2. A researcher aims to investigate the determinants of accounting quality of firms. With a sample that contains 108 firm-year observations from 1984 to 2003, the research run a mult- variate regression and got the following results: EQit = Bo + B1ASSET + B1STDCFOt+ B2SALESt + B3CYCLEt Where EQ is standard deviation of the Earnings; ASSET is total assets; STDCFO is standard deviation of cash flow from operation; SALES is sales revenue and CYCLE is operating cycle. Variables Coefficient Estimated Standard Deviation Intercept 0.0157 0.0019 ASSET -0.0033 -0.0021 STDCFO 0.1388 0.0043 SALES 0.0004 0.0004 CYCLE 0.0321 0.0093 The total sum of

The weights (in pounds) of 6 vehicles and the variability of their braking distances (in feet) when stopping on a dry surface are shown in the table. Can you conclude that there is a significant linear correlation between vehicle weight and variability in braking distance on a dry surface? Use a =0.01. Welght, x Varlablity in braking distance, y 5960 5390 6500 5100 5870 4800 2 1.75 1.95 1.93 1.57 1.64 1.50 E Click here to view a table of critical values for Student's t-distribution. Setup the hypothesis for the test. Ho: P Vo Hạ: p Identify the critical value(s). Select the correct choice below and fill in any answer boxes within your choice. (Round to three decimal places as needed.) O A. The critical values are -to = and to = O B. The critical value is. Calculate the test statistic. t= (Round to three decimal places as needed.) What is your conclusion? a significant linear correlation between vehicle weight and variability in braking distance on a dry surface. There Venough evidence…

Listed below are altitudes (thousands of feet) and outside air temperatures (°F) recorded during a flight. Find the (a) explained variation, (b) unexplained variation, and (c) indicated prediction interval. There is sufficient evidence to support a claim of a linear correlation, so it is reasonable to use the regression equation when making predictions. For the prediction interval, use a 95% confidence level with the altitude of 6327 ft (or 6.327 thousand feet). Altitude Temperature a. Find the explained variation. (Round to two decimal places as needed.) 2 55 8 40 13 25 20 - 3 28 - 26 31 - 41 34 - 53

The Cadet is a popular model of sport utility vehicle, known for its relatively high resale value. The bivariate data given below were taken from a sample of fifteen Cadets, each bought new two years ago, and each sold used within the past month. For each Cadet in the sample, we have listed both the mileage x (in thousands of miles) that the Cadet had on its odometer at the time it was sold used and the price y (in thousands of dollars) at which the Cadet was sold used. The least-squares regression line for these data has equation y = 40.86-0.45x. This line is shown in the scatter plot below. Mileage, x (in thousands) 29.7 27.8 26.8 24.2 21.1 23.0 24.3 15.4 37.7 23.6 34.4 27.8 23.5 20.9 259 Used selling price, y (in thousands of dollars) 27.4 29.4 31.2 30.1 31.7 31.7 27.2 34.2 23.4 28.2 26.3 26.5 33.7 30.6 267 Used selling price (in thousands of dollars) 40- 35+ 30- 25- 20 THIS 15 X 20 X X X 30 Mileagex (in thousands) X 35 40