Fit these three regression models and then discuss the similarities and differences between them, particularly as relates to slope estimates (use CI’s) and R2. Also address why this is a “special case” and we wouldn’t necessarily expect to see these model characteristics for a typical dataset. a) Additive model including both predictors (output attached) b) Model including only Moisture (output attached) c) Model including only Sweetness BrandLiking = 68.62 + 4.38 Sweetness Term                     95% CI                     P-Value Constant          (50.16, 87.09)                 0.000 Sweetness       (-1.46, 10.21)                   0.130 S                            R-sq                     R-sq(adj) 10.8915                15.57%                    9.54%

The Conde Nast Traveler Gold List  for 2012 provided rating for the top 20 small cruise ships. The data from annual Readers’ Choice Survey are the overall scores(Y) each ship received based on several criteria, including Itineraries/Schedule (X1), Shore Excursions(X2), and   Food/Dinning(X3). The estimated regression equation to predict the overall scores is Y= 35.6184+0.1105 X1+0.2445 X2+0.2474 X3. Part of the regression results is shown below.                                                            Coefficients       Standard Error Intercept                                           35.6184            13.2308 Itineraries/Schedule(X1)                0.1105               0.1297 Shore Excursions(X2)                      0.2445               0.0434 Food/Dinning(X3)                            0.2474               0.0621     Use the T test to determine whether or not the coefficient of X1 is significant. Use Level of significance=.05? Be sure to state null and alternative hypotheses.…

Acrylamide is a chemical that is sometimes found in cooked starchy foods and which is thought to increase the risk of certain kinds of cancer. The paper "A Statistical Regression Model for the Estimation of Acrylamide Concentrations in French Fries for Excess Lifetime Cancer Risk Assessment"+ describes a study to investigate the effect of frying time (in seconds) and acrylamide concentration (in micrograms per kilogram) in french fries. The data in the accompanying table are approximate values read from a graph that appeared in the paper. Frying Acrylamide Time Concentration 150 240 240 270 300 300 150 125 + 195 185 135 275 USE SALT (a) Find the equation of the least-squares line for predicting acrylamide concentration using frying time. (Round your answers to four decimal places.) ŷ = (b) Does the equation of the least-squares line support the conclusion that longer frying times tend to be paired with higher acrylamide concentrations? Explain. O No, the least squares regression line…

A researcher plans to study the causal effect of police on crime using data from a random sample of U.K. counties. He plans to regress the county’s crime rate on the (per capita) size of the county’s police force. Explain why this regression is likely to suffer from omitted variable bias. Which variable would you add to the regression to controlfor important omitted variable? Determine whether the regression will likely over or underestimate the effect of police on the crime rate?

I recently asked this question and was wondering if you could show me how to do it, not just putting it in a caculator or excel but written out equations and seeing my information plugged in so I can understand. Thank you.   Calculate the simple linear regression to determine if the amount of time spent on homework can be predicted by amount of sleep. Graph the relationship and determine, numerically, if there are any outliers. Interpret all results in a paragraph citing the appropriate statisitcs.

Show the best fitted line on scatter diagram and Find the predicted value for each y using the exposure time and the equation obtained in part b (b. Find the equation of regression line between radiation doses on exposure time .usingleast square method)

Provide an example of a regression that arguably would have a high value of R² but would produce biased and inconsistent estimators of a causal effect. Explain why the R² is likely to be high. Explain why the OLS estimators would be biased and inconsistent.

A group of Maternal and Child Health public health practitioners are interested in the relationship between depression and a number of health outcomes. Suppose the research team gathers information on a group of participants, and constructs a multiple linear regression model looking at the relationship between depression and household income dichotomized as above and below the federal poverty line controlling for a number of potential confounders. The following is a computerized output displaying the results of their analysis.   Parameter Estimate Standard Error t Value Pr > |t| Intercept 0.2617346843 0.09209917 2.84 0.0046 Income (1/0) -.1962038300 0.04574793 -4.29 <.0001 Race (W or AA) -.0320329506 0.03900447 -0.82 0.4118 bmicontinuous 0.0051185980 0.00216986 2.36 0.0186 Alcohol (Y/N) -.0088735044 0.03090631 -0.29 0.7741   A) What are the independent and dependent variables? B) Which potential…

This is my question! All parts please.