A researcher is investigating possible explanations for deaths in traffic accidents. He examined data from 2000 for each of the 52 cities randomly selected in the US. The data included information on the following variables: Deaths: The number of deaths in traffic accidents per city and Income: The median income per city As part of his study, he ran the following simple linear regression model as pictured :  Question:  Based on the above results, the researcher tested the hypotheses ( Null: B1=0 versus Alternative: B1 not equal to 0) using T test. What do we know about the test statistic of the test, what is the approximate p-value, and value of Rsquared? And based on your result, what is your conclusion? Show your work for full credit.

In this question, we investigate the relationship between the top (maximum) speed (mph) and maximum height for a random sample of roller coasters in the United States. Here is the scatterplot and a summary of the simple linear regression model.   Construct a 95% confidence interval for the true slope of the regression line and interpret your confidence interval in context.

Body Fat. Where we considered the regression of percentage of body fat on nine body measurements: height, weight, hip, forearm, neck, wrist, triceps, scapula, and sup. Use the technology of your choice to do the following. a. Use the maximum- R2 criterion to obtain a regression equation for these data. b. Use the adjusted- R2 criterion to obtain a regression equation for these data. c. Use the Mallows’ Cp criterion to obtain a regression equation for these data. d. Are the regression equations obtained in parts (a), (b), and (c) the same? e. Use the analysis in parts (a), (b), and (c) to select a regression equation for these data. For the regression equation that you selected, obtain plots of residuals versus fitted values, residuals versus the included predictor variables, and a normal probability plot of the residuals. Perform a residual analysis to assess the appropriateness of the regression equation, constancy of the conditional standard deviations, and normality of the…

You want to assess the differences in frequency counts for four different groups. You know there are 150 people total. Without any additional information about how the data is distributed, what would you use for the "expected frequency" in each group?     a.1/4 b.37.5 c.25 d..375

An econometric model is a multiple linear regression model if  Question 10Select one: a. it explains the average value of y as a  linear function of several explanatory variables  b. it explains the sample mean of y as a function, linear in the parameters , of several x  c. it explains y as a linear function of several x , the explanatory variables  d. it explains the average of y as a function of several x  e. none of the answers is correct

You are given the following data, where X1X1 (final percentage in science class) and X2X2 (number of absences) are used to predict YY (standardized science test score in third grade): X1X1 X2X2 YY 95 2 415 82 0 400 92 6 375 72 1 410 75 4 370 80 1 390 78 3 350 70 3 345 65 7 300 70 3 350 88 3 375 89 1 450 Determine the following multiple regression values.Report intercept and slopes for regression equation accurate to at least 2 decimal places:    Intercept:  b0=b0=     Partial slope X1X1:  b1=b1=     Partial slope X2X2:  b2=b2= Report the coefficient of multiple determination for the model (NOT the adjusted R2R2) and the sum of squares total accurate to at least 2 decimal places:    R2=R2=     SSTotal=SSTotal= Test the significance of the overall regression model.     F-test statistic =     P-value = Report the Mean Squares residuals accurate to 2 decimal places:    Report the test statistics for the regression coefficients accurate to 2 decimal places:…

Use the following table and information to answer questions 21 and 22: A company wants to build anew site with the number of bathrooms commensurate with the number of employees who will workthere. The analytics team decides to analyze a linear model of these data from extant sites to inform thisdecision before construction. Number of employees 650 730 810 900 102 107 1150 Number of bathrooms 40 50 54 61 82 110 121 21. The linear regression equation is:a. y-hat = 79.96 + 0.0094xb. y-hat = 0.0094 – 79.96xc. y-hat = 0.0094 – 79.96xd. y-hat = 79.96 – 0.0094x22. Predict how many bathrooms a new site with 1150 employees should have.a. 91b. 70c. 121d. The linear regression equation should not be used to make predictions.