unstandardized beta
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7) If F (2,344) = 340.2, p < .001, then what is this saying in general about the regression model? (see p. 217)
Why should you be cautious in using unstandardized beta? (p. 218)
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- Please finish the R code to conduct the hypothesis testing. I need the code for the p-value for the F test, tstat, and the p value for tstat. Write the conclusion to the hypothesis testing once completed. # Example 11.1# simple linear regression# H0: B=0 (there is no linear correlation)# H1: B≠0 (there is a linear correlation)data <- data.frame(estriol=c(7,9,9,12,14,16,16,14,16,16,17,19,21,24,15,16,17,25,27,15,15,15,16,19,18,17,18,20,22,25,24),birthweight=c(25,25,25,27,27,27,24,30,30,31,30,31,30,28,32,32,32,32,34,34,34,35,35,34,35,36,37,38,40,39,43))linear <- lm(birthweight~estriol, data = data)summary(linear)# This gives the results for a and b and the F statistic. # Call:# lm(formula = birthweight ~ estriol, data = data)# # Residuals:# Min 1Q Median 3Q Max # -8.1200 -2.0381 -0.0381 3.3537 6.8800 # # Coefficients:# Estimate Std. Error t value Pr(>|t|) # (Intercept) 21.5234 2.6204 8.214 4.68e-09 ***# estriol 0.6082 0.1468 4.143…Fit a regression line to the data shown in the chart, and find the coefficient of correlation for the line. Use the regression line to predict life expectancy in the year 2020, where x is the number e decades after 1900. 2 (1920) life expectancy, y 48.3 years 50.6 years 52.2 years 53.4 years 54.4 years year, x 0 (1900) 4 (1940) 6 (1960) 8 (1980) Choose the regression line. A. y = 0.750x + 48.78 O B. y = 0.750x - 48.78 O C. y = 48.78x + 0.750 O D. y = 48.78 The coefficient of correlation rounded to three decimal places is 0.985. Is the regression line a good fit? * Yes No The life expectancy in the year 2020, rounded to one decimal place isSuppose you wanted to estimate the effect of being educated on being in the labor force. You estimate a model with two variables, LF is a binary variable = 1 if the person is participating in the labor force and the variable educ measures the number of years of education a person has received. You get the following estimated regression: LF = 0.1 + 0.15educ Which of the following is the correct interpretation about the effect of an additional year of education? O A 1% increase in education increases the number of those participating in the labor force by 15 percentage points. A additional year in education increases the number of those participating in the labor force by 0.15 percentage points A additional year in education increases the number of those participating in the labor force by 0.15% An additional year of education increases the probability of participating in the labor force by 15 percentage points. An additional year of education increases the probability of participating…
- You run a regression analysis on a bivariate set of data (n = 110). You obtain the regression equation y = – 3.546x 4.393 0.248 (which is significant at a = 0.01). You want to predict with a correlation coefficient of r = - what value (on average) for the explanatory variable will give you a value of 100 on the response variable. What is the predicted explanatory value? X = (Report answer accurate to one decimal place.)ABC University uses data from a random sample of students in a regression analysis to see i he number of hours a student spends playing video games weekly (x) is a predictor of the student's GPA (y) at the end of the term. An intern for ABC goes ahead and performs the regression using Excel. Examine the following Excel regression printout. Describe what the value -0.0292...in the cell next to "X Variable 1" label means. SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square 0.709640286 0.503589336 0.45394827 Standard Error 0.552383099 Observations 12 ANOVA df SS MS Regression 3.095395785 3.09539579 28 72.28745 Residual 10 3.051270881 0.3051270937 Total 11 6.146666667 Coefficients Standard Error t Stat P-value Interce pt X Variable 1 3.46198431 0.296274535 11.6850552 11 1.94E-10 -0.029275496 0.009191503 -3.1850607 37 6.88E-06 Regression should not be used on the data set. The correlation coefficient is -0.0292 when rounded to 3 places. The regression line has a…You run a regression analysis on a bivariate set of data (n = 53). You obtain the regression equation y = 1.478x 22.799 with a correlation coefficient of = 0.783 (which is significant at a = 0.01). You want to predict what value (on average) for the explanatory variable will give you a value of 130 on the response variable. What is the predicted explanatory value? X = (Report answer accurate to one decimal place.)
- In the following case find the best predicted value of y given the following: x = 2.00, r = -0.123 (p-value = 0.62), y (bar)= 8.00, n = 30 and the equation of the regression line is: y (hat) = 7.00 - 2.00xIn the following case find the best predicted value of y given the following: x = 3.00, r = 0.052 (p-value = 0.85), y (bar)= 5.00, n = 20 and the equation of the regression line is: y (hat) = 6.00 + 4.00x10.2.19-T Question Help ▼ Find the regression equation, letting the first variable be the predictor (x) variable. Using the listed lemon/crash data, where lemon imports are in metric tons and the fatality rates are per 100,000 people, find the best predicted crash fatality rate for a year in which there are 475 metric tons of lemon imports. Is the prediction worthwhile? Lemon Imports Crash Fatality Rate 227 262 363 499 15.4 518 16 15.7 15.4 15 Find the equation of the regression line. (Round the constant three decimal places as needed. Round the coefficient to six decimal places as needed.)
- The Pearson correlation between X and Y is r = 0.40. When a second variable, X₂, is added to the regression equation, we obtain R2 = 0.64. How much variance for the Y scores is predicted by using both X and X₂ as predictor variables? O 0.40 or 40% O 0.64 or 64% O 0.48 or 48% O 0.16 or 16%A researcher investigates the relationship between cigarette smoking (X) and work absences (Y). The number of cigarettes smoked daily and the number of days absent from work due to illness are collected for N= 12 employees. The preliminary results are below: What is the regression equation? How many absences (Y) would expect someone who smokes X = 10 cigarettes a day to have?When testing for bl in a regression model, the null hypothesis (2-tailed) is b1=0. Why? O Then the alternative hypothesis can be b0=1 This indicates that bl=b2 This indicates that bl=b0 A change in x does not result in a change in y
