I am not sure where to start to answer question 2A

use DARK ORANGE column numerical predictor variable, BLUE column categorical variable (the dummy variable) and all other WHITE column numerical variables as potential predictor variables in the multiple regression analysis.

Variable Info: A study of a random sample of 76 cold cereal characteristics Source StatCrunch Owner ds-231%sc Variables Name of cereal, calories per serving, grams of protein, grams of fat, milligrams of sodium, grams of dietary fiber, grams of complex carbohydrates, grams of sugars, milligrams of potassium, weight in ounces of one serving Manufacturer of Cereal General Mills, Kelloggs, Other Mfr CODE 1=General Mills or Kelloggs 0=Other

Name	calories (Yellow)	protein (White)	fat (White)	sodium (White)	fiber (White)	carbo (White)	sugars (White)	potass (White)	weight (Orange)	mfrCODE (Blue)	mfr_Label (White)
100%_Bran	70	4	1	130	10.0	5.0	6	280	1.00	0	Other
100%_Natural_Bran	120	3	5	15	2.0	8.0	8	135	1.00	0	Other
All-Bran	70	4	1	260	9.0	7.0	5	320	1.00	1	GenMills or Kelloggs
All-Bran_with_Extra_Fiber	50	4	0	140	14.0	8.0	0	330	1.00	1	GenMills or Kelloggs
Almond_Delight	110	2	2	200	1.0	14.0	8	1	1.00	0	Other
Apple_Cinnamon_Cheerios	110	2	2	180	1.5	10.5	10	70	1.00	1	GenMills or Kelloggs
Apple_Jacks	110	2	0	125	1.0	11.0	14	30	1.00	1	GenMills or Kelloggs
Basic_4	130	3	2	210	2.0	18.0	8	100	1.33	1	GenMills or Kelloggs
Bran_Chex	90	2	1	200	4.0	15.0	6	125	1.00	0	Other
Bran_Flakes	90	3	0	210	5.0	13.0	5	190	1.00	0	Other
Cap'n'Crunch	120	1	2	220	0.0	12.0	12	35	1.00	0	Other
Cheerios	110	6	2	290	2.0	17.0	1	105	1.00	1	GenMills or Kelloggs
Cinnamon_Toast_Crunch	120	1	3	210	0.0	13.0	9	45	1.00	1	GenMills or Kelloggs
Clusters	110	3	2	140	2.0	13.0	7	105	1.00	1	GenMills or Kelloggs
Cocoa_Puffs	110	1	1	180	0.0	12.0	13	55	1.00	1	GenMills or Kelloggs
Corn_Chex	110	2	0	280	0.0	22.0	3	25	1.00	0	Other
Corn_Flakes	100	2	0	290	1.0	21.0	2	35	1.00	1	GenMills or Kelloggs
Corn_Pops	110	1	0	90	1.0	13.0	12	20	1.00	1	GenMills or Kelloggs
Count_Chocula	110	1	1	180	0.0	12.0	13	65	1.00	1	GenMills or Kelloggs
Cracklin'_Oat_Bran	110	3	3	140	4.0	10.0	7	160	1.00	1	GenMills or Kelloggs
Crispix	110	2	0	220	1.0	21.0	3	30	1.00	1	GenMills or Kelloggs
Crispy_Wheat_&_Raisins	100	2	1	140	2.0	11.0	10	120	1.00	1	GenMills or Kelloggs
Double_Chex	100	2	0	190	1.0	18.0	5	80	1.00	0	Other
Froot_Loops	110	2	1	125	1.0	11.0	13	30	1.00	1	GenMills or Kelloggs
Frosted_Flakes	110	1	0	200	1.0	14.0	11	25	1.00	1	GenMills or Kelloggs
Frosted_Mini-Wheats	100	3	0	0	3.0	14.0	7	100	1.00	1	GenMills or Kelloggs
Fruit_&_Fibre_Dates,_Walnuts,_and_Oats	120	3	2	160	5.0	12.0	10	200	1.25	0	Other
Fruitful_Bran	120	3	0	240	5.0	14.0	12	190	1.33	1	GenMills or Kelloggs
Fruity_Pebbles	110	1	1	135	0.0	13.0	12	25	1.00	0	Other
Golden_Crisp	100	2	0	45	0.0	11.0	15	40	1.00	0	Other
Golden_Grahams	110	1	1	280	0.0	15.0	9	45	1.00	1	GenMills or Kelloggs
Grape_Nuts_Flakes	100	3	1	140	3.0	15.0	5	85	1.00	0	Other
Grape-Nuts	110	3	0	170	3.0	17.0	3	90	1.00	0	Other
Great_Grains_Pecan	120	3	3	75	3.0	13.0	4	100	1.00	0	Other
Honey_Graham_Ohs	120	1	2	220	1.0	12.0	11	45	1.00	0	Other
Honey_Nut_Cheerios	110	3	1	250	1.5	11.5	10	90	1.00	1	GenMills or Kelloggs
Honey-comb	110	1	0	180	0.0	14.0	11	35	1.00	0	Other
Just_Right_Crunchy__Nuggets	110	2	1	170	1.0	17.0	6	60	1.00	1	GenMills or Kelloggs
Just_Right_Fruit_&_Nut	140	3	1	170	2.0	20.0	9	95	1.30	1	GenMills or Kelloggs
Kix	110	2	1	260	0.0	21.0	3	40	1.00	1	GenMills or Kelloggs
Life	100	4	2	150	2.0	12.0	6	95	1.00	0	Other
Lucky_Charms	110	2	1	180	0.0	12.0	12	55	1.00	1	GenMills or Kelloggs
Maypo	100	4	1	0	0.0	16.0	3	95	1.00	0	Other
Muesli_Raisins,_Dates,_&_Almonds	150	4	3	95	3.0	16.0	11	170	1.00	0	Other
Muesli_Raisins,_Peaches,_&_Pecans	150	4	3	150	3.0	16.0	11	170	1.00	0	Other
Mueslix_Crispy_Blend	160	3	2	150	3.0	17.0	13	160	1.50	1	GenMills or Kelloggs
Multi-Grain_Cheerios	100	2	1	220	2.0	15.0	6	90	1.00	1	GenMills or Kelloggs
Nut&Honey_Crunch	120	2	1	190	0.0	15.0	9	40	1.00	1	GenMills or Kelloggs
Nutri-Grain_Almond-Raisin	140	3	2	220	3.0	21.0	7	130	1.33	1	GenMills or Kelloggs
Nutri-grain_Wheat	90	3	0	170	3.0	18.0	2	90	1.00	1	GenMills or Kelloggs
Oatmeal_Raisin_Crisp	130	3	2	170	1.5	13.5	10	120	1.25	1	GenMills or Kelloggs
Post_Nat._Raisin_Bran	120	3	1	200	6.0	11.0	14	260	1.33	0	Other
Product_19	100	3	0	320	1.0	20.0	3	45	1.00	1	GenMills or Kelloggs
Puffed_Rice	50	1	0	0	0.0	13.0	0	15	0.50	0	Other
Puffed_Wheat	50	2	0	0	1.0	10.0	0	50	0.50	0	Other
Quaker_Oat_Squares	100	4	1	135	2.0	14.0	6	110	1.00	0	Other
Quaker_Oatmeal	100	5	2	0	2.7	1.0	1	110	1.00	0	Other
Raisin_Bran	120	3	1	210	5.0	14.0	12	240	1.33	1	GenMills or Kelloggs
Raisin_Nut_Bran	100	3	2	140	2.5	10.5	8	140	1.00	1	GenMills or Kelloggs
Raisin_Squares	90	2	0	0	2.0	15.0	6	110	1.00	1	GenMills or Kelloggs
Rice_Chex	110	1	0	240	0.0	23.0	2	30	1.00	0	Other
Rice_Krispies	110	2	0	290	0.0	22.0	3	35	1.00	1	GenMills or Kelloggs
Shredded_Wheat	80	2	0	0	3.0	16.0	0	95	0.83	0	Other
Shredded_Wheat_'n'Bran	90	3	0	0	4.0	19.0	0	140	1.00	0	Other
Shredded_Wheat_spoon_size	90	3	0	0	3.0	20.0	0	120	1.00	0	Other
Smacks	110	2	1	70	1.0	9.0	15	40	1.00	1	GenMills or Kelloggs
Special_K	110	6	0	230	1.0	16.0	3	55	1.00	1	GenMills or Kelloggs
Strawberry_Fruit_Wheats	90	2	0	15	3.0	15.0	5	90	1.00	0	Other
Total_Corn_Flakes	110	2	1	200	0.0	21.0	3	35	1.00	1	GenMills or Kelloggs
Total_Raisin_Bran	140	3	1	190	4.0	15.0	14	230	1.50	1	GenMills or Kelloggs
Total_Whole_Grain	100	3	1	200	3.0	16.0	3	110	1.00	1	GenMills or Kelloggs
Triples	110	2	1	250	0.0	21.0	3	60	1.00	1	GenMills or Kelloggs
Trix	110	1	1	140	0.0	13.0	12	25	1.00	1	GenMills or Kelloggs
Wheat_Chex	100	3	1	230	3.0	17.0	3	115	1.00	0	Other
Wheaties	100	3	1	200	3.0	17.0	3	110	1.00	1	GenMills or Kelloggs
Wheaties_Honey_Gold	110	2	1	200	1.0	16.0	8	60	1.00	1	GenMills or Kelloggs

Multiple Regression Modeling Steps 1. Open the Excel worksheet containing your Team Project Data. 2. As you learned in Modules 2 and 3, you will be using the set of potentially meaningful numerical independent variables and the one selected "two-category" dummy variable in your study to develop a "best" multiple regression model for predicting your numerical response variable Y. Follow the step by step modeling process described in the PowerPoints at the end of Module 3. A. Start with a visual assessment of the possible relationships of your numerical dependent variable Y with each potential predictor variable by developing the scatterplot matrix (use JMP) and paste this into your report. B. Then fit a preliminary multiple regression model using these potential numerical predictor variables and, at most, one categorical dummy variable. C. Then assess collinearity with VIF until you are satisfied that you have a final set of possible predictors that are independent," i.e., not unduly correlated with each other. Note your observations. D. Use stepwise regression approaches to fit a multiple regression model with this set of potentially meaningful numerical independent variables (and, if appropriate, the one selected categorical dummy variable). . (1) Based on the forward modeling criterion determine which independent variables should be included in your regression model. . (2) Based on the backward selection modeling criterion determine which independent variables should be included in your regression model. . (3) Based on the mixed selection modeling criterion determine which independent variables should be included in your regression model. . (4) Based on the Adjusted r2 criterion determine which independent variables should be included in your regression model. E. Comment on the consistency of your findings in Step 2D (1)-(4). F. Paste screenshots of (1), (2), and (3) outputs from Step 2D above into your report. G. Based on Step 2D (along with the principle of parsimony if necessary) select a "best" multiple regression model. Note your finding. H. Using the predictor variables from your selected "best" multiple regression model, rerun the multiple regression model in order to assess its assumptions. You may use Excel or JMP for this step. I. Look at the set of residual plots, cut and paste them into the report, and briefly comment on the appropriateness of your fitted model. (1) If the assumptions are met and the fitted model is appropriate, continue to Step 2). . (2) If the normality assumption is problematic, state this but continue to Step 2) with caution because your sample size is large enough for the central limit theorem to enable the use of classical inferential methods. Note: You do not need to check the assumption of independence in your project. That assumption is met Multiple Regression Modeling Steps 1. Open the Excel worksheet containing your Team Project Data. 2. As you learned in Modules 2 and 3, you will be using the set of potentially meaningful numerical independent variables and the one selected "two-category" dummy variable in your study to develop a "best" multiple regression model for predicting your numerical response variable Y. Follow the step by step modeling process described in the PowerPoints at the end of Module 3. A. Start with a visual assessment of the possible relationships of your numerical dependent variable Y with each potential predictor variable by developing the scatterplot matrix (use JMP) and paste this into your report. B. Then fit a preliminary multiple regression model using these potential numerical predictor variables and, at most, one categorical dummy variable. C. Then assess collinearity with VIF until you are satisfied that you have a final set of possible predictors that are independent," i.e., not unduly correlated with each other. Note your observations. D. Use stepwise regression approaches to fit a multiple regression model with this set of potentially meaningful numerical independent variables (and, if appropriate, the one selected categorical dummy variable). . (1) Based on the forward modeling criterion determine which independent variables should be included in your regression model. . (2) Based on the backward selection modeling criterion determine which independent variables should be included in your regression model. . (3) Based on the mixed selection modeling criterion determine which independent variables should be included in your regression model. . (4) Based on the Adjusted r2 criterion determine which independent variables should be included in your regression model. E. Comment on the consistency of your findings in Step 2D (1)-(4). F. Paste screenshots of (1), (2), and (3) outputs from Step 2D above into your report. G. Based on Step 2D (along with the principle of parsimony if necessary) select a "best" multiple regression model. Note your finding. H. Using the predictor variables from your selected "best" multiple regression model, rerun the multiple regression model in order to assess its assumptions. You may use Excel or JMP for this step. I. Look at the set of residual plots, cut and paste them into the report, and briefly comment on the appropriateness of your fitted model. (1) If the assumptions are met and the fitted model is appropriate, continue to Step 2). . (2) If the normality assumption is problematic, state this but continue to Step 2) with caution because your sample size is large enough for the central limit theorem to enable the use of classical inferential methods. Note: You do not need to check the assumption of independence in your project. That assumption is met