A simple linear regression model was fitted to two variables: Y, the response variable and X, the explanatory variable.A scatter plot of Y versus X as well as the residual plot after fitting the least squares regression line to the data are given below.Which ONE of the following statements regarding the assumptions for this simple linear regression is FALSE?Group of answer choicesThere are two outliers from the general trend.TThe observation with the largest value of x is likely to have a large effect on the position of the fitted line.Two of the observations have unusual y values given their x values.Two of the observations are outliers in x.There are doubts about the Normality of the errors due to the presence of one or more outliers.

Which of the following statements about a linear regression model are correct?i) It is possible that a high R-square can result from outliersii) It is always true that the high the R-square, the better the linear regression model iii) Scatterplot can be helpful in identifying the existence of outliers in fitting a regression modeliv) Regression model cannot produce a range of predictions. v) A p-value higher than 0.5 suggests the estimated coefficient is statistically significant. Question 2Answera.i) and v)b.None of the abovec.i) and iii)d.iv) and v)e.i), ii) and iii)

Suppose you have a residuals plot that shows a funnel shape for the residuals. Which assumption of linear regression is being violated?1 pointthe linearity assumptionthe independence assumptionboth the linearity and the independence assumptionsthe equal variance assumptionboth the linearity and the equal variance assumptions

Which assumption of linear regression is violated if the residuals exhibit a pattern when plotted against the predicted values?

A regression model was fitted in R with the following call:lm(y ~ x + I(x^2))Assume that all the modelling assumptions are satisfied. Which of the statements below about this model is FALSE?Group of answer choicesThis model has THREE coefficients that need to be estimated.It would be valid to make a prediction for a value outside the range of observed x.We cannot make a statement about the effect of a 1-unit change of x, on the average value of y.The model fitted contains a quadratic term.

Which assumption is NOT made by Linear Regression?Answer areaLinearity of relationshipsIndependence of errorsHomoscedasticityData follows a normal distribution