The following table shows the number, in millions, of women employed outside the home in the given year. Number in millions 16.11 Year 1942 1943 18.70 1944 19.17 1945 19.03 1946 16.78 (a) Use regression to find a quadratic model for the data. (Let t be the number - years since 1942. Round the regression parameters to three decimal places.) N = (b) Express using functional notation the number of women working outside the home in 1947, and then estimate that value. (Round your answer to two decimal places.) million )=[ (c) The actual number of women working outside the home in 1947 was 16.90 million, whereas in 1948 the number was 17.58 million. In light of this, is a quadratic model appropriate for the period from 1942 through 1948? Yes O No

The following table shows the number, in millions, of women employed outside the home in the given year. Number in millions 16.11 Year 1942 1943 18.70 1944 19.17 1945 19.03 1946 16.78 (a) Use regression to find a quadratic model for the data. (Let t be the number - years since 1942. Round the regression parameters to three decimal places.) N = (b) Express using functional notation the number of women working outside the home in 1947, and then estimate that value. (Round your answer to two decimal places.) million )=[ (c) The actual number of women working outside the home in 1947 was 16.90 million, whereas in 1948 the number was 17.58 million. In light of this, is a quadratic model appropriate for the period from 1942 through 1948? Yes O No

Algebra & Trigonometry with Analytic Geometry

13th Edition

ISBN:9781133382119

Author:Swokowski

Publisher:Swokowski

Chapter7: Analytic Trigonometry

Section7.6: The Inverse Trigonometric Functions

Problem 93E

See similar textbooks

Similar questions

Fit a linear-regression model relating annual inci- dence of diabetes to time period. (For this purpose, score the time period as 1 if 1970−1974, 2 if 1975−1979, . . . , 5 if 1990−1994.)
In running a regression of the retunrs of stock XYZ against the returns on the market, the Std for the returns of stock XYZ is 20% and that of the market returns is 15%. If the estimated beta is found to be 0.75 : What is the maximum possible value of beta given that the standar deivation of the returns of stock XYZ is 20% and those of the market is 15% ?
Construct an example of a regression model that satisfies the assumptionE(ui | Xi) = 0 but for which E(U | X ) ≠0n.
Suppose that a researcher, using wage data on 200 randomly selected male workers and 240female workers, estimates the OLS regressionW age ˆ =10(0.2)+2(0.4)× M ale, R2 = 0.10, SER = 4,where Wage is measured in dollars per hour and Male is a binary variable that is equal to 1 if theperson is a male and 0 if the person is a female. Define the wage gender gap as the difference inmean earnings between men and women.1. What is the estimated gender gap?2. Is the estimated gender gap significantly different from 0? (Compute the p-value for testingthe null hypothesis that there is no gender gap.)13. Construct a 95% confidence interval for the gender gap.4. In the sample, what is the mean wage of women? Of men?5. Another researcher uses these same data but regresses Wages on Female, a variable that isequal to 1 if the person is female and 0 if the person a male. What are the regression estimatescalculated from this regression?W age ˆ = _ + _ × F emale, R2 = _, SER = _.
The slope of the regression line represents the amount of change that is expected to take place in x when y increases by one unit. True False
Find the least-squares regression line treating square footage as the explanatory variable. y = (Round the slope to three decimal places as needed. Round the intercept to one decimal place as needed.)
The following estimated regression model was developed relating yearly income (Y in $1,000s) of 30 individuals with their age in years (X1) and their gender (X2) (0 if male and 1 if female). The yearly income of a 24-year-old female individual is
The following table gives the actual or projected population in millions for a certain city for selected years from 2000 to 2050. Use the linear regression to find the linear equation that is best fit for the data, width x equal to the number of years past 2000. Round your coefficients to three decimal places. Years Population (billions) 2000 1.1 2010 1.9 2020 2.4 2030 2.7 2040 3.1 2050 3.8
Suppose a psychologist used number of minutes of sleep in the night before a quiz to predict the time needed to finish the quiz using simple linear regression. The unstandardized coefficient (B in SPSS) is 5. If the psychologist converts the unit of sleep duration from minute to hour (a value of 60 [minutes] is changed to a value of 1 [hour]) and do the regression again, what is the unstandardized coefficient of sleep in this analysis using hour as the unit?
Write down the regression model with the unknown coefficients.
Compute the least-squares regression equation for the given data set. Use a TI-84 calculator. Round the slope and y-Intercept to at least four decimal places.
For the 23 space shuttle flights before the Challenger mission disaster in 1986, The following table shows the temperature (◦F) at the time of the flight and whether at least one primary O-ring suffered thermal distress. Use logistic regression to model the effect of temperature on the probability of thermal distress. Interpret the effect. Estimate the probability of thermal distress at 31◦F, the temperature at the time of the Challenger flight. At what temperature does the estimated probability equal 0.50? At that temperature, give a linear approximation for the change in the estimated probability per degree increase in temperature. Interpret the effect of temperature on the odds of thermal distress. Test the hypothesis that temperature has no effect, using (i) the Wald test, (ii) the likelihood-ratio test.