4. The following output from R presents the results from computing a linear model.In our data example we are interested to study the relationship between students' academic performanceapi00 with variable enroll which is the number of students in the school.Call:1m (formula = api00 - enroll, data = d)Residuals:мin10 Median30Маx-285.50 -112.55-6.7095.06 389.15Coefficients:Estimate Std. Error t value Pr(>|t|)(Intercept) 744.25141enroll15.93308 46.711 < 2e-16 ***-0.199870.02985 -6.695 7.34e-11 ***Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1' 1Residual standard error: 135 on 398 degrees of freedomMultiple R-squared: 0.1012, Adjusted R-squared:F-statistic: 44.83 on 1 and 398 DF, p-value: 7.339e-110.09898a. Predict the monthly auto insurance premium for a driver with 10 years of driving experience.b. Compute the standard deviation of errors.c. Using alpha=0.05, test whether B, is different from zero

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4. The following output from R presents the results from computing a linear model. In our data example we are interested to study the relationship between students' academic performance api00 with variable enroll which is the number of students in the school. Call: Im (formula = api00 - enrol1, data = d) Residuals: Min 10 Median 30 Мах -285.50 -112.55 -6.70 95.06 389.15 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 744.25141 15.93308 46.711 < 2e-16 *** enroll -0.19987 0.02985 -6.695 7.34e-11 *** Signif. codes: 0 '*** ' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' 1 %3D Residual standard error: 135 on 398 degrees of freedom Multiple R-squared: 0.1012, Adjusted R-squared: F-statistic: 44.83 on 1 and 398 DF, p-value: 7.339e-11 0.09898 a. Predict the monthly auto insurance premium for a driver with 10 years of driving experience. b. Compute the standard deviation of errors. c. Using alpha=0.05, test whether B, is different from zero

4. The following output from R presents the results from computing a linear model. In our data example we are interested to study the relationship between students' academic performance api00 with variable enroll which is the number of students in the school. Call: Im (formula = api00 - enrol1, data = d) Residuals: Min 10 Median 30 Мах -285.50 -112.55 -6.70 95.06 389.15 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 744.25141 15.93308 46.711 < 2e-16 * enroll -0.19987 0.02985 -6.695 7.34e-11 * Signif. codes: 0 '* ' 0.001 '' 0.01 '*' 0.05 '.' 0.1 ' 1 %3D Residual standard error: 135 on 398 degrees of freedom Multiple R-squared: 0.1012, Adjusted R-squared: F-statistic: 44.83 on 1 and 398 DF, p-value: 7.339e-11 0.09898 a. Predict the monthly auto insurance premium for a driver with 10 years of driving experience. b. Compute the standard deviation of errors. c. Using alpha=0.05, test whether B, is different from zero

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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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

4. The following output from R presents the results from computing a linear model.
In our data example we are interested to study the relationship between students' academic performance
api00 with variable enroll which is the number of students in the school.
Call:
1m (formula = api00 - enroll, data = d)
Residuals:
мin
10 Median
30
Маx
-285.50 -112.55
-6.70
95.06 389.15
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 744.25141
enroll
15.93308 46.711 < 2e-16 ***
-0.19987
0.02985 -6.695 7.34e-11 ***
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1
' 1
Residual standard error: 135 on 398 degrees of freedom
Multiple R-squared: 0.1012, Adjusted R-squared:
F-statistic: 44.83 on 1 and 398 DF, p-value: 7.339e-11
0.09898
a. Predict the monthly auto insurance premium for a driver with 10 years of driving experience.
b. Compute the standard deviation of errors.
c. Using alpha=0.05, test whether B, is different from zero

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