A researcher records age in years (x) and systolic blood pressure (y) for volunteers. They perform aregression analysis was performed, and a portion of the computer output is as follows:ŷ = 4.5+ 14.4xCoefficients(Intercept)xEstimate4.5Ho: B₁ = 0H₁: B₁ > 0Ho: B₁ = 0Ha: B₁ <014.4Ho: B₁ = 0Ha:B₁ #0Std. Error Test statistic2.94.71.553.06P-valueSpecify the null and the alternative hypotheses that you would use in order to test whether a linearrelationship exists between x and y.0.070

The regression equation is y^=4.5+14.4x. To test whether a linear relationship exists between x and…

Answered: A researcher records age in years (x)…

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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A group of 13 health children and adolescents participated in a study designed to analyze the relationship between age (AGE in years) and average total sleep time (ATST in minutes). To obtain a measure for ATST, recordings were taken on each subject on three consecutive nights and then averaged. The results obtained are given below along with the corresponding scatter plot. The estimated regression line is Y-hat= 646.483 – 14.041x Interpret the slope and intercept of the estimated regression line. Is it meaningful?
The following table gives the data for the average temperature and the snow accumulation in several small towns for a single month. Determine the equation of the regression line, >= bo + b₁x. Round the slope and y-intercept to the nearest thousandth. Then determine if the regression equation is appropriate for making predictions at the 0.05 level of significance. Critical Values of the Pearson Correlation Coefficient Average Temperatures and Snow Accumulations 42 33 15 44 35 15 34 8 18 25 9 14 28 26 11 25 Average Temperature (°F) Snow Accumulation (in.) 35 43 20 10
Consider the following computer output from a multiple regression analysis relating the cost of car insurance to the variables: number of car accidents, driver's credit score, and safety rating of the car. Answer Coefficients Coefficients Standard Error 680 Intercept Car Accidents (In last 3 years) Credit Score -74.80 Safety Rating - 104.25 122.40 Does the sign of the coefficient for the variable number of car accidents make sense? 79.71 14.75 8.89 11.46 t Stat P-value 8.531 0.0000 0.0000 8.298 -8.414 -9.097 0.0000 0.0000 O Yes, because it is expected that as the number of car accidents increases then the cost should also increase. O No, because it is expected that as the number of car accidents increases then the cost should also increase. O Yes, because it is expected that as the number of car accidents increases then the cost should decrease. O No, because it is expected that as the number of car accidents increases then the cost should decrease. Keyp- Keyboard Short Tables
Interpret the estimated education coefficient. Is the estimate of the constant statistically significant at the 5% level? Explain how you reached this conclusion. Imagine you want to test with an F-test whether the effect of married and nonwhite are jointly significantly different from zero. Write down the null and alternative hypothesis for such a test
Consider the following computer output from a multiple regression analysis relating the price of a used car to the variables: age of car, mileage, and safety rating. Coefficients Coefficients Standard Error t Stat P-value Intercept 49314.99 6450.95 7.645 0.0000 Age (Year) - 25890.57 3156.87 - 8.201 0.0000 Mileage - 1584.68 127.77 - 12.402 0.0000 (in Thousands) Safety Rating - 1762.27 2658.52 -0.663 0.5102 Does the sign of the coefficient for the variable safety rating make sense? Answer E Tables Keypa Keyboard Shorte O Yes, because it is expected that as safety rating increases then the price should decrease. O No, because it is expected that as safety rating increases then the price should also increase. O No, because it is expected that as safety rating increases then the price should decrease. O Yes, because it is expected that as safety rating increases then the price should also increase.
A student used multiple regression analysis to study how family spending (y) is influenced by income (x1), family size (x2), and additions to savings (x3). The variables y, x1, and x3 are measured in thousands of dollars. The following results were obtained. ANOVA df SS Regression 3 45.9634 Residual 11 2.6218 Total Coefficients Standard Error intercept 0.0136 x1 0.7992 0.074 x2 0.2280 0.190 x3 -0.5796 0.920 answer please : 1: Carry out a test to see if x3 and y are significantly related. Use a 5% level of significance.
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 - 2.00x
A student used multiple regression analysis to study how family spending (y) is influenced by income (x) family size (x2), and addition to savings(x3). The variables y, x1, and x3. The variables y, x1, and x3 are measured in thousands of dollars . The following results were obtained. ANOVA df SS Regression 3 45.9634 Residual 11 2.6218 Total Coefficient Standard Error Intercept 0.0136 X1 0.7992 0.074 X2 0.2280 0.190 X3 -0.5796 0.920 Write out the estimated regression equation for the relationship between the variables. Compute coefficient of determination. What can you say about the strength of this relationship? Carry out a test to determine whether y is significantly related to the independent variables. Use a 5% level of significant. Carry out a test to see if X3 and y are significantly related. Use a 5% level of significance
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…
Consider the following computer output from a multiple regression analysis relating the cost of car insurance to the variables: number of car accidents, driver's credit score, and safety rating of the car. Intercept Car Accidents (In last 3 years) Credit Score Safety Rating Coefficients 1186 213.48 Coefficients - 130.46 294.11 Standard Error Does the sign of the coefficient for the variable safety rating make sense? 123.87 21.89 14.26 356.37 t Stat P-value 9.575 0.0000 9.752 0.0000 -9.149 0.0000 0.825 0.4128
A student used multiple regression analysis to study how family spending (y) is influenced by income (x1), family size (x2), and additions to savings(x3). The variables y, x1, and x3 are measured in thousands of dollars. The following results were obtained. ANOVA df SS Regression 3 45.9634 Residual 11 2.6218 Total Coefficients Standard Error Intercept 0.0136 x1 0.7992 0.074 x2 0.2280 0.190 x3 -0.5796 0.920 Write out the estimated regression equation for the relationship between the variables. Compute coefficient of determination. What can you say about the strength of this relationship? Carry out a test to determine whether y is…
Consider the following computer output from a multiple regression analysis relating the cost of car insurance to the variables: number of car accidents, driver's credit score, and safety rating of the car. Intercept Car Accidents (In last 3 years) Credit Score Safety Rating Answer Coefficients 933 167.94 - 102.63 -199.18 Does the sign of the coefficient for the variable credit score make sense? Coefficients Standard Error 95.65 17.99 10.89 19.98 t Stat P-value 9.754 0.0000 9.335 0.0000 -9.424 0.0000 -9.969 0.0000 O Yes, because it is expected that as the credit score increases then the cost should decrease. O No, because it is expected that as the credit score increases then the cost should decrease. O Yes, because it is expected that as the credit score increases then the cost should also increase. O No, because it is expected that as the credit score increases then the cost should also increase. Tables Keypad Keyboard Shortcuts

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