Simple Regression Anser Pe4

True / False Questions

1. A scatter plot is used to visualize the association (or lack of association) beteen to !uantitative variables.

TRUE

"he scatter plot shos association beteen to !uantitative variables.

AA!S*+ Anal"tic

*looms+ Remember Diclt"+  Eas"

/earning 0b1ecti2e+ 3-4 !alclate and test a correlation coecient 5or signi6cance.

Topic+ 7isal Displa"s and !orrelation Anal"sis

2. "he correlation coe$cient r measures the strength of the linear relationship beteen to variables.

TRUE

A correlation coe$cient measures linearit4 beteen to variables.

AA!S*+ Anal"tic

*looms+ Remember Diclt"+  Eas"

/earning 0b1ecti2e+ 3-4 !alclate and test a correlation coecient 5or signi6cance.

Topic+ 7isal Displa"s and !orrelation Anal"sis

%. &earson's correlation coe$cient (r ) re!uires that both variables be interval or ratio data.

TRUE

Correlation assumes !uantitative data ith at least interval measurements.

AA!S*+ Anal"tic

*looms+ Remember Diclt"+  Eas"

/earning 0b1ecti2e+ 3-4 !alclate and test a correlation coecient 5or signi6cance.

Topic+ 7isal Displa"s and !orrelation Anal"sis

. f r * .++ and n * 1,- then the correlation is significant at  * ./+ in a to0tailed test.

TRUE

t calc * r N(n 0 2)L(1 0 r ²)O^1L2 * (.++)N(1, 0 2)L(1 0 .++²)O^1L2 * 2., Q t ./2+ * 2.1+ for d.5. * 1, 0 2 * 1.

AA!S*+ Anal"tic

*looms+ Appl"

Diclt"+ 3 Medim /earning 0b1ecti2e+ 3-4 !alclate and test a correlation coecient 5or signi6cance.

Topic+ 7isal Displa"s and !orrelation Anal"sis

+. A sample correlation r * ./ indicates a stronger linear relationship than r * 0.,/.

FALSE

"he sign onl4 indicates the direction- not the strength- of the linear relationship.

AA!S*+ Anal"tic

*looms+ 8nderstand Diclt"+  Eas"

/earning 0b1ecti2e+ 3-4 !alclate and test a correlation coecient 5or signi6cance.

Topic+ 7isal Displa"s and !orrelation Anal"sis

,. A common source of spurious correlation beteen X and Y is hen a third unspecied variable Z aects both X and Y .

TRUE

Both X and Y could be inuenced b4 Z .

AA!S*+ Anal"tic

*looms+ 8nderstand Diclt"+  Eas"

/earning 0b1ecti2e+ 3-4 !alclate and test a correlation coecient 5or signi6cance.

Topic+ 7isal Displa"s and !orrelation Anal"sis

3. "he correlation coe$cient r ala4s has the same sign as b1 in Y * b/

5 b1 X . TRUE

"he t 0test for the slope in simple regression gives the same result as the t 0test for r .

AA!S*+ Anal"tic

*looms+ 8nderstand Diclt"+  Eas"

/earning 0b1ecti2e+ 3-49 Fit a simple regression on an E%cel scatter plot.

Topic+ Regression Terminolog"

6. "he tted intercept in a regression has little meaning if no data values near X * / have been observed.

TRUE

&redicting Y for X * / makes little sense if the observed data have no values near X * /.

AA!S*+ Anal"tic

*looms+ 8nderstand Diclt"+  Eas"

/earning 0b1ecti2e+ 3-43 Interpret t)e slope and intercept o5 a r egression e:ation.

Topic+ Simple Regression

7. "he least s!uares regression line is obtained hen the sum of the s!uared residuals is minimized.

TRUE

"he =JS method minimizes the sum of s!uared residuals.

AA!S*+ Anal"tic

*looms+ Remember Diclt"+  Eas"

/earning 0b1ecti2e+ 3-49 Fit a simple regression on an E%cel scatter plot.

Topic+ 0rdinar" /east S:ares Formlas

1/. n a simple regression- if the coe$cient for X is positive and

signicantl4 dierent from zero- then an increase in X is associated

ith an increase in the mean (i.e.- the e8pected value) of ^Y. TRUE

"he conditional mean ofY depends on X (unless the slope is eectivel4 zero).

AA!S*+ Anal"tic

*looms+ 8nderstand Diclt"+  Eas"

/earning 0b1ecti2e+ 3-43 Interpret t)e slope and intercept o5 a r egression e:ation.

Topic+ Simple Regression

11. n least0s!uares regression- the residuals e1- e2- . . . - en ill ala4s have a zero mean.

TRUE

"he residuals must sum to zero if the =JS method is used- so their mean is zero.

AA!S*+ Anal"tic

*looms+ Remember Diclt"+ 3 Medim /earning 0b1ecti2e+ 3-43 Interpret t)e slope and intercept o5 a r egression e:ation.

Topic+ 0rdinar" /east S:ares Formlas

12. 9hen using the least s!uares method- the column of residuals ala4s sums to zero.

TRUE

"he residuals must sum to zero if the =JS method is used.

AA!S*+ Anal"tic

*looms+ Remember Diclt"+ 3 Medim /earning 0b1ecti2e+ 3-43 Interpret t)e slope and intercept o5 a r egression e:ation.

Topic+ 0rdinar" /east S:ares Formlas

1%. n the model Sales * 2,6 5 3.%3 Ads- an additional :1 spent on ads

ill increase sales b4 3.%3 percent.

FALSE

"he slope coe$cient is in the same units as Y (dollars- not percent-in this case).

AA!S*+ Anal"tic

*looms+ Appl"

Diclt"+ 3 Medim /earning 0b1ecti2e+ 3-43 Interpret t)e slope and intercept o5 a r egression e:ation.

Topic+ Simple Regression

1. f R² * .%, in the model Sales * 2,6 5 3.%3 Ads ith n * +/- the to0 tailed test for correlation at α * ./+ ould sa4 that there is a

signicant correlation beteen Sales and Ads.

TRUE

t calc * r N(n 0 2)L(1 0 r ²)O^1L2 * (.,/)N(+/ 0 2)L(1 0 .%,)O^1L2 * +.17, Q t ./2+ * 2./11 for d.5. * +/ 0 2 * 6.

AA!S*+ Anal"tic

*looms+ Appl"

Diclt"+ ; $ard /earning 0b1ecti2e+ 3-4 !alclate and test a correlation coecient 5or signi6cance.

Topic+ 7isal Displa"s and !orrelation Anal"sis

1+. f R² * .%, in the model Sales * 2,6 5 3.%3 Ads- then Ads e8plains

%, percent of the variation in Sales.

TRUE

9e can interpret R² as the fraction of variation in Y e8plained b4 X (e8pressed as a percent).

AA!S*+ Anal"tic

*looms+ Appl"

Diclt"+  Eas"

/earning 0b1ecti2e+ 3-4< Interpret t)e standard error= R3= A>07A table= and F test.

Topic+ 0rdinar" /east S:ares Formlas

1,. "he ordinar4 least s!uares regression line ala4s passes through the point .

TRUE

"he =JS formulas re!uire the line to pass through this point.

AA!S*+ Anal"tic

*looms+ Remember Diclt"+ 3 Medim /earning 0b1ecti2e+ 3-43 Interpret t)e slope and intercept o5 a r egression e:ation.

Topic+ Regression Terminolog"

13. "he least s!uares regression line gives unbiased estimates of β/ and β1.

TRUE

"he e8pected values of the =JS estimators b/ and b1 are the true parameters β/ and β1.

AA!S*+ Anal"tic

*looms+ Remember Diclt"+ 3 Medim /earning 0b1ecti2e+ 3-49 Fit a simple regression on an E%cel scatter plot.

Topic+ 0rdinar" /east S:ares Formlas

16. n a simple regression- the correlation coe$cient r is the s!uare root of R².

TRUE

n fact- e could use the notation r ² instead of R² hen talking about simple regression.

AA!S*+ Anal"tic

*looms+ Remember Diclt"+ 3 Medim /earning 0b1ecti2e+ 3-4< Interpret t)e standard error= R3= A>07A table= and F test.

Topic+ 0rdinar" /east S:ares Formlas

17. f SSR is 16// and SSE is 2//- then R² is .7/.

TRUE

R² * SSRLSST * SSRL(SSR 5 SSE) * 16//L(16// 5 2//) * .7/.

AA!S*+ Anal"tic

*looms+ Appl"

Diclt"+ 3 Medim /earning 0b1ecti2e+ 3-4< Interpret t)e standard error= R3= A>07A table= and F test.

Topic+ Tests 5or Signi6cance

2/. "he idth of a prediction interval for an individual value ofY is less than standard error se.

FALSE

"he formula for the interval idth multiplies the standard error b4 an e8pression Q 1.

AA!S*+ Anal"tic

*looms+ 8nderstand Diclt"+ 3 Medim /earning 0b1ecti2e+ 3-4? Distingis) bet@een con6dence and prediction inter2als 5or Y.

Topic+ !on6dence and &rediction Inter2als 5or Y

21. f SSE is near zero in a regression- the statistician ill conclude that the proposed model probabl4 has too poor a t to be useful.

FALSE

SSF is the sum of the s!uare residuals- hich ould be smaller if the

t is good.

AA!S*+ Anal"tic

*looms+ Appl"

Diclt"+ 3 Medim /earning 0b1ecti2e+ 3-4< Interpret t)e standard error= R3= A>07A table= and F test.

Topic+ Tests 5or Signi6cance

22. #or a regression ith 2// observations- e e8pect that about 1/

residuals ill e8ceed to standard errors.

TRUE

f the residuals are normal- 7+. percent (17/ of 2//) ill lie ithin M2se (so 1/ outside).

AA!S*+ Anal"tic

*looms+ Appl"

Diclt"+ 3 Medim /earning 0b1ecti2e+ 3- Identi5" nsal residals and )ig)-le2erage obser2ations.

Topic+ 8nsal 0bser2ations

2%. Condence intervals for predicted Y are less precise hen the residuals are ver4 small.

FALSE

Small residuals impl4 a small standard error and thus a narro@er prediction interval.

AA!S*+ Anal"tic

*looms+ 8nderstand Diclt"+ 3 Medim /earning 0b1ecti2e+ 3-4? Distingis) bet@een con6dence and prediction inter2als 5or Y.

Topic+ !on6dence and &rediction Inter2als 5or Y

2. Cause0and0eect direction beteen X and Y ma4 be determined b4 running the regression tice and seeing hether Y * β/ 5 β1 X or X * β1 5 β/Y has the largerR².

FALSE

Cause and eect cannot be determined in the conte8t of simple regression models.

AA!S*+ Anal"tic

*looms+ 8nderstand Diclt"+ 3 Medim /earning 0b1ecti2e+ 3-43 Interpret t)e slope and intercept o5 a r egression e:ation.

Topic+ Simple Regression

2+. "he ordinar4 least s!uares method of estimation minimizes the estimated slope and intercept.

FALSE

=JS minimizes the sum of s!uared residuals.

AA!S*+ Anal"tic

*looms+ Remember Diclt"+ 3 Medim /earning 0b1ecti2e+ 3-49 Fit a simple regression on an E%cel scatter plot.

Topic+ 0rdinar" /east S:ares Formlas

2,. ;sing the ordinar4 least s!uares method ensures that the residuals

ill be normall4 distributed.

FALSE

=JS produces unbiased estimates but cannot ensure normalit4 of the residuals.

AA!S*+ Anal"tic

*looms+ Remember Diclt"+ 3 Medim /earning 0b1ecti2e+ 3-4 Test residals 5or 2iolations o5 regression assmptions.

Topic+ Residal Tests

23. f 4ou have a strong outlier in the residuals- it ma4 represent a dierent causal s4stem.

TRUE

=utliers might come from a dierent population or causal s4stem.

AA!S*+ Anal"tic

*looms+ 8nderstand Diclt"+ 3 Medim /earning 0b1ecti2e+ 3- Identi5" nsal residals and )ig)-le2erage obser2ations.

Topic+ 0t)er Regression &roblems 0ptionalB

26. A negative correlation beteen to variables X and Y usuall4 4ields a negative p0value for r .

FALSE

"he p0value cannot be negative.

AA!S*+ Anal"tic

*looms+ 8nderstand Diclt"+ 3 Medim /earning 0b1ecti2e+ 3-4C Test )"pot)eses abot t)e slope and intercept b" sing t tests.

Topic+ 7isal Displa"s and !orrelation Anal"sis

27. n linear regression beteen to variables- a signicant relationship e8ists hen the p0value of the t test statistic for the slope is greater than α.

FALSE

ReHect β1 * / if the p0value is less t)an α.

AA!S*+ Anal"tic

*looms+ Appl"

Diclt"+  Eas"

/earning 0b1ecti2e+ 3-4C Test )"pot)eses abot t)e slope and intercept b" sing t tests.

Topic+ Tests 5or Signi6cance

%/. "he larger the absolute value of the t statistic of the slope in a simple linear regression- the stronger the linear relationship e8ists beteen X and Y .

TRUE

"he correlation coe$cient measures linearit4- regardless of its sign (5 or 0).

AA!S*+ Anal"tic

*looms+ Appl"

Diclt"+  Eas"

/earning 0b1ecti2e+ 3-4C Test )"pot)eses abot t)e slope and intercept b" sing t tests.

Topic+ Tests 5or Signi6cance

%1. n simple linear regression- the coe$cient of determination (^R²) is estimated from sums of s!uares in the A<=>A table.

TRUE

R² * SSRLSST or R² * 1 0 SSELSST .

AA!S*+ Anal"tic

*looms+ Remember Diclt"+ 3 Medim /earning 0b1ecti2e+ 3-4< Interpret t)e standard error= R3= A>07A table= and F test.

Topic+ 0rdinar" /east S:ares Formlas

%2. n simple linear regression- the p0value of the slope ill ala4s e!ual the p0value of the F statistic.

TRUE

"his is true onl4 if there is one predictor (but is no longer true in multiple regression).

AA!S*+ Anal"tic

*looms+ Remember Diclt"+ 3 Medim /earning 0b1ecti2e+ 3-4< Interpret t)e standard error= R3= A>07A table= and F test.

Topic+ Anal"sis o5 7ariance+ 02erall Fit

%%. An observation ith high leverage ill have a large residual (usuall4 an outlier).

FALSE

"he concepts are distinct (a high0leverage point could have a good

t).

AA!S*+ Anal"tic

*looms+ 8nderstand Diclt"+ 3 Medim /earning 0b1ecti2e+ 3- Identi5" nsal residals and )ig)-le2erage obser2ations.

Topic+ 8nsal 0bser2ations

%. A prediction interval for Y is narroer than the corresponding condence interval for the mean of ^Y.

FALSE

&redicting an individual case re!uires a ider condence interval than predicting the mean.

AA!S*+ Anal"tic

*looms+ Remember Diclt"+ 3 Medim /earning 0b1ecti2e+ 3-4? Distingis) bet@een con6dence and prediction inter2als 5or Y.

Topic+ !on6dence and &rediction Inter2als 5or Y

%+. 9hen X is farther from its mean- the prediction interval and condence interval for Y become ider.

TRUE

"he idth increases hen X diers from its mean (revie the formula).

AA!S*+ Anal"tic

*looms+ 8nderstand Diclt"+ 3 Medim /earning 0b1ecti2e+ 3-4? Distingis) bet@een con6dence and prediction inter2als 5or Y.

Topic+ !on6dence and &rediction Inter2als 5or Y

%,. "he total sum of s!uares (SST ) ill never e8ceed the regression sum of s!uares (SSR).

FALSE

"he identit4 is SSR 5 SSE * SST .

AA!S*+ Anal"tic

*looms+ Remember Diclt"+  Eas"

/earning 0b1ecti2e+ 3-4< Interpret t)e standard error= R3= A>07A table= and F test.

Topic+ Anal"sis o5 7ariance+ 02erall Fit

%3. ?@igh leverage? ould refer to a data point that is poorl4 predicted b4 the model (large residual).

FALSE

A high0leverage observation ma4 have a good t (onl4 its X value determines its leverage).

AA!S*+ Anal"tic

*looms+ Remember Diclt"+ 3 Medim /earning 0b1ecti2e+ 3- Identi5" nsal residals and )ig)-le2erage obser2ations.

Topic+ 8nsal 0bser2ations

%6. "he studentized residuals permit us to detect cases here the regression predicts poorl4.

TRUE

Studentized residuals resemble a t 0distribution. A large studentized t 0 value (e.g.- t D 02.// or t Q 5 2.//) ould implies a poor t.

AA!S*+ Anal"tic

*looms+ 8nderstand Diclt"+ 3 Medim /earning 0b1ecti2e+ 3- Identi5" nsal residals and )ig)-le2erage obser2ations.

Topic+ 8nsal 0bser2ations

%7. A poor prediction (large residual) indicates an observation ith high leverage.

FALSE

@igh leverage indicates an unusuall4 large or small  value (not a poor prediction). A high0leverage observation ma4 have a good t or a poor t. =nl4 its X value determines its leverage.

AA!S*+ Anal"tic

*looms+ 8nderstand Diclt"+ 3 Medim /earning 0b1ecti2e+ 3- Identi5" nsal residals and )ig)-le2erage obser2ations.

Topic+ 8nsal 0bser2ations

/. Ill-conditioned refers to a variable hose units are too large or too small (e.g.- :2-%-+,3).

TRUE

n F8cel- a s4mptom of poor data conditioning is e8ponential notation (e.g.- .%F 5 /,).

AA!S*+ Anal"tic

*looms+ Remember Diclt"+ 3 Medim /earning 0b1ecti2e+ 3-4 &er5orm r egression anal"sis @it) E%cel or ot)er so5t@are.

Topic+ 0t)er Regression &roblems 0ptionalB

1. A simple decimal transformation (e.g.- from 16-271 to 16.271) often improves data conditioning.

TRUE

Peeping data magnitudes similar helps avoid e8ponential notation (e.g.- .%F 5 /,).

AA!S*+ Anal"tic

*looms+ 8nderstand Diclt"+ 3 Medim /earning 0b1ecti2e+ 3-4 &er5orm r egression anal"sis @it) E%cel or ot)er so5t@are.

Topic+ 0t)er Regression &roblems 0ptionalB

2. "o0tailed t-tests are often used because an4 predictor that diers signicantl4 from zero in a to0tailed test ill also be signicantl4 greater than zero or less than zero in a one0tailed test at the same α.

TRUE

"rue because the critical t is larger in the to0tailed test (the default in most softare).

AA!S*+ Anal"tic

*looms+ Appl"

Diclt"+ 3 Medim /earning 0b1ecti2e+ 3-4C Test )"pot)eses abot t)e slope and intercept b" sing t tests.

Topic+ Tests 5or Signi6cance

%. A predictor that is signicant in a one0tailed t-test ill also be signicant in a to0tailed test at the same level of signicance α. FALSE

#alse because the critical t ould be larger in a to0tailed test.

AA!S*+ Anal"tic

*looms+ Remember Diclt"+ 3 Medim /earning 0b1ecti2e+ 3-4C Test )"pot)eses abot t)e slope and intercept b" sing t tests.

Topic+ Tests 5or Signi6cance

. =mission of a relevant predictor is a common source of model misspecication.

TRUE

n a multivariate orld- simple regression ma4 be inade!uate.

AA!S*+ Anal"tic

*looms+ Remember Diclt"+ 3 Medim /earning 0b1ecti2e+ 3-4 &er5orm r egression anal"sis @it) E%cel or ot)er so5t@are.

Topic+ 0t)er Regression &roblems 0ptionalB

+. "he regression line must pass through the origin.

FALSE

"he =JS intercept estimate does not- in general- e!ual zero. 9e might be unable to reHect a zero intercept if a t 0test- but the tted intercept is rarel4 zero.

AA!S*+ Anal"tic

*looms+ Remember Diclt"+  Eas"

/earning 0b1ecti2e+ 3-49 Fit a simple regression on an E%cel scatter plot.

Topic+ 0rdinar" /east S:ares Formlas

,. =utliers can be detected b4 e8amining the standardized residuals.

TRUE

A poor t implies a large t 0value (e.g.- larger than M% ould be an outlier).

AA!S*+ Anal"tic

*looms+ Remember Diclt"+  Eas"

/earning 0b1ecti2e+ 3- Identi5" nsal residals and )ig)-le2erage obser2ations.

Topic+ 8nsal 0bser2ations

3. n a simple regression- there are n 0 2 degrees of freedom associated

ith the error sum of s!uares (SSE). TRUE

"his is true in simple regression because e estimate to parameters ( β/ and β1).

AA!S*+ Anal"tic

*looms+ Remember Diclt"+  Eas"

/earning 0b1ecti2e+ 3-4< Interpret t)e standard error= R3= A>07A table= and F test.

Topic+ Anal"sis o5 7ariance+ 02erall Fit

6. n a simple regression- the F statistic is calculated b4 taking the ratio of MSR to the MSE.

TRUE

B4 denition- F calc * MSRMSE (obtained from the A<=>A table).

AA!S*+ Anal"tic

*looms+ 8nderstand Diclt"+ 3 Medim /earning 0b1ecti2e+ 3-4< Interpret t)e standard error= R3= A>07A table= and F test.

Topic+ Anal"sis o5 7ariance+ 02erall Fit

7. "he coe$cient of determination is the percentage of the total

variation in the response variable Y that is e8plained b4 the predictor X .

TRUE

R² * SSRLSST or R² * 1 0 SSELSST lies beteen / and 1 and often is e8pressed as a percent.

AA!S*+ Anal"tic

*looms+ 8nderstand Diclt"+ 3 Medim /earning 0b1ecti2e+ 3-4< Interpret t)e standard error= R3= A>07A table= and F test.

Topic+ 0rdinar" /east S:ares Formlas

+/. A dierent condence interval e8ists for the mean value of Y for each dierent value of X .

TRUE

Both the interval idth and also E(Y T X ) * β/ 5 β1 X depend on the value of X .

AA!S*+ Anal"tic

*looms+ Remember Diclt"+ 3 Medim /earning 0b1ecti2e+ 3-4? Distingis) bet@een con6dence and prediction inter2als 5or Y.

Topic+ !on6dence and &rediction Inter2als 5or Y

+1. A prediction interval for Y is idest hen X is near its mean.

FALSE

"he prediction interval is narro@est hen X is near its mean. Revie

the formula- hich has a term ( % i - )² in the numerator. "he minimum ould be hen % i  .

AA!S*+ Anal"tic

*looms+ Remember Diclt"+ 3 Medim /earning 0b1ecti2e+ 3-4? Distingis) bet@een con6dence and prediction inter2als 5or Y.

Topic+ !on6dence and &rediction Inter2als 5or Y

+2. n a to0tailed test for correlation at α * ./+- a sample correlation coe$cient ^r * /.2 ith ⁿ * 2+ is signicantl4 dierent than zero.

TRUE

t calc * r N(n 0 2)L(1 0 r ²)O^1L2 * (.2)N(2+ 0 2)L(1 0 .2²)O^1L2 * 2.217 Q t ./2+ * 2./,7 for d.5. * 2+ 0 2 * 2%.

AA!S*+ Anal"tic

*looms+ Appl"

Diclt"+ 3 Medim /earning 0b1ecti2e+ 3-4 !alclate and test a correlation coecient 5or signi6cance.

Topic+ 7isal Displa"s and !orrelation Anal"sis

+%. n correlation anal4sis- neither X nor Y is designated as the independent variable.

TRUE

n correlation anal4sis- X and Y covar4 ithout designating either as

?independent.?

AA!S*+ Anal"tic

*looms+ Remember Diclt"+  Eas"

/earning 0b1ecti2e+ 3-4 !alclate and test a correlation coecient 5or signi6cance.

Topic+ 7isal Displa"s and !orrelation Anal"sis

+. A negative value for the correlation coe$cient (r ) implies a negative value for the slope (b1).

TRUE

"he sign of r must be the same as the sign of the slope estimateb1.

AA!S*+ Anal"tic

*looms+ Remember Diclt"+ 3 Medim /earning 0b1ecti2e+ 3-49 Fit a simple regression on an E%cel scatter plot.

Topic+ 0rdinar" /east S:ares Formlas

++. @igh leverage for an observation indicates that X is far from its mean.

TRUE

B4 denition- observations have higher leverage hen X is far from its mean.

AA!S*+ Anal"tic

*looms+ Remember Diclt"+ 3 Medim /earning 0b1ecti2e+ 3- Identi5" nsal residals and )ig)-le2erage obser2ations.

Topic+ 8nsal 0bser2ations

+,. Autocorrelated errors are not usuall4 a concern for regression models using cross0sectional data.

TRUE

9e more often e8pect autocorrelated residuals in time series data.

AA!S*+ Anal"tic

*looms+ Remember Diclt"+  Eas"

/earning 0b1ecti2e+ 3-4 Test residals 5or 2iolations o5 regression assmptions.

Topic+ Residal Tests

+3.

+3. "her"here are e are usuallusuall4 seve4 several posral possible rsible regregressioession lines tn lines that ihat ill minll minimizimizee the sum of

the sum of s!uared erros!uared errors.rs.

FALSE FALSE

"he =JS solution for the estim

"he =JS solution for the estimatorsators bb// and and bb11 is uni!ue. is uni!ue.

/earning 0b1ecti2e+ 3-49 Fit a simple regression on an

/earning 0b1ecti2e+ 3-49 Fit a simple regression on an E%cel scatter plot.E%cel scatter plot.

Topic+ 0rdinar" /east S:ares Formlas Topic+ 0rdinar" /east S:ares Formlas

+6.

+6. 9hen 9hen the ethe errrrors iors in a rn a regregressioession moden model arl are not e not indepindependeendent- thnt- thee

In document Chap 012 (Page 32-91)