The US government is interested in understanding what predicts death rates. They have a set of data that includes the number of deaths in each state, the number of deaths resulting from vehicle accidents (VEHICLE), the number of people dying from diabetes (DIABETES), the number of deaths related to the flu (FLU) and the number of homicide deaths (HOMICIDE). Your run a regression to predict deaths and get the following output: Which is not true of the coefficient of determination? A. It is the square of the coefficient of correlationB. It is negative when there is an inverse relationship between X and YC. It reports the percentage of the variation in Y explained by XD. It is calculated using sums of squares (e.g., SSR, SSE, SST). R2 cannot be negative Regression StatisticsMultiple R0.874642413R Square0.764999351Adjusted R Square0.744564512Standard Error62.97881926Observations51ANOVAdfMSSignificance FRegression4593934.928148483.73237.436035146.36772E-14Residual46182451.25713966.331676Total50 776386.1851Coefficients Standard ErrorUpper 95%t StatP-valueLower 95%Intercept240.800228551.133929614.7092063982.31605E-05137.8729666343.7274903VEHICLE3.0427829811.5823262211.9229808240.06068593-0.1422745066.227840468DIABETES11.242122651.6590664896.7761736651.97533E-087.90259501714.58165028FLU12.323045842.0570122155.9907499572.9897E-078. 18249500716.46359668HOMICIDE1.3621284562.1135628170.6444702970.522471501-2.8922528375.616509749

Identify the correct option which do not explains true about the coefficient of determination The…

Regression Statistics Multiple R 0.874642413 R Square 0.764999351 Adjusted R Square 0.744564512 Standard Error 62.97881926 Observations 51 ANOVA df MS Significance F Regression 4 593934.928 148483.732 37.43603514 6.36772E-14 Residual 46 182451.2571 3966.331676 Total 50 776386.1851 Coefficients Standard Error Upper 95% t Stat P-value Lower 95% Intercept 240.8002285 51.13392961 4.709206398 2.31605E-05 137.8729666 343.7274903 VEHICLE 3.042782981 1.582326221 1.922980824 0.06068593 -0.142274506 6.227840468 DIABETES 11.24212265 1.659066489 6.776173665 1.97533E-08 7.902595017 14.58165028 FLU 12.32304584 2.057012215 5.990749957 2.9897E-07 8. 182495007 16.46359668 HOMICIDE 1.362128456 2.113562817 0.644470297 0.522471501 -2.892252837 5.616509749

Regression Statistics Multiple R 0.874642413 R Square 0.764999351 Adjusted R Square 0.744564512 Standard Error 62.97881926 Observations 51 ANOVA df MS Significance F Regression 4 593934.928 148483.732 37.43603514 6.36772E-14 Residual 46 182451.2571 3966.331676 Total 50 776386.1851 Coefficients Standard Error Upper 95% t Stat P-value Lower 95% Intercept 240.8002285 51.13392961 4.709206398 2.31605E-05 137.8729666 343.7274903 VEHICLE 3.042782981 1.582326221 1.922980824 0.06068593 -0.142274506 6.227840468 DIABETES 11.24212265 1.659066489 6.776173665 1.97533E-08 7.902595017 14.58165028 FLU 12.32304584 2.057012215 5.990749957 2.9897E-07 8. 182495007 16.46359668 HOMICIDE 1.362128456 2.113562817 0.644470297 0.522471501 -2.892252837 5.616509749

Algebra & Trigonometry with Analytic Geometry

13th Edition

ISBN:9781133382119

Author:Swokowski

Publisher:Swokowski

Chapter4: Polynomial And Rational Functions

Section4.6: Variation

Problem 37E

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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

The US government is interested in understanding what predicts death rates. They have a set of data that includes the number of deaths in each state, the number of deaths resulting from vehicle accidents (VEHICLE), the number of people dying from diabetes (DIABETES), the number of deaths related to the flu (FLU) and the number of homicide deaths (HOMICIDE). Your run a regression to predict deaths and get the following output:
Which is not true of the coefficient of determination?

A. It is the square of the coefficient of correlation

B. It is negative when there is an inverse relationship between X and Y

C. It reports the percentage of the variation in Y explained by X

D. It is calculated using sums of squares (e.g., SSR, SSE, SST). R2 cannot be negative

Regression Statistics
Multiple R
0.874642413
R Square
0.764999351
Adjusted R Square
0.744564512
Standard Error
62.97881926
Observations
51
ANOVA
df
MS
Significance F
Regression
4
593934.928
148483.732
37.43603514
6.36772E-14
Residual
46
182451.2571
3966.331676
Total
50 776386.1851
Coefficients Standard Error
Upper 95%
t Stat
P-value
Lower 95%
Intercept
240.8002285
51.13392961
4.709206398
2.31605E-05
137.8729666
343.7274903
VEHICLE
3.042782981
1.582326221
1.922980824
0.06068593
-0.142274506
6.227840468
DIABETES
11.24212265
1.659066489
6.776173665
1.97533E-08
7.902595017
14.58165028
FLU
12.32304584
2.057012215
5.990749957
2.9897E-07
8. 182495007
16.46359668
HOMICIDE
1.362128456
2.113562817
0.644470297
0.522471501
-2.892252837
5.616509749

Definition Definition Relationship between two independent variables. A correlation 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.

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