Based on the residual plot below, which of the following statements is TRUE? Residual Plot The pattern in the residual plot above indicates that we do not have an issue with Heteroskedasticity. The pattern in the residual plot above indicates that we do have an issue with Heteroskedasticity. | The pattern in the residual plot above indicates that we do have an issue with Endogeneity or Omitted variables. | The pattern in the residual plot above indicates that we do have an issue with Multicollinearity. | The pattern in the residual plot above indicates that we do not have an issue with Multicollinearity. e- residual

Could you help me with these 2 please

(d) Perfect negative linear.corelation 60--1 (g) No linear correlation (linearly uncorrelated) Sep 5-3:11 PM Remember the stork example? Correlation does not always mean causation! Two variables may be strongly correlated because they are both variables, called lurking or confounding variables. June 15, 2020-- Research shows bald men are more llkely to die after they get the coronavirus than their hairier counterparts Since the vtus emerged in China, researchers have noticed a higher incidence of death Telegraph in England. Jn 15 2020 -age bald men reported The -ma >Coronavirus ticws Pta wwe.datymat.co.uknews/articie-854804. Coronavina-study-daims.nomt The Real Cause of Polio! Polio Rates / Ice Cream Sales 1949 0000 ke Creamm Nonth In the late 1940s, before there was a polio vaccine, public health experts In America noted that polio cases increased in step with the consumption of ice cream and soft drinks, Eliminating such treats was even recommended as part of an anti-polio diet. It…

Im regressing a model with an endogenous variable as an independent variable, and using the Multiple Indicator approach. Whats assumptions needed for the Betas to be “identified”, and separately the Beta estimates to be “consistent”? What do we mean by “identified” and “consistent”?

Current Attempt in Progress State the null and alternative hypotheses for the situation described below for a statistical test. Testing to see if there is evidence that the proportion of people who smoke is greater for males than for females. Let group 1 be the males and let group 2 be the females. Ho: = < vs Ha: 41 :: p 2 :: p ^ P₁ P1 ^ :: P2 P2 p I ₁ :: I₂

Why is it that my standard residuals appear to be non-linear yet the rest of my results appear linear? What does this imply in context to the topic? (strengths dependency on weight). If there is something wrong with my R script please let me know. Or if there is a reason as to why this has happened.