The results below represent the logit regression on the probability of taking out a loan.Assume that the equation is statistically significant.Coefficients:Estimate Std. Error z value Pr (>|z|)wwwwwwwwww(Intercept) -11.97617541.5197691 -7.880 3.27e-15 ***1.6895310.106523 15.861 < 2e-16 ***0.0006640.0005231.271 0.2036580.686931 0.0687960.0539410.002416-0.070995 0.056987 -1.246 0.2128410.0831010.056694 1.466 0.142710.1313430.036441AgeCCAvgEducationExperienceFamilyIncomeMortgage9.9852e-16 ***22.482e-16 ***Select one:O a. Increases probability of taking out a loanO b. Decreases probability of taking out a loanO c. Cannot be determined3.604 0.000313 ***What is the effect of increasing Age (in years) on the probability of taking out a loan?Direction not magnitude.

It is required to determine the effect of increasing age on the probability of taking out a loan.

Answered: The results below represent the logit…

A First Course in Probability (10th Edition)

10th Edition

ISBN:9780134753119

Author:Sheldon Ross

Publisher:Sheldon Ross

Chapter1: Combinatorial Analysis

Section: Chapter Questions

Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...

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Run a regression of the logarithm of average hourly earnings, ln(wage) on educ, exper, exper2, female, married and numdep. Discuss the statistical significance of each of the slope coefficients and interpret the coefficient on female and married.
How many predictors are in this regression?
Hanny
You estimated a regression with the following output. Source | SS df MS Number of obs = 325 -------------+---------------------------------- F(1, 323) = 42850.36 Model | 285905003 1 285905003 Prob > F = 0.0000 Residual | 2155111.65 323 6672.17228 R-squared = 0.9925 -------------+---------------------------------- Adj R-squared = 0.9925 Total | 288060115 324 889074.429 Root MSE = 81.683 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 11.83842 .0571895 207.00 0.000 11.72591 11.95093 _cons | 52.14457 5.949458 8.76 0.000 40.43999 63.84915…
In this regression, which predictor(s) is/are significant? Coefficients: (Intercept) time pub O'pubs' Neither 'time' Both Estimate Std. Error t value Pr (>|t|) 43082.4 3099.5 13.900 9.26e-09 982.9 121.8 452.1 2.174 0.0504 149.7 0.814 0.4317
K 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 2000, where x is the number of decades after 1900. year, x 0 (1900) life expectancy, y 49.1 years 51.4 years 2 (1920) 4 (1940) 53.0 years 6 (1960) 54.2 years 55.2 years 8 (1980) Choose the regression line. OA. y = 0.750x - 49.58 O C. y = 0.750x + 49.58 OB. y=49.58x + 0.750 = 49.58 ○ D. y = The life expectancy in the year 2000, rounded to one decimal place is years.
You estimated a regression with the following output. Source | SS df MS Number of obs = 423 -------------+---------------------------------- F(1, 421) = 267.80 Model | 8758968.84 1 8758968.84 Prob > F = 0.0000 Residual | 13769523.8 421 32706.7074 R-squared = 0.3888 -------------+---------------------------------- Adj R-squared = 0.3873 Total | 22528492.7 422 53385.0537 Root MSE = 180.85 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 6.150402 .3758334 16.36 0.000 5.411658 6.889145 _cons | -8.022201 24.02003 -0.33 0.739 -55.23632 39.19192…
The data show the bug chirps per minute at different temperatures. Find the regression equation, letting the first variable be the independent (x) variable. Find the best predicted temperature for a time when a bug is chirping at the rate of 3000 chirps per minute. Use a significance level of 0.05. What is wrong with this predicted value? Chirps in 1 min 1118 796 1161 918 1103 1219 Temperature (F) 82 72.2 92.2 73 87.5 94.4
The slope, b represents * O predicted value of Y when X = 0. O the estimated average change in Y per unit change in X. O variation around the line of regression. O the predicted value of Y.

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