8. (20 marks) One end A of an elastic string was attached to a horizontal bar and a mass m grams,...
R is a little difficult for me, please answer if you can interpret the R code, I want to learn better how to interpret the R code 4. each 2 pts] Below is the R output for a simple linear regression model Coefficients: Estimate Std. Error t value Pr(>t) (Intercept) 77.863 4.199 18.544 3.54e-13 3.485 3.386 0.00329* 11.801 Signif. codes: 0 0.0010.010.05 0.11 Residual standard error: 3.597 on 18 degrees of freedom Multiple R-squared: 0.3891, Adjusted R-squared: 0.3552 F-statistic: 11.47...
An elastic string of negligible mass has one end fixed to a ceiling at point A. The other end, point B, which is attached to a particle of mass 3 kg, is in a position such that it is vertically below point A, with the distance AB equal to 0.7 m. The mass is released from rest. If the modulus of elasticity of the string is 35 N and its natural length is 1.3m find i) The distance of the...
4. Linear Regression [TOTAL 5 MARKS] McDonald (1989) collected the amphipod crustacean Platorchestia platensis on a beach near Stony Brook, Long Island. He removed and counted the number of eggs for 28 females, and then freeze-dried and weighed the mothers (in mg) summary (1m (eggs~mass, eggdata) ) Call 1m (formula data eggdata) eggs mass, Coefficients: Estimate Std. Error t value Pr (>I t|) 12.6890 4.2009 3.021 0.0056 (Intercept) 1.6017 0.6176 2.593 0.0154 mass Signif. codes: 0* 0.001 0.01 0.05 .0.1...
8. A regression of wage (log(wage) is run on a set of following variables: female (-1 if female), educ (years of education), exper (years of experience) and tenure (years with current employer). The regression results are listed as follows. Coefficients: Estimate Std. Error tvalue Pr(Itl) (Intercept) -1.56794 0.72455 -2.164 0.0309 female -1.81085 0.26483 -6.838 2.26e-11*** educ 0.57150 0.04934 11.584 <2e-16*** 0.02540 0.01157 2.195 0.0286 exper 0.14101 0.02116 6.663 6.83e-11*** tenure Signif. codes:0.0010.010.050.1'"1 Residual standard error: 2.958 on 521 degrees of...
8. A regression of wage (log(wage) is run on a set of following variables: female (-1 if female), educ (years of education), exper (years of experience) and tenure (years with current employer). The regression results are listed as follows. Coefficients: Estimate Std. Error tvalue Pr(Itl) (Intercept) -1.56794 0.72455 -2.164 0.0309 female -1.81085 0.26483 -6.838 2.26e-11*** educ 0.57150 0.04934 11.584 <2e-16*** 0.02540 0.01157 2.195 0.0286 exper 0.14101 0.02116 6.663 6.83e-11*** tenure Signif. codes:0.0010.010.050.1'"1 Residual standard error: 2.958 on 521 degrees of...
8. A regression of wage (log(wage) is run on a set of following variables: female (-1 if female), educ (years of education), exper (years of experience) and tenure (years with current employer). The regression results are listed as follows. Coefficients: Estimate Std. Error tvalue Pr(Itl) (Intercept) -1.56794 0.72455 -2.164 0.0309 female -1.81085 0.26483 -6.838 2.26e-11*** educ 0.57150 0.04934 11.584 <2e-16*** 0.02540 0.01157 2.195 0.0286 exper 0.14101 0.02116 6.663 6.83e-11*** tenure Signif. codes:0.0010.010.050.1'"1 Residual standard error: 2.958 on 521 degrees of...
Consider the dataset in the proj2-3.txt file on BlackBoard. In this problem, focus is on high systolic blood pressure (sbp) and possible explanatory variables Body Mass Index (bmi), and scale (scl). Consider the linear regression model with response high SBP and scale as explana- tory variables. Explain the coefficients in the model? Explain the null hypotheses that the estimated slope equals 0? Write a summary of your findings. What is your conclusion? Residuals: Min 1Q Median 3Q Max -72.64 -27.55...
(13 points) Suppose you have a simple linear regression model such that Y; = Bo + B18: +€4 with and N(0,0%) Call: 1m (formula - y - x) Formula: F=MSR/MSE, R2 = SSR/SSTO ANOVA decomposition: SSTOSSE + SSR Residuals: Min 1Q Modian -2.16313 -0.64507 -0.06586 Max 30 0.62479 3.00517 Coefficients: Estimate Std. Error t value Pr(> It) (Intercept) 8.00967 0.36529 21.93 -0.62009 0.04245 -14.61 <2e-16 ... <2e-16 .. Signif. codes: ****' 0.001 '** 0.01 '* 0.05 0.1'' 1 Residual standard...
just anw the c part thx Question 1 (100 Marks) The following table is the regression results from the econometric model: LOG(SALES) = B. + B2LOG (PRICE) + BzADVERT + e For a sample of 66 observations. SALES: Monthly Sales of product A ($1000) PRICE: A price Index of product A (SI) ADVERT: Adverting Expenditure on product A (S1000) Dependent Variable: LOGSALES Method: Least Squares Date:03/19/20 Time: 20:04 Included observations: 66 Variable Coefficient Std. Error -Statistic Prob. LOGPRICE ADVERT 5.325153...
Problem 4 (20%) Figure 5 shows a uniform elastic bar fixed at one end and attached to a mass M at the other end. The cross sectional area for the bar is A, mass density per unit length p, modulus of elasticity E and second moment of area I. For the longitudinal vibration: S Set the necessary coordinate system, governing equation of motion and boundary conditions a. b. Derive the general solution. Explain how you can obtain the natural frequencies...