regression model given is: Y = 4.6 + 1.2X This means that for every one-unit increase in the…

Q: To monitor and improve its productivity, a company made an investigation and found out that the…

A: Note: Since we only answer up to 3 sub-parts, we'll answer the first 3. Please resubmit the…

Q: A regression analysis was performed to determine if there is a relationship between hours of TV…

A: Given that y=ax+b a= -0.784 b=34.422 r2=0.793881 r=0.891

Q: We want to predict the percentage weight loss for 2011 participants, based on 2010 data. If we…

A: Step 1:We want to predict the simple linear regression model where starting weight(xi) is a…

Q: test scores (Y). The results of the regression were: y=ax+b a=6.813 b=21.42 r2=0.403225 r=0.635 Use…

A: The regression equation is given by, y = ax+b y = 6.813 * x + 21.42

Q: The accompanying data are the number of wins and the earned run averages (mean number of earned…

A: From the given information, the equation of the regression line is y=-0.20x+6.58.

Q: Can you find r^2

A: From we have : X Y -0.3 0.09 0.3 0.07 2.1 0.47 1.8 0.85 0 0.31 0 0.38 0.3 0.71 0…

Q: What is meant by the slope of the regression

A: In a linear regression model, We have one dependent variable and one independent variable.

Q: A regression analysis was performed to determine if there is a relationship between hours of TV…

A: Define the variables: x = Number of hours of TV watched per day y= Number of sit ups a person can do…

Q: A regression was run to determine if there is a relationship between hours of TV watched per day (x)…

A: From the provided information, The regression equation is as follow: y = -0.817x + 32.111 Here x is…

Q: The three pieces of information that the standard error for slope is dependent on in an inference of…

Q: True or False - the r squared always decrease when explanatory variables are added to a regression…

A: Explain about the r squared always decrease or not when variables add to regression model.

Q: The accompanying table shows results from regressions per gal). The predictor (x) variables are WT…

A: Given that: Predictor(x) Variables P-Value R2 AdjustedR2 Regression Equation WT/DISP/HWY 0…

Q: Based on the regression results, answer the following questions* A sample of data is collected (from…

A: There are 6 independent variable in the given output. CEOANN, EMPL, MKTVAL, and EPSIN are all…

Q: 3 Click the icon to view the table of numnbers of wins and earned run average. ..... The equation of…

A: Given : X Y 20 2.67 18 3.31 17 2.66 16 3.72 14 3.93 12 4.39 11 3.79 9 5.14

Q: Regression Statistics Multiple R R Square Adjusted R Square Standard Error .490 Observations 1000…

A: Given:

Q: Use the two regression lines to estimate when sales in grocery stores are likely to equal sales in…

Q: In a regression context, under what situation is the predicted value for Y equal to the mean of Y?

A: To find: under what situation is the predicted value for Y equal to the mean of Y Let us assume…

Q: he admissions officer for Clearwater College developed the following estimated regression equation…

A: Given that:n= 10 Observations k= 2 independent variables

Q: A researcher is investigating possible explanations for deaths in traffic accidents. He examined…

A: The formula of R -squared is,where,SSR denotes the sum of regression squaresSSE denotes the error…

Q: Creating the app, they collected sample data and developed the following regression equation: where…

A: Given y = - 8.825 + 20.486x_{1} + 0.56x_{2} + 1.02x_{3}

Q: A company has a set of data with employee age (X) and the corresponding number of annual…

Q: e accompanying data are the number of wins and the eamed run averages (mean number of earned runs…

Concept explainers

Correlation

Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.

Linear Correlation

A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.

Regression Analysis

Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.

Question

(a) An estimated regression equation was developed relating the average number of runs given up per inning pitched given the average number of strikeouts per inning pitched and the average number of home runs per inning pitched. What are the values of R2 and R 2? (Round your answers to four decimal places.)
R2 =
R.2 =
(b) Does the estimated regression equation provide a good fit to the data? Explain.
Considering the nature of the data, being able to explain slightly ---Select-- v than 50% of the variability in the number of runs given up per inning pitched using just two independent variables is ---Select---
(c) Suppose the earned run average (ERA) is used as the dependent variable in the estimated regression equation from (a) instead of the average number of runs given up per inning pitched. Does the estimated regression equation that uses the ERA provide a good fit to the data? Explain.
Since --Select-- v than 50% of the variability in the ERA can be explained by the linear effect of home runs per inning pitched (HR/IP) and strike-outs per inning pitched (SO/IP), this is --Select--
v considering the complexity of predicting pitching performance.

Major League Baseball (MLB) pitching statistics were reported for a random sample of 20 pitchers from the American League for one full season.
Player
W L
so/IP HR/IP R/IP
Team
ERA
Verlander, J
DET
24 5
2.40
1.00
0.10
0.29
Beckett, J
BOS
13
7
2.89
0.91
0.11
0.34
Wilson, C
TEX
16 7
2.94
0.92
0.07
0.40
Sabathia, C
NYY
19 8
3.00
0.97
0.07
0.37
Haren, D
LAA
16
10
3.17
0.81
0.08
0.38
McCarthy, B
OAK
9 9
3.32
0.72
0.06
0.43
Santana, E
LAA
11
12
3.38
0.78
0.11
0.42
Lester, J
BOS
15
9
3.47
0.95
0.10
0.40
Hernandez, F
SEA
14
14 3.47
0.95
0.08
0.42
Buehrle, M
CWS
13
9
3.59
0.53
0.10
0.45
Pineda, M
SEA
9
10
3.74
1.01
0.11
0.44
Colon, B
NYY
8
10
4.00
0.82
0.13
0.52
Tomlin, J
CLE
12 7
4.25
0.54
0.15
0.48
Pavano, C
MIN
9.
9 13
4.30
0.46
0.10
0.55
Danks, J
CWS
Cws
8
8 12
4.33
0.79
0.11
0.52
Guthrie, J
BAL
17
4.33
0.63
0.13
0.54
Lewis, C
TEX
14
10
4.40
0.84
0.17
0.51
Scherzer, M
DET
15
9
4.43
0.89
0.15
0.52
Davis, W
TB
11 10
4.45
0.57
0.13
0.52
Porcello, R
DET
14
4.75
0.57
0.10
0.57

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

Step 3

VIEW

Trending now

This is a popular solution!

Step by step

Solved in 3 steps with 2 images

SEE SOLUTION Check out a sample Q&A here

Knowledge Booster

$Correlation, Regression, and Association$

Learn more about

Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, statistics and related others by exploring similar questions and additional content below.

Similar questions

A regression was run to determine if there is a relationship between hours of study per week (x) and the final exam scores (y). The results of the regression were: y=ax+ba=6.88b=34.85r2=0.535824r=0.732Use this to predict the final exam score of a student who studies 6 hours per week, and please round your answer to a whole number.
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=ax+ba=-0.759b=20.094r2=0.573049r=-0.757 Use this to predict the number of situps a person who watches 7 hours of TV can do (to one decimal place)
**Based on the regression results, answer the following questions** Data are collected on the income (measured in $1000’s of $’s), house value (measured in $1000’s of $’s), years in a home, age, sex (=1 for male, =0 for female), and the amount outstanding on a mortgage (measured in $1000’s of $’s). The dependent variable is income. Based on the regression results, answer the following questions b) What is the estimated regression equation? c) How much of the variation in income is explained by the regressors? d) What is the standard error of the error term in the regression equation?
The accompanying data are the number of wins and the earned run averages (mean number of earned runs allowed per nine innings pitched) for eight baseball pitchers in a recent season. Find the equation of the regression line. Then construct a scatter plot of the data and draw the regression line. Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. If the x-value is not meaningful to predict the value of y, explain why not. (a) x = 5 wins (b) x = 10 wins (c) x = 19 wins (d) x = 15 wins Click the icon to view the table of numbers of wins and earned run average. The equation of the regression line is y = x+. (Round to two decimal places as needed.)
Data were collected to explain the amount of a customer’s purchase (expressed in $’s) based on the amount of time the customer has spent on the company’s website (expressed in minutes) Based on the regression results, answer the following questions a) What is the estimated regression equation? b) What percentage of the variation in purchase amount is explained by time? c) What is the correlation between purchase amount and time?
Scenario: A medical researcher wishes to see whether there is a relationship between a person's age, cholesterol level, and systolic blood pressure. Eight people are randomly selected. The data is listed in the table. First, find the multiple regression equation. Next, determine the coefficient of determination. Then, use the regression equation to predict a person's blood pressure reading if the person selected is 50 years old with a cholesterol reading of 220. Age Cholesterol level Blood pressure Person 1 38 220 116 Person 2 41 225 120 Person 3 45 200 123 Person 4 48 190 131 Person 5 51 250 142 Person 6 53 215 145 Person 7 57 200 148 Person 8 61 170 150 Discussion Prompts Respond to the following prompts in your initial post: 1. Identify the explanatory variables and response variable for the data. 2. What is the multiple regression equation for the data? 3. What is the coefficient of determination? 4. If a person 50 years old with a cholesterol of 220 is selected, what is that…
A team from UNHABITAT took a random sample of 230 households in a city to better understand how the size of a house (measured in metres) is related to the number of people living in that house. They performed a regression analysis on the data and found that regression model had an R² = 18.3%. What is the correlation between the size of a house and the number of people living in that house? 0.667 0.183 0.033 0.904 0.428
The age and height (in cm) of 400 adult women from Bolivia were measured. A researcher wants to know if age has any effect on height. A linear regression is carried out in Minitab and the following output obtained. Coefficients Term Constant Age (a) Write down the regression model. (b) Interpret the regression coefficient for the fitted model. (c) Use the output from Minitab to explain if the age of a participant affects their height. Percent (d) The normal probability plot of the residuals from this regression model is given below. Do the assumptions of the regression model seem reasonable? Justify your answer. 99.9 8 28 22299229 88 Coef SE Coef 152.94 7.69 0.022 0.231 01 -100 T-Value P-Value VIF 19.90 0.000 0.10 0.924 1.00 -50 Normal Probability Plot (response is Height) 0 Residual 50 ***** 100 150
What is measured by the standard error of estimaate for a regression equation?
**Based on the regression results, answer the following questions** Data are collected on the income (measured in $1000’s of $’s), house value (measured in $1000’s of $’s), years in a home, age, sex (=1 for male, =0 for female), and the amount outstanding on a mortgage (measured in $1000’s of $’s). The dependent variable is income. Based on the regression results, answer the following questions e) Are any of the explanatory (independent) variables significant at the 10% level of significance? How do you know? f) What is the predicted value of income for a person that has been in a house for 5 years, is 38 years old, is a male, has a house valued at $250,000, and has $100,000 left on their mortgage? (g) What is the effect on the income of a person being a male versus a female?
Develop an estimated multiple regression equation that relates risk of a stroke to a person's age, systolic blood pressure and whether the person is a smoker. x1=persons age x2systrolic blood pressure x3=whether person is a smoker or non smoker
Might we be able to predict life expectancies from birthrates? Below are bivariate data giving birthrate and life expectancy information for each of twelve countries. For each of the countries, both x, the number of births per one thousand people in the population, and y, the female life expectancy (in years), are given. Also shown are the scatter plot for the date and the least-squares regression line. The equation for this line is y=82.84-0.49x. Birthrate, x (number of births per 1000 people) 24.6 50.1 40,6 20.0 50.7 28.1 143 49.0 31.9 14.8 33.6 46.5 Send data to calculator Send data to Excel Female life expectancy, y (in years) 74.3 54.4 66.2 73.6 58.9 72.7 76.1 61.5 62.4 71.4 68.0 57:5 Based on the sample data and the regression line, complete the following. Female life expectancy (in years) Birthrate (number of births per 1000 people)