Suppose we have:a response variable Y andan explanatory factor X with  four levels.We ran the following code in R:mymodel <- lm(Y ~ X)What is the correct R code to test the null hypothesis that the expected value of Y does not depend on the level of X.Group of answer choicesplot(mymodel)anova(submodel,mymodel)t.test(mymodel)summary(mymodel)$coefanova(mymodel)

Suppose we have:a response variable Y andan explanatory factor X with four levels.We ran the following code in R:mymodel <- lm(Y ~ X)Which of the following statements is FALSE.Group of answer choicesThe t value column of summary(mymodel) gives the t-statistic to test the null hypothesis that the true value of the regression coefficient is zero.By default, the estimated value of the intercept is the fitted value for the first level of X.The value of the fourth regression coefficient in the summary(mymodel) output is the estimated difference between the expected values of Y at level 4 and level 3 of X.mymodel has four regression coefficients.The Pr(>|t|) values in summary(mymodel) are the p-values for the null hypothesis that the true value of the regression coefficient is zero.

A null hypothesis with a one-sample test can be formulated with a <, > or ≠Group of answer choicesTrueFalse

If we reject a null hypothesis at the 0.05 level of significance, then we must also reject it at the 0.10 level.Group of answer choicesTrueFalse

There is a need to test a hypothesis.Group of answer choicesTrueFalse

A researcher administers a treatment to a sample of participants selected from a population with µ = 80. If a hypothesis test is used to evaluate the effect of the treatment, which combination of factors is most likely to result in rejecting the null hypothesis?​Group of answer choices​A sample mean much different than 80 with α = .05​A sample mean near 80 with α = .05​A sample mean near 80 with α = .01​A sample mean much different than 80 with α = .01