Using output given below, (a) Set-up simultaneous 95% confidence intervals for Bo and B1. (b) Set-up...
10. The following is the simple linear regression analysis output: E(Y) = Bo + B1 (ADV_X) The REG Procedure Model: MODELI Dependent Variable: SALES_T Analysis of Variance Sun of Mean Squares Square 4.90000 4.90000 1. 10000 0.36667 6.00000 Source Model Error Corrected Total F Value 13.36 Pr>F 0.0354 Root PSE Dependent Mean Coeff Var 0.60553 2.00000 30.27650 R-Square Ady A-se 0.8167 0.7556 Parameter Estimates Variable DF "estinato Value Pr > Itt 95% confidence Linite "Error .. 63503 0.19149 -0.10000 0.70000...
Which model is more appropriate for these data: the model in SAS Output 1 or the model in SAS Output 2? Which test statistic and p-value should you use to make this decision? Output 1 because the interaction is not significant (F = 0.92, p-value = 0.4594). Output 1 because the interaction is not significant (F = 6.25, p-value = 0.0003). Output 1 because the interaction is significant (F = 6.25, p-value = 0.0003). Output 2 because the interaction is...
please answer the following using the r code provided . The data set below contains information about the gasoline mileage performance for 32 au- tomobiles. We are interested in developing a model to predict the miles per gallon () using related predictor variables. The variables in the study are Dependent variable: Miles per gallon (v) Independent variables: ri horsepower (ft-lb) ra: torque (ft-lb) r: horsepower+torque (ft-lb) rs: carburetor (barrels) (a) We first start by fitting a model using y and...
can you answer question 9 please Problems 473 results from parts (a), (b), and (c). What model seems most plausible? How do the data limit your conclusions? tle the data from Freund (1979), presented in Problem 22 in Chapter 14. Taking be model discussed there as the maximum model, repeat parts (a) through (h) of Problem 6. In part (h), note the possible role of collinearity. A random sample of data was collected on residential sales in a large city....
X Part I. Derive Bivariate Regression by hand. Again, we are using the same data set that we used in the in-class assessment. Case Dietary Fat Body Fat 22 9.8 22 11.7 14 8.0 21 9.7 32 10.9 26 7.8 30 21 17 1. Step 1: Find the mean of dietary fat x = 2. Step 2: Find the mean of body fat y = 3. Step 3: Find the sum of (x1 - x)y- y) = 3316 4. Step...
(I did this homework in completion but professor was not happy with answers whatsoever, need additional answers and especially improvement to 1.b help!! photos not attaching? mean by severai steps. inis is a View Feedback homework and will need you to work, in one two View Feedback or various steps. Unfortunately, I cannot read your screen shot of what you did on excel. As I have said in numerous messages announcements etc, I cannot аcсept pictures. You need to write...