Week 9 Sklearn Fish

IConsider the following multiple linear regression model and the Excel print out of its regression results: Beer = Bo + BIEDUC + B2AGE + BAGE? + BAGENDER + BERACE+ BEGENDER*RACE+ E, where Beer is monthly beer consumption (ounces), EDUC is years of education. We have 2 qualitative variables: gender and race. Gender takes 2 values, GEN=1 if the person is male and GEN=0 for females. The variable race also takes 2 values, RACE=1 if the person is white and RACE=0 if the person is not white. SUMMARY OUTPUT Regression Statistics R Square Adjusted R Square Standard Error Observations ??? 0.4684 ??? 40 ANOVA df MS Regression Residual ??? 319.3 64.8 ??? ??? ??? 8.43 Total ??? 597.5 Coefficients Standard Error Intercept -150.254 107.397 EDUC -16.7755 8.4579 75.45905 -1.72456 AGE 37.3261 AGE 0.5397 GEN 238.9424 81.6054 RACE 123.7404 103.1804 GEN. RACE 76.4308 51.0670 a. Calculate the missing numbers (???). b.Interpret the parameter of RACE (123.74). c. Is the parameter of RACE (Bs) significant?…

A researcher conducts a multiple regression with Y as the dependent variable and X1, X2, X3 and X4 as explanatory variables. Using the regression output below, fully describe this model and discuss important parts of the output. What is the predicted value of Y if X1 = 3, X2 = 15, X3 = 7 and X4 = 0.003? %3D SUMMARY OUTPUT Regression Staistics Muliple R R Square Adjusted R Square Standard Emor Observations 0.7236 0.5236 0.5159 5.3928 252 ANOVA Significance F 1. 10662E-38 SS MS Regression Residual 1973 9392 29.0820 67.8749 7895.7567 7183.2599 4 247 Total 251 15079.0166 Upper 95% 33.4049 Coefficients Standard Eror t Stat Pvalue 2.2273 0.026830873 Lower 95% 7.9594 2.0508 Intercept X1 17.7278 1.5583 0.2750 5.6662 4.05265E-08 1.0166 2.0999 X2 1.8376 0.1997 9.1999 1.4442 -74708 -3721 4324 1.55861E-17 2.2310 X3 55100 -5.5348 7.94036E-08 -3.5492 X4 -3.1079 1887 8435 -0.0016 0.998687788 3715 2166

We have data on Lung Capacity of persons and we wish to build a multiple linear regression model that predicts Lung Capacity based on the predictors Age and Smoking Status. Age is a numeric variable whereas Smoke is a categorical variable (0 if non-smoker, 1 if smoker). Here is the partial result from STATISTICA. b* Std.Err. of b* Std.Err. N=725 of b Intercept Age Smoke 0.835543 -0.075120 1.085725 0.555396 0.182989 0.014378 0.021631 0.021631 -0.648588 0.186761 Which of the following statements is absolutely false? A. The expected lung capacity of a smoker is expected to be 0.648588 lower than that of a non-smoker. B. The predictor variables Age and Smoker both contribute significantly to the model. C. For every one year that a person gets older, the lung capacity is expected to increase by 0.555396 units, holding smoker status constant. D. For every one unit increase in smoker status, lung capacity is expected to decrease by 0.648588 units, holding age constant.

Independent variable data is listed in cells B2 through B100, and dependent variable data is in cells C2 through C100. Which spreadsheet function would calculate the slope of a linear regression model of this data? Group of answer choices =SLOPE(B2:B100,C2:C100) =SLOPE(C2:C100,B2:B100) =SLOPE(B2,C2) =SLOPE(C2,C100,B2,B100)

A group of Maternal and Child Health public health practitioners are interested in the relationship between bacterial vaginosis (BV) and a number of negative 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 BV and depression, controlling for maternal age. The following is a computerized output displaying the results of their analysis.Parameter Intercept Maternal Age DepressionEstimate StandardError tValue Pr>|t|0.2186206635 -.0046496845 0.19124124150.06635040 0.00221338 0.031518843.29 0.0010 -2.10 0.0360 6.07 <.0001 A) What are the dependent and independent variables in this investigation?B) Based on the information above, was the research team justified in controlling for maternal age in this population? Why or why not?C) Write out the model in symbols. Round to 3 decimal places.D) Is there a significant association between BV and depression?

Corvette, Ferrari, and Jaguar produced a variety of classic cars that continue to increase in value. The data showing the rarity rating (1–20) and the high price ($1000s) for 15 classic cars is contained in the Excel Online file below. Construct a spreadsheet to answer the following questions.     Open spreadsheet   Develop a scatter diagram of the data using the rarity rating as the independent variable and price as the dependent variable. Does a simple linear regression model appear to be appropriate?   A simple linear regression model _________appearsdoes not appear to be appropriate. Develop an estimated multiple regression equation with  rarity rating and  as the two independent variables.        (to whole numbers) What is the value of the coefficient of determination? Note: report  between 0 and 1.  (to 3 decimals) What is the value of the  test statistic?  (to 2 decimals) What is the -value?  (to 4 decimals) Consider the nonlinear relationship shown by equation .…

A residual plot is a plot in which the residuals are plot ted against the value of the explanatory variable x. When a residual plot exabits a noticeable pattern, the variables do not have a linear relationship, and the least square regression line should not be used. When a residual plot exabits no noticeable pattern, the least square regression line may be used to describe the relationship between the variables. For each of the following residual plots, determine whether a linear model is appropriate. a. Residual 0.5 2.0 1.5 1.0 0505 0 -0.5 -1.0 -1.5 -2.0 -2 The residual plot in (a) [Select] square regression line [Select] b. Residual 19 15 10 5 0 -5 -10- -15 -10 10 15 and thus the least- The residual plot in (b) does not exabit a noticeable pattern and thus the least -square regression line regression line may be used

Suppose a multiple regression model is fitted into a variable called model. Which Python method below returns residuals for a data set based on a multiple regression model? Select one. model.residualsvalues O model.residvalues model.residuals model.resid

The residual plot for a linear regression model is shown below. Assess the fit of the linear model, and justify your answer.   The line is a good fit because the points on the residual plot have a clear pattern. The line is a good fit because the points on the residual plot do not have any noticeable pattern. The line is not a good fit because the points on the residual plot do not have any noticeable pattern. The line is not a good fit because the points on the residual plot have a clear pattern.

Pls help ASAP. Pls show all work.

Plzz explain..

Tire pressure (psi) and mileage (mpg) were recorded for a random sample of seven cars of thesame make and model. The extended data table (left) and fit model report (right) are based on aquadratic model   What is the predicted average mileage at tire pressure x = 31?

B and c