Using a sample of 50, the following regression output is obtained from estimating the linear probability regressionmodel y = 30 + B1x + ε. What is the predicted probability when x = 14?InterceptX8.34O 0.724.42O 3.86Coefficients4.14-0.02StandardError0.300.03t Stat1.45-4.65P-value0.00010.0000

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Answered: Using a sample of 50, the following…

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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Use the given information about sums of squares and sample size for a linear model to fill in all values in the analysis of variance for regression table below. SSModel = 250 with SSTotal = 3000 and a sample size of n = Round your answer for the p-value to four decimal places, and all other answers to three decimal places, if necessary. Source Model Error Total i df 100. SS MS F-statistic i p-value
Two measures of a baseball player's effectiveness as a hitter are the number of hits he makes in a season and thenumber of times he "bats in" a run (knows as "Runs Batted In" or RBIs). Can we predict a batter's RBIs from hisMajor League Baseball batters in 2017.hits? Below is numerical and graphical output from a computer regression of RBIs on Hits for 12 randomly selected Major League Baseball batters in 2017. Assume that the conditions for inference have been satisfied.(a) Do these data provide convincing evidence that there is a linear relationship between RBIs and Flits for MajorLeague Baseball batters in 2017? (b) Construct a 95% confidence interval for the slope of the population regression line for predicting RBIs from Hits.
Perform a regression on two different variables within the attached data, and exaplin what the findings determine. What decision can be made?
Consider a binary response variable y and a predictor variable x. The following table contains the parameter estimates of the linear probability model (LPM) and the logistic regression model, with the associated p-values shown in parentheses. Variable Intercept x LPM Logistic -6.90 (0.06) 0.19 (0.06) a. Test for the significance of the intercept and the slope coefficients at the 5% level in both models. Coefficients Intercept Slope -0.67 (0.02) 0.07 (0.03) LPM Logistic b. What is the predicted probability implied by the linear probability model for x=20 and x= 35? Note: Round intermediate calculations to at least 4 decimal places and final answers to 2 decimal places. Report the probability between 0 and 1 (not in %).
Analyse the following regression model Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -70.215668 83.5899819 -0.840001 0.40144881 -234.58524 94.1539002 -234.58524 94.1539002 LandArea 5.4726E-06 2.86E-05 0.19134566 0.84835946 -5.077E-05 6.1712E-05 -5.077E-05 6.1712E-05 Rooms -12.67723 10.5132648 -1.2058319 0.22865076 -33.350291 7.9958309 -33.350291 7.9958309 EquivArea 3.78736382 0.24640948 15.3702034 1.3343E-41 3.30282944 4.2718982 3.30282944 4.2718982 Condition 16.0812012 10.2954004 1.56197919 0.11914546 -4.163456 36.3258584 -4.163456 36.3258584 Years 2.13812701 0.32673784 6.54386092 1.9948E-10 1.49563664 2.78061738 1.49563664 2.78061738
b. Use statistical software to replicate the following regression analysis with all the Independent variables. Compute the coefficient of multiple determination. (Negative amounts should be indicated by a minus sign. Round your answer to 3 decimal places.) The regression equation is Paralegal GPA = -0.411 +1.20 HSGPA + 0.00163 SAT_Verbal - 0.00194 SAT_Math Predictor Constant HSGPA SAT Verbal SAT Math 0.001629 -0.001939 Coefficient SE Coefficient -0.4111 1.2014 t 0.7823 -0.53 р 0.622 0.2955 0.002147 4.07 0.010 0.76 0.482 0.002074 -0.94 0.393 Analysis of Variance SOURCE DF SS Regression 3 4.3595 MS F 1.4532 10.33 р 0.014 Residual Error 5 0.7036 0.1407 Total 8 5.0631 Source DF Seq SS HSGPA 1 4.2061 SAT Verbal 1 0.0303 SAT Math 1 0.1231 Coefficient of multiple determination Conduct a global test of hypothesis from the preceding output. c-1. State the decision rule at the 0.05 level of significance. (Round your answer to 2 decimal places.) Reject H₂ if F> c-2. Compute the value of F. (Round…
If a regression model of the form y= B,+B,x,+... + B,x, is fit to 132 observations on each variable and yields an R´value of 0.87, fill in the blanks in the following ANOVA table. Do all calculations to at least three decimal places. Source of Degrees of freedom Sums of Mean F statistic variation squares squares Regression 69 Error Total
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The commercial division of a real estate firm is conducting a regression analysis of the relationship between x, annual gross rents (in thousands of dollars), and y, selling price (in thousands of dollars) for apartment buildings. Data were collected on several properties recently sold and the following computer output was obtained. Analysis of Variance SOURCE DF Adj SS Regression 1 41587.3 Error 7 Total 8 51984.1 Predictor Coef SE Coef T-Value Constant 20.000 3.2213 6.21 X 7.210 1.3626 5.29 Regression Equation Y = 20.0 + 7.21 X (a) How many apartment buildings were in the sample? (b) Write the estimated regression equation. ŷ = (c) What is the value of sb1? Find the value of the test statistic. (Round your answer to two decimal places.) Find the p-value. (Round your answer to three decimal places.) p-value =
Rewrite the regression model to include coefficients from your regression analysis output and then answer the following question What would be the company's loss if the significant variable(s) change per unit? SUMMARY OUTPUT Regression Statistics Multiple R 0.93082 R Square 0.866425 Adjusted R Square 0.85833 Standard Error 4108.993 Observations 36 ANOVA df SS MS F Significance F Regression 2 3.61E+09 1.81E+09 107.0261 3.75E-15 Residual 33 5.57E+08 16883824 Total 35 4.17E+09 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 3996.678 6603.651 0.605223 0.549171 -9438.55 17431.91 -9438.55 17431.91 X Variable 1 43.5364 3.589484 12.12887 1.05E-13 36.23354 50.83926 36.23354 50.83926 X Variable 2…
Let's study the relationship between brand, camera resolution, and internal storage capacity on the price of smartphones. Use α = .05 to perform a regression analysis of the Smartphones01CS dataset, and then answer the following questions. When you copy and paste output from MegaStat to answer a question, remember to choose to "Keep Formatting" to paste the text. a. Did you find any evidence of multicollinearity and variance inflation among the predictors. Explain your answer using a VIF analysis. b. Copy and paste the normal probability plot for your analysis. Is there any evidence that the errors are not normally distributed? Explain. c. Copy and paste the Residuals vs. Predicted Y-values. Does the pattern support the null hypothesis of constant variance for the errors? Explain. d. Study the residuals analysis. Which observations, if any, have unusual residuals? e. Study the residuals analysis. Calculate the leverage statistic. Which observations, if any, are high leverage…
In a regression model involving 30 observations, the following estimated regression equation was obtained:ŷ = 17 + 4x1 − 3x2 + 8x3 + 8x4For this model, SSR = 700 and SSE = 100.The critical F value at 95% confidence is _____. a. 2.76 b. 2.69 c. 2.53 d. 2.99

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