![headerH TRUE) 33 Corn <- factor(datal, 1]) 34 Yield <- datal, 21 35 Corn 36 table (Corn) 37 Yield 38 tapply(Yield, list (Corn) mean) group means 39 boxplot(Yield~datal,1]) 40 41 InsectSprays 42 table(InsectSprays) 43 Jungkook InsectSprays, 1] 44 Jungkook 45-Jin= InsectSprays [, 2] 46 Jin 47 boxplot (Jungkook-Jin) 48 49 pairwise.t.test(vield, Corn, pool.sd FALSE, p. adjust.method none 50 Insectsprays 51 does 52 ToothGrowth 53 factor (supp) 54 50:13 Clop Level Console Terminal x C/Users/Edward/Desktop/statistics lab/ > anova(1mlen factor (supp) + factor (dose), ToothGrowth)) Analysis of Variance Table Response: len Df Sum Sq Mean Sq F value Pr OF) factor(supp) 1 205.35 205.35 14.017 0.0004293 factor (dose) 2 2426.43 1213.22 82.811 < 2.2e-16 Residuals 56 820.43 14.65 signif. codes: 00.0010.010.050.1 > anova (1mClen factor (supp) + dose, ToothGrowth)) Analysis of Variance Table 1 ToothGrowth) Analysis of var iance Table? Response: len Df Sum Sq Mean q F value Pr(>F) 123.989 6. 314e-16 О.01.*. О.05 factor (supp) 1 205.35 205.35 11.447 0.001301 dose Residuals 57 1022.56 17.94 1 2224.30 2224.30 0.1/11 signif. codes : o does 0 . , 0.001 [11 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1. [19] 1.0 1.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 0.5 0.5 0.5 0.5 0.5 0. [13 9.23](http://img.homeworklib.com/questions/6fc1a5c0-748c-11ea-bccf-e71a4a771756.png?x-oss-process=image/resize,w_560)
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Using the built in data in R “ToothGrowth”. Why factor(does)
resulted in a different p-value using...
The R code will help to answer the question. 8. DeGroot&Shervish (2002) consider an experiment to...
The R code will help to answer
the question.
8. DeGroot&Shervish (2002) consider an experiment to study the combined effects of taking a stimulant and a tranquilizer. In this experiment three types of stimulant and four types of tranquilizer are administered to a group of rabbits. Each rabbit received one of the stimulants, then 20 minutes later, one of the tranquilizers. One hour later their response time (in microseconds) to a stimulus was measured. The results were: Tranquilizer Stimulant 1...
6. Consider the additive two way analysis of variance model where Σα:la.-012b1ßi = 0 and the...
6. Consider the additive two way analysis of variance model where Σα:la.-012b1ßi = 0 and the Eij are independent normal randonn vari- ables with zero means and variance ơ2.Let i-1 (a) Show that are unbiased estimators of the respective parameters. (b) (Devore, 1987) In an experiment to assess the effect of the angle of pull on the force required to separate electrical connectors, four different angles (factor A) were used and each of 5 connectors (factor B) were pulled once...
Using R output provided 1). Perform hypothesis testing for B(beta)1=2 using A(alpha)=0.05 > summary(ls) Call: Residuals:...
Using R output provided
1). Perform hypothesis testing for B(beta)1=2 using
A(alpha)=0.05
> summary(ls) Call: Residuals: Min 1Q Median 3Q Max 0.20283 -0.14691 -0.02255 0.06655 0.44541 Coefficients: (Intercept) 0.365100.099043.686 0.003586 ** Signif. codes: 0 '***' 0.001 '0.01 '*'0.05 '.' 0.1''1 Estimate Std. Error t value Pr>Itl) 0.96683 0.18292 5.286 0.000258** Residual standard error: 0.1932 on 11 degrees of freedom Multiple R-squared 0.7175, Adjusted R-squared: 0.6918 F-statistic: 27.94 on 1 and 11 DF, p-value: 0.0002581 anovaCLs) Analysis of Variance Table Response:...
(a) Using the above t-test data to determine whether or not there is a linear relationship...
(a) Using the above t-test data to determine whether or not there
is a linear relationship between the two variables.
(b) Using the above ANOVA F-test data to determine whether or not
there is a linear relationship between the two variables.
(c) How do the results in (a) compare to those in (b)?
We were unable to transcribe this imageAnalysis of Variance Table Response: DatSGPA Dat $ACT 1 3. 588 3. 5878 9. 2402 0.002917 Df Sum Sq Mean sq...
> summaryCls) Call: Lm(formula y X) Residuals: -0.20283 -0.146910.02255 0.06655 0.44541 Coefficients: (Intercept) 0.36510 0.09904 3.686...
> summaryCls) Call: Lm(formula y X) Residuals: -0.20283 -0.146910.02255 0.06655 0.44541 Coefficients: (Intercept) 0.36510 0.09904 3.686 0.003586 ** Min 1Q Median 3Q Max Estimate Std. Error t value Pr(>ltl) 0.96683 0.18292 5.286 0.000258*** Signif. codes: 00.001*0.010.050.11 Residual standard error: 0.1932 on 11 degrees of freedom Multiple R-squared 0.7175, Adjusted R-squared: 0.6918 F-statistic: 27.94 on 1 and 11 DF, p-value: 0.0002581 > anovaCls) Analysis of Variance Table Response : y Df Sum Sq Mean Sq F value PrOF) 1 1.04275 1.04275...
write answer step by step on the paper What would you expect to see if you...
write answer step by step on the paper
What would you expect to see if you were to run the following code? Please describe th analysis and the result briefly. (20 pts) 5- > myANOVA <- aov(Learning" Group Condition) >summary(myANOVA) Group Condition Group:Condition Residuals Df Sum Sq 1.8454 1 0.1591 0.3164 59 1.3325 Mean Sq 1.84537 0.15910 0.31640 0.02258 F value 81.7106 7.0448 14.0100 Pr(>F) 9.822e-13** 0.0102017 0.000414*** Signif. codes:0.001*0.01' 0.05'0.1'"'1 > boxplot(Learning"Group"condition,col:c("#ffdddd","#ddddff"))
R code explain please. Please explain what “factor”, “tapply”, “residuals” means. Please explain the code in...
R
code explain please.
Please explain what “factor”, “tapply”, “residuals”
means.
Please explain the code in the photo precisely.
MAST20005/MAST90058: Week 9 Lab Goals: (i) Analysis of variance, (i) Study the distribution of the test statistics and p-values through simulations; (ii) Compare tests by simulating power curves. Data for Section 1: Corn data (corn.txt). The yield obtained, in bushels per acre, of 4 test plots for each of 4 different varieties of corn. The data file can be obtained from...
Analysis of Variance Table Response: Price Df Sum Sq Mean Sq F value Pr(>F) Living.Area 1 1.3501e+12 1.3501e+12 362.0394 < 2e-16 *** Bedrooms 1 2.3642e+10 2.3642e+10 6.3394 0.01241 * Fireplaces...
Analysis of Variance Table Response: Price Df Sum Sq Mean Sq F value Pr(>F) Living.Area 1 1.3501e+12 1.3501e+12 362.0394 < 2e-16 *** Bedrooms 1 2.3642e+10 2.3642e+10 6.3394 0.01241 * Fireplaces 1 7.6232e+07 7.6232e+07 0.0204 0.88642 Residuals 259 9.6588e+11 3.7293e+09 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > Using= 0.05, perform an F test of overall linear relationship. State the hypotheses, the value of F-test statistic, p-value, and your conclusion.
Suppose we are working on the same data set. Two multiple linear regressions have been done...
Suppose we are working on the same data set. Two multiple linear regressions have been done with following Printout, where “ “ means blank space. Analysis of Variance Table 1 Response: y Df Sum Sq Mean Sq F value Pr(>F) x1 1 --------- ---------- -------- ------------- *** x2 1 80.206 80.206 17.892 0.0001474 *** Residuals 37 165.864 4.483 Analysis of Variance Table 2 Response: y Df Sum Sq Mean Sq F...
What are the implications of predictability results in Part 2 and 3 for investment decisions? Part...
What are the implications of predictability results in Part 2 and 3 for investment decisions? Part 2 use log dividend-price ratio to predict the 5-year stock market excess log returns: lm(formula = lnexret[2:t] ~ dp[1:t - 1]) Residuals: Min 1Q Median 3Q Max -0.54389 -0.07305 0.01977 0.10712 0.34107 Coefficients: Estimate Std. Error t value (Intercept) 0.58469 0.12768 4.579 dp[1:t - 1] 0.13510 0.03771 3.582 Pr(>|t|) (Intercept) 1.58e-05 *** dp[1:t - 1] 0.000567 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’...
The R code will help to answer
the question.
8. DeGroot&Shervish (2002) consider an experiment to study the combined effects of taking a stimulant and a tranquilizer. In this experiment three types of stimulant and four types of tranquilizer are administered to a group of rabbits. Each rabbit received one of the stimulants, then 20 minutes later, one of the tranquilizers. One hour later their response time (in microseconds) to a stimulus was measured. The results were: Tranquilizer Stimulant 1...
6. Consider the additive two way analysis of variance model where Σα:la.-012b1ßi = 0 and the Eij are independent normal randonn vari- ables with zero means and variance ơ2.Let i-1 (a) Show that are unbiased estimators of the respective parameters. (b) (Devore, 1987) In an experiment to assess the effect of the angle of pull on the force required to separate electrical connectors, four different angles (factor A) were used and each of 5 connectors (factor B) were pulled once...
Using R output provided
1). Perform hypothesis testing for B(beta)1=2 using
A(alpha)=0.05
> summary(ls) Call: Residuals: Min 1Q Median 3Q Max 0.20283 -0.14691 -0.02255 0.06655 0.44541 Coefficients: (Intercept) 0.365100.099043.686 0.003586 ** Signif. codes: 0 '***' 0.001 '0.01 '*'0.05 '.' 0.1''1 Estimate Std. Error t value Pr>Itl) 0.96683 0.18292 5.286 0.000258** Residual standard error: 0.1932 on 11 degrees of freedom Multiple R-squared 0.7175, Adjusted R-squared: 0.6918 F-statistic: 27.94 on 1 and 11 DF, p-value: 0.0002581 anovaCLs) Analysis of Variance Table Response:...
(a) Using the above t-test data to determine whether or not there
is a linear relationship between the two variables.
(b) Using the above ANOVA F-test data to determine whether or not
there is a linear relationship between the two variables.
(c) How do the results in (a) compare to those in (b)?
We were unable to transcribe this imageAnalysis of Variance Table Response: DatSGPA Dat $ACT 1 3. 588 3. 5878 9. 2402 0.002917 Df Sum Sq Mean sq...
> summaryCls) Call: Lm(formula y X) Residuals: -0.20283 -0.146910.02255 0.06655 0.44541 Coefficients: (Intercept) 0.36510 0.09904 3.686 0.003586 ** Min 1Q Median 3Q Max Estimate Std. Error t value Pr(>ltl) 0.96683 0.18292 5.286 0.000258*** Signif. codes: 00.001*0.010.050.11 Residual standard error: 0.1932 on 11 degrees of freedom Multiple R-squared 0.7175, Adjusted R-squared: 0.6918 F-statistic: 27.94 on 1 and 11 DF, p-value: 0.0002581 > anovaCls) Analysis of Variance Table Response : y Df Sum Sq Mean Sq F value PrOF) 1 1.04275 1.04275...
write answer step by step on the paper
What would you expect to see if you were to run the following code? Please describe th analysis and the result briefly. (20 pts) 5- > myANOVA <- aov(Learning" Group Condition) >summary(myANOVA) Group Condition Group:Condition Residuals Df Sum Sq 1.8454 1 0.1591 0.3164 59 1.3325 Mean Sq 1.84537 0.15910 0.31640 0.02258 F value 81.7106 7.0448 14.0100 Pr(>F) 9.822e-13** 0.0102017 0.000414*** Signif. codes:0.001*0.01' 0.05'0.1'"'1 > boxplot(Learning"Group"condition,col:c("#ffdddd","#ddddff"))
R
code explain please.
Please explain what “factor”, “tapply”, “residuals”
means.
Please explain the code in the photo precisely.
MAST20005/MAST90058: Week 9 Lab Goals: (i) Analysis of variance, (i) Study the distribution of the test statistics and p-values through simulations; (ii) Compare tests by simulating power curves. Data for Section 1: Corn data (corn.txt). The yield obtained, in bushels per acre, of 4 test plots for each of 4 different varieties of corn. The data file can be obtained from...
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