A computer software developer would like to use the number of downloads (in thousands) for the trial version of his new shareware to predict the amount of revenue(in thousands of dollars) he can make on the full version of the new shareware. Following is the output from a simple linear regression along with the residual plotand normal probability plot obtained from a data set of 30 different sharewares that he has developed:Regression StatisticsMultiple RR SquareAdjusted R SquareStandard Error0.86910.75540.746744.4765Observations30.0000ANOVASS171062.9193 171062.9193Significance F0.0000dfMSRegressionAResidual186.47592855388.43091978. 1582Total29226451.3503Coefficients95.0614Standard Eror26.9183Lower 95%-150, 2009Upper 95%39.92184.5513P-valueInterceptDownloadt Stat3.53159.29920.00150.00003.72970.40112.9082Download Residual Plot12010080604020-2040-60-80-1000.0020.0040.0060.0080.00100.00120.00DownloadNormal Probability Plot12010080604020-20........-40-6080100-2-11.3.7 ValueResidualsResiduals Referring to Table 12-11, which of the following is the correct interpretation for the coefficient of determination?O A.74.67% of the variation in revenue can be explained by the variation in the number of downloads.В.75.54% of the variation in revenue can be explained by the variation in the number of downloads.74.67% of the variation in the number of downloads can be explained by the variation in revenue.D.75.54% of the variation in the number of downloads can be explained by the variation in revenue.

We have given the output of regression model

Referring to Table 12-11, which of the following is the correct interpretation for the coefficient of determination? А. 74.67% of the variation in revenue can be explained by the variation in the number of downloads. В. 75.54% of the variation in revenue can be explained by the variation in the number of downloads. OC. 74.67% of the variation in the number of downloads can be explained by the variation in revenue. D. 75.54% of the variation in the number of downloads can be explained by the variation in revenue.

Referring to Table 12-11, which of the following is the correct interpretation for the coefficient of determination? А. 74.67% of the variation in revenue can be explained by the variation in the number of downloads. В. 75.54% of the variation in revenue can be explained by the variation in the number of downloads. OC. 74.67% of the variation in the number of downloads can be explained by the variation in revenue. D. 75.54% of the variation in the number of downloads can be explained by the variation in revenue.

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

A computer software developer would like to use the number of downloads (in thousands) for the trial version of his new shareware to predict the amount of revenue
(in thousands of dollars) he can make on the full version of the new shareware. Following is the output from a simple linear regression along with the residual plot
and normal probability plot obtained from a data set of 30 different sharewares that he has developed:
Regression Statistics
Multiple R
R Square
Adjusted R Square
Standard Error
0.8691
0.7554
0.7467
44.4765
Observations
30.0000
ANOVA
SS
171062.9193 171062.9193
Significance F
0.0000
df
MS
Regression
AResidual
1
86.4759
28
55388.4309
1978. 1582
Total
29
226451.3503
Coefficients
95.0614
Standard Eror
26.9183
Lower 95%
-150, 2009
Upper 95%
39.9218
4.5513
P-value
Intercept
Download
t Stat
3.5315
9.2992
0.0015
0.0000
3.7297
0.4011
2.9082
Download Residual Plot
120
100
80
60
40
20
-20
40
-60
-80
-100
0.00
20.00
40.00
60.00
80.00
100.00
120.00
Download
Normal Probability Plot
120
100
80
60
40
20
-20
........
-40
-60
80
100
-2
-1
1.
3.
7 Value
Residuals
Residuals

Referring to Table 12-11, which of the following is the correct interpretation for the coefficient of determination?
O A.
74.67% of the variation in revenue can be explained by the variation in the number of downloads.
В.
75.54% of the variation in revenue can be explained by the variation in the number of downloads.
74.67% of the variation in the number of downloads can be explained by the variation in revenue.
D.
75.54% of the variation in the number of downloads can be explained by the variation in revenue.

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