27 - It is believed that, the average numbers of hours spent studying per day (HOURS) during undergraduate education should have a positive linear relationship with the starting salary (SALARY, measured in thousands of dollars per month) after graduation. Given below is the Excel output for predicting starting salary (Y) using number of hours spent studying per day (X) for a sample of 51 students. NOTE: Only partial output is shown. The value of the measured t-test statistic to test whether mean SALARY depends linearly on HOURS is Regression Statistics Multiple R R Square Adjusted R Square 0.8857 0.7845 0.7801 Standard Error 1.3704 Observations 51 ANOVA df SS MS F Significance F Regression Residual 1 335.0472 335.0473 178.3859 1.8782 Total 50 427.0798 Standard Coefficients Error t Stat P-value Lower 95% Upper 95% Intercept Hours -1.8940 0.4018 -4.7134 0.0000 -2.7015 -1.0865 0.9795 0.0733 13.3561 0.0000 0.8321 1.1269 Note: 2.051E-05 = 2.051*10 and 5.944E – 18 =5.944*10-18

27 - It is believed that, the average numbers of hours spent studying per day (HOURS) during undergraduate education should have a positive linear relationship with the starting salary (SALARY, measured in thousands of dollars per month) after graduation. Given below is the Excel output for predicting starting salary (Y) using number of hours spent studying per day (X) for a sample of 51 students. NOTE: Only partial output is shown. The value of the measured t-test statistic to test whether mean SALARY depends linearly on HOURS is Regression Statistics Multiple R R Square Adjusted R Square 0.8857 0.7845 0.7801 Standard Error 1.3704 Observations 51 ANOVA df SS MS F Significance F Regression Residual 1 335.0472 335.0473 178.3859 1.8782 Total 50 427.0798 Standard Coefficients Error t Stat P-value Lower 95% Upper 95% Intercept Hours -1.8940 0.4018 -4.7134 0.0000 -2.7015 -1.0865 0.9795 0.0733 13.3561 0.0000 0.8321 1.1269 Note: 2.051E-05 = 2.05110 and 5.944E – 18 =5.94410-18

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

27 - It is believed that, the average numbers of hours spent studying per day (HOURS) during undergraduate education
should have a positive linear relationship with the starting salary (SALARY, measured in thousands of dollars per
month) after graduation. Given below is the Excel output for predicting starting salary (Y) using number of hours
spent studying per day (X) for a sample of 51 students. NOTE: Only partial output is shown.
The value of the measured t-test statistic to test whether mean SALARY depends linearly on HOURS is
Regression Statistics
Multiple R
R Square
Adjusted R Square
0.8857
0.7845
0.7801
Standard Error
1.3704
Observations
51
ANOVA
df
SS
MS
F
Significance F
Regression
335.0472
335.0473
178.3859
Residual
1.8782
Total
50
427.0798
Standard
Coefficients
Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept
-1.8940
0.4018
-4.7134
0.0000
-2.7015
-1.0865
Hours
0.9795
0.0733
13.3561
0.0000
0.8321
1.1269
Note: 2.051E–05 = 2.051*10-05
and 5.944E – 18 =5.944 *10-18
a) -4.7134
b)
0.9795
C)
-1.8940
13.3561

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