Of the 6 explanatory variables below, how many are not significant at the 1% level?Observations1338ANOVAdfSSMSSignificance FRegressionResidualTotal1.472E+11 2.45E+10 668.12431711331 48874008927 367197661337 1.96074E+11Coefficients Standard Errort StatP-valueLower 95%Intercept-13361.1 1087.671766 -12.2841 6.21526E-33 -15494.8608age257.2881 11.88574319 21.64678 2.84861E-89 233.9712431bmi332.5701 27.72216553 11.99654 1.47163E-31 278.1862322children479.3694 137.64421833.48267 0.000512527 209.3461372gender131.1106 332.8107041 0.393949 0.693681495 -521.780121smoker23820.43 411.8428778 57.83865O 23012.50222region353.64151.9265899 2.327703 0.020077148 55.59834776O a) 1b) 2c) 3d) 4

Result: P value< level of significance then variable is significant. P value> level of…

Answered: Of the 6 explanatory variables below,…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Based on the scatter plot above, describe the relationship between the amount of cost damage and the distance from nearest fire station to the residential. Determine the regression line for the above regression output. Interpret the slope coefficient of distance to the nearest station affecting the amount of fire cost damage. Compute the correlation coefficient and interpret its meaning. Does the distance to the nearest fire station has significant influence on the cost damage amount? Conduct a test at 1% significance level. ] Predict the fire cost damage generated if the distance from nearest fire station is 2.5 kilometers. Is this estimate reliable? Explain. []
Compute the predicted value of IPT for the mean value of PSS (static facial expressions (higher PSS)) The regression formula is: The terms of the equation and their values are:
Perform a repeated measures ANOVA on the data in the table and construct an ANOVA summary table. Does the ANOVA suggest that systematic differences exist across trials? Calculate the intraclass correlation coefficient for models 2,1 and 3,1. Calculate the standard error of measurement. Assume a person has an observed score of 40. Construct a 95% CI about this subject’s observed score to estimate the interval in which we think that person’s true score lies. Calculate the minimal difference using a 95% CI. Day 1 Day 2 Day 3 35.6 36.8 37.5 40.3 39.5 41.5 37.5 36.5 35.5 45.8 42.5 48.6 41.3 38.6 37.2 34.8 31.9 32.5 41.3 46.5 45.6 32.1 34.7 36.5 29.8 31.2 33.8 48.6 45.6 43.5 42.6 41.8 43.4 37.8 35.1 39.5
**Please complete table**
A multiple regression analysis produced the following tables. Predictor Coefficients StandardErrort Statistic p-valueIntercept -139.609 2548.989 -0.05477 0.957154x 24.24619 22.25267 1.089586 0.295682x 32.10171 17.44559 1.840105 0.08869Source df SS MS F p-valueRegression 2 302689 151344.5 1.705942 0.219838Residual 13 1153309 88716.07Total 15 1455998Using = 0.01 to test the null hypothesis H :?1 = ?2 = 0, the critical F value is ____.6.701.964.845.995.70
A study was conducted to examine how fear of public speaking varies across year of tertiary education (1st, 2nd or 3rd). Subjects were interviewed at the end of each of year of their tertiary education for fear of public speaking (measured on a 10 point metric scale). Which of the following would be an appropriate statistical test to conduct, which addresses these hypotheses? Group of answer choices: Factorial ANOVA Mixed ANOVA Multiple Regression Within Subjects ANOVA Single Factor ANOVA
Present the results using APA format. What does the conclusions mean in general terms?
A regression analysis was performed and the summary output is shown below. Regression Statistics Multiple R 0.7802268560.780226856 R Square 0.6087539470.608753947 Adjusted R Square 0.5870180550.587018055 Standard Error 6.7217061336.721706133 Observations 20 ANOVA dfdf SSSS MSMS F� Significance F� Regression 11 1265.3871265.387 1265.3871265.387 28.006928.0069 4.9549E-054.9549E-05 Residual 1818 813.264813.264 45.18145.181 Total 1919 2078.6512078.651 Step 2 of 2: Which measure is appropriate for determining the proportion of variation in the dependent variable explained by the set of independent variable(s) in this model?
Question 2:(15 points) Consider the following joint probability density function of the random variables X and Y: 4xy, 0
Consider the following ANOVA table for a multiple regression model relating housing prices (in thousands of dollars) to the number of bedrooms in the house and the size of the lot on which the house was built (in square feet). There were 9090 total observations. Estimated Price=20,160.07+2188.83(Bedrooms)+0.2139(Lot Size)Estimated Price=20,160.07+2188.83(Bedrooms)+0.2139(Lot Size) ANOVA dfdf SSSS MSMS F� Significance F� Regression 22 306,443.7975306,443.7975 153,221.8988153,221.8988 21.727421.7274 2.2211E-082.2211E-08 Residual 8787 613,525.5190613,525.5190 7052.01757052.0175 Total 8989 919,969.3165919,969.3165 What percent of variation in housing prices is explained by the number of bedrooms and lot size? Round your answer to two decimal places
A car dealership would like to develop a regression model that would predict the number of cars sold per month by a dealership employee based on theemployee's number of years of sales experience. The accompanying regression output was developed based on a random sample of employees. ANOVA df SS Regression 1 79.909407 Residual 23 261.210593 Total 24 341.12 Coefficients Standard Error Intercept 7.271539 1.229763 Slope 0.539854 0.203521 The coefficient of determination is 0.234 Test statistic= 0.704 P-value= 0.014 Construct a 95% confidence interval around the sample slope and interpret its meaning. The confidence interval is (__________,_________). (Type an integer or decimal rounded to three decimal places as needed.)
A researcher is interested in whether an approved supplement enhances the performance of baseball players. The research compares two different baseball teams on the number of home runs they hit for the season. Which test is most appropriate to conduct? Chi-square Independent t-test Correlation/Regression Dependent t-test One-Way ANOVA

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