6. Consider the additive two way analysis of variance model where Σα:la.-012b1ßi = 0 and the...
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...
> 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...
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.
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:...
Using the built in data in R “ToothGrowth”. Why factor(does) resulted in a different p-value using anova function. 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...
(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...
6. A supermarket chain in the US studied the relationship between apple sales and the price at which apples are offered. Three price levels were studied: (1) the chief competitor's price, (2) a price slightly higher than the chief competitor's price, and (3) a price moderately higher than the chief competitor's price. Twenty-four shops of comparable size were selected for the study and eight shops were randomly assigned to each price level. Data on shop sales (in thousands) of apples...
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"))
A company manager is interested in analyzing the relationship between years of working experience and the salary of their employees. He has collected the data from 30 employees of their years of experience and the salary. Below provided is a partial regression output from R. Use the provided information to answer below questions Coefficients: (Intercept) YearsExperience Estimate Std. Error t value Pr(>ltl) 25792.2 2273.1 9450.0 --- Signif. codes: O '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1'' 1 Analysis of...
2. 2. After we fit the model, the R commander output is provided below. Coefficients: (Intercept) -5.128e+03 1.103e+02 46.49 2e-16** Estimate std. Brror t value Pr(lt|) TEMP PERT TEM: FERT 1.45se-01 9.692e-03 -15.01 1.06e-12 3.110e+01 1.344e+00 23.13 2e-16* 1.397e+02 3.140e+00 44.51 < 2e-16** TEMPSQ FERTSO -1.334e-01 6.853e-03 19.46 6.46e-15 -1.144e+00 2.741e-02 41.74 <2e-16 signif. codes: 00.001 0.01 0.05 011 Residual standard error: 1.679 on 21 degrees of freedom Multiple R-squared: 0.993, F-statistic: 596.3 on 5 and 21 DF, p-value: 2.2e-16...