Question: 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)
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
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