4.3 Consider the simple linear regression model fit to the solar energy data. DATA SETS FOR EXERCISES 555 TABLE B.2 Solar Thermal Energy Test Data y x₁ X₂ X3 X4 xs 271.8 783.35 33.53 40.55 16.66 13.20 264.0 748.45 36.50 36.19 16.46 14.11 238.8 684.45 34.66 37.31 17.66 15.68 230.7 827.80 33.13 32.52 17.50 10.53 251.6 860.45 35.75 33.71 16.40 11.00 257.9 875.15 34.46 34.14 16.28 11.31 263.9 909.45 34.60 34.85 16.06 11.96 266.5 905.55 35.38 35.89 15.93 12.58 229.1 756.00 35.85 33.53 16.60 10.66 239.3 769.35 35.68 33.79 16.41 10.85 258.0 793.50 35.35 34.72 16.17 11.41 257.6 801.65 35.04 35.22 15.92 11.91 267.3 819.65 34.07 36.50 16.04 12.85 267.0 808.55 32.20 37.60 16.19 13.58 259.6 774.95 34.32 37.89 16.62 14.21 240.4 711.85 31.08 37.71 17.37 15.56 227.2 694.85 35.73 37.00 18.12 15.83 196.0 638.10 34.11 36.76 18.53 16.41 278.7 774.55 34.79 34.62 15.54 13.10 272.3 757.90 35.77 35.40 15.70 13.63 267.4 753.35 36.44 35.96 16.45 14.51 254.5 704.70 37.82 36.26 17.62 15.38 224.7 666.80 35.07 36.34 18.12 16.10 181.5 568.55 35.26 35.90 19.05 16.73 227.5 653.10 35.56 31.84 16.51 10.58 253.6 704.05 35.73 33.16 16.02 11.28 263.0 709.60 36.46 33.83 15.89 11.91 265.8 726.90 36.26 34.89 15.83 12.65 263.8 697.15 37.20 36.27 16.71 14.06 b. Construct and interpret a plot of the residuals versus the predicted response.

Using data from 50 workers, a researcher estimates Wage = β0 + β1Education + β2Experience + β3Age + ε, where Wage is the hourly wage rate and Education, Experience, and Age are the years of higher education, the years of experience, and the age of the worker, respectively. The regression results are shown in the following table. Coefficients Standard Error t Stat p-Value Intercept 8.93 3.96 2.26 0.0289 Education 1.34 0.38 3.53 0.0010 Experience 0.48 0.20 2.40 0.0205 Age −0.04 0.05 −0.80 0.4278 a-1. Interpret the point estimate for β1. multiple choice 1 As Education increases by 1 year, Wage is predicted to increase by 1.34/hour. As Education increases by 1 year, Wage is predicted to increase by 0.48/hour. As Education increases by 1 year, Wage is predicted to increase by 1.34/hour, holding Age and Experience constant. As Education increases by 1 year, Wage is predicted to increase by 0.48/hour, holding Age and Experience constant. a-2. Interpret the point estimate for β2. multiple…

Compute the least-squares regression equation for the given data set. Use a Ti-84 caloulator. Round the slope and at least four decimal places. 4 6 12 -9 -3 5 -11 3. 31 36 3 -2 -14 Send data to Excel Regression line equation: y =I

Ch. 21 part 2 Run a regression analysis on the following bivariate set of data with y as the response variable. x y 43.8 54.1 41.3 51.2 35.3 60.1 48.1 44.9 42.8 51.8 44.7 50.8 39.4 56.6 38.2 56.6 40.7 53.5 45.3 51.1 45 46.7 41.1 52.2 Predict what value (on average) for the response variable will be obtained from a value of 40.1 as the explanatory variable. Use a significance level of α=0.05 to assess the strength of the linear correlation.What is the predicted response value?  (Report answer accurate to one decimal place.)y = ________________

Use General Linear Test

Gerardo is investigating how long his phone's battery lasts (in hours) for various brightness levels (on a scale of 0-100). His data is displayed in the table and graph below. Hours 10+ 9 8 7 6 3 2 1 Brightness Level (x) 16 Hours (y) 10 20 30 37 31 6.7 5.8 5.2 Submit Question -1 What is the residual for the point (58,7.1)? hours Rejected Questions 40 50 Brightness Level 37 4 60 70 44 5.9 80 58 7.1 77 5.1 99 4.2 90 100 110 Math Statistics

Data from the United Nations on individual countries Obesity rates and vegetable consumption by country were organized into the scatter plot below.   Linear Regression Equation for this data: y = -0.8449x + 50.328 Correlation Coefficient: 0.5   Question 8 options:   Strongly Correlated   Weakly Correlated   Not correlated   Moderately Correlated

A researcher is interested in examining the relationship between spousal abuse and child abuse.  Specifically, they are interested in determining whether there is a predictive relationship between spousal abuse and child abuse in 5 county social services offices.  Calculate the linear regression line for the following data.  Note you have already calculated the first step to this analysis (Pearson's Correlation)